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23 September 2022

Customer risk assessment: what you need to know.

Insights Customer risk assessment: What you need to know

A customer risk assessment is a necessity when onboarding new customers . It ensures that high-risk individuals are identified, and appropriate anti-money laundering (AML) measures are put in place.

But what elements should firms consider as part of an AML customer risk assessment? And how do they determine what to prioritize? 

What is a customer risk assessment?

In order to understand the money laundering risks each customer poses, a customer risk assessment should consider a number of factors.  These include verifying the identity of a customer , considering how to engage with them – the products and services they access, the type of transactions they carry out, and how often – and the geographical locations to which the customer is linked. 

In addition, firms should ensure they comply with national and international sanctions by screening customer and beneficial owner names against United Nations and other relevant sanctions lists.

Firms will have different levels of risk appetite regarding the customers they are willing to work with. However, it is important that a consistent customer risk assessment methodology is implemented, setting out the criteria for customer risk scoring weighting mechanisms, and the rationale behind these.

The main purpose of the assessment is to identify the risks to which a firm may be exposed, either in the course of a business relationship, or for an occasional transaction. The more complex this interaction is, the more rigorous a customer risk assessment needs to be. 

By being well informed, firms will be better placed to determine the correct level of customer due diligence (CDD). Ongoing reviews should be completed, particularly if a customer starts to act in a manner that deviates from their risk profile. The Financial Action Task Force (FATF) recommends that where firms cannot apply the appropriate level of CDD, they should not enter into the business relationship, or should terminate the business relationship.

What factors should be included in a customer due diligence risk assessment?

There are four main pillars to consider in a customer risk assessment: 

In the US, the Financial Crimes Enforcement Network’s (FinCEN) CDD Final Rule clarifies and strengthens customer due diligence requirements. It requires applicable financial institutions to establish and maintain written policies and procedures that are designed to:

• Identify and verify the identity of customers
• Identify and verify the identity of the beneficial owners of companies opening accounts
• Understand the nature and purpose of customer relationships to develop customer risk profiles
• Conduct ongoing monitoring to identify and report suspicious transactions and, on a risk basis, to maintain and update customer information

Dynamic AML customer risk assessment

Ongoing due diligence of customers is needed to help firms mitigate money laundering risk , but what is suspicious for one customer won’t be for another. 

Some general behaviors that may raise a red flag, or prompt a re-evaluation of a customer risk assessment include: 

• Changing banks a number of times in a short space of time 
• Attempts to disguise the real owner of the business
• Requests for short-cuts or unusual speed in transactions
• Involvement of a third-party funder with no connection to the business 
• A large amount of private funding from an individual running a cash-intensive business
• False or suspicious documents used
• A large amount of cash transactions inconsistent with the profile of the customer
• Business transactions involve countries with a high risk of money laundering and/or funding of terrorism
• Overly complicated ownership structures
• Inconsistent level of business activity

Firms need to more accurately flag suspicious actors and activities . To do so, they need to understand the importance of dynamic risk assessments and have the data and technology to enable this.

Misclassification of low-risk customers as high risk, and inaccurate or insubstantial KYC information gathering, can dilute the effectiveness of AML measures – and a wholly manual and complex process may not be enough to guarantee the results needed.

Firms should consider simplifying the architecture of their risk models and introducing statistical analysis to complement expert judgment. Machine learning algorithms can improve the quality of data and help continuously update customer profiles, while considering behavior and additional factors.

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Originally published 23 September 2022, updated 15 April 2024

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What Is AML Customer Risk Assessment: Its Importance and How to Do It

Last Updated: April 1, 2024 by Tamas Kadar

Understanding and mitigating customer risk is pivotal to sustaining growth and maintaining a competitive edge. Customer risk assessment serves as a critical tool, enabling organizations to decipher the complexities of customer behavior, financial stability, and potential for fraud or default. This process not only safeguards a company’s assets but also fortifies its reputation, ensuring a trust-based relationship with its clientele. 

While customer risk assessment tools are mandatory for financial institutions, it is essential for all businesses. Failing to perform an adequate risk assessment can cost a lot in fines and leave organizations vulnerable to financial criminals. 

Let’s look more closely at what AML customer risk assessment is, how to do it, and what to consider before implementing it.

What Is AML Customer Risk Assessment?

In the realm of anti-money laundering (AML), customer risk assessment is a critical process where financial institutions evaluate the potential risks posed by customers to prevent money laundering and terrorist financing. 

This comprehensive evaluation includes verifying customer identities, checking customer details against various sanctions lists and analyzing transaction patterns, the services they use and their geographical connections. Conducting a customer risk assessment is a vital part of adhering to AML standards, as it enables financial institutions to pinpoint, comprehend, and lessen the potential risks that might emanate from their client base.

The assessment’s goal is to find out whether a customer poses a money laundering threat, is involved in financing terrorism, is a politically exposed person or appears on any criminal or sanctions lists. 

Implementing standardized AML customer risk assessment ensures that financial organizations can identify potential threats effectively, allowing them to decide on the appropriate level of due diligence. Such measures are vital for safeguarding the integrity of financial systems and maintaining compliance with national and international regulatory standards, ultimately ensuring secure and lawful business operations.

What Does Customer Risk Assessment in AML Involve?

Customer risk assessment is a cornerstone of AML compliance, involving several key steps:

• Customer identification and verification: Institutions must verify the identity of their customers using reliable, independent source documents, data, or information. This process, known as Know Your Customer (KYC), is crucial for establishing the customer’s identity and the legitimacy of their activities.
• AML customer risk scoring: This process involves assigning a numerical score to a customer or transaction based on various risk indicators, which helps in determining the level of scrutiny and monitoring required. This score is calculated using an AML risk scoring model, a tool that helps businesses measure how risky their customers are in terms of money laundering.  It looks at things like what customers do for a living, where they live, and how they use their money. The model gives scores to different risk factors, adds them up, and uses this total score to decide how closely to watch a customer’s activities. If the score is high, the bank may need to look more closely at the customer’s transactions. The model is regularly updated to stay effective and to keep up with new laws and emerging risks.
• Enhanced Due Diligence (EDD): For customers classified as higher risk throughout AML customer risk profiling and found to be politically exposed persons (PEPs) or those from high-risk countries, enhanced due diligence measures are applied. This involves a deeper investigation into the customer’s background, source of funds, and the nature of their transactions.
• Ongoing monitoring: Customer risk assessment is not a one-time process. Continuous monitoring of transactions is essential to detect any unusual or suspicious behavior that could indicate money laundering or terrorist financing.
• Sanction screening: Regularly screening customers against national and international sanctions lists ensures that the institution is not inadvertently facilitating illegal activities.
• Transaction review and reporting: Institutions must review transactions to identify patterns consistent with money laundering. Suspicious activities are reported to relevant authorities as per the regulatory requirements.

By rigorously assessing and monitoring customer risks, financial institutions can detect and prevent illicit activities, ensuring compliance with AML regulations and safeguarding the integrity of the financial system.

Let’s break down what counts as a banking high-risk customer, and explore how to identify them.

Find Out Here

The Importance of Assessing Customer Risk

Assessing customer risk is vital across industries, especially in finance, to safeguard against illegal activities and maintain trust. It’s not just about following rules; it’s about being a responsible player in the global financial system. 

Financial institutions use AML customer risk assessment to prevent money laundering and terrorism financing, protecting themselves and their customers. These assessments pinpoint suspicious activities, helping to avert fraud and financial crimes. Moreover, they ensure that resources are focused where they’re most needed, enhancing operational efficiency. 

By understanding the risk each customer poses, institutions can offer tailored services, maintaining compliance and building stronger customer relationships. In a world where financial transactions cross borders with ease, customer risk assessment is the anchor for navigating international regulations and managing global risks. 

Ultimately, it’s about making informed, data-driven decisions to continuously refine AML case management strategies, ensuring the financial sector remains robust and trustworthy.

Main Elements of an AML Customer Risk Assessment

AML customer risk assessment is pivotal for financial organizations, ensuring compliance and mitigating risks associated with financial crimes. The two key components of customer risk assessment are risk identification, i.e. reviewing all available information to verify the customer’s identity and detect potential risk factors, and customer fraud scoring to categorize customers based on how great of a risk they pose to the business. 

Customer Risk Identification

Initially, financial institutions need identification proofing documentation to assess a customer’s risk profile. These are some of the main components of identifying customers and spotting potential risk factors:

• Differentiating between individuals vs. entities: Differentiating between individual consumers and legal entities is vital, as each has distinct risk factors associated with their activities.
• Reviewing customer affiliations and profiles: Understanding a customer’s background, including employment history, social connections, and financial behaviors, is crucial. Unusual financial activities, like a jobless individual making substantial deposits, can indicate potential risk.
• Geographic considerations: The risk level can vary based on a customer’s geographic connections. Special attention is needed for transactions in locations that don’t align with a customer’s residence or workplace. Money mules, who carry substantial amounts of cash, often establish accounts in various places to sidestep the requirement of declaring these cash transactions.
• Reviewing services requested by customers: The nature of services a customer seeks can be indicative of risk. For example, frequent inquiries about cash deposit processes or international transfers might warrant closer scrutiny.

Customer Risk Scoring

After evaluating the risk factors, a risk score is assigned to each customer in order to categorize them into different risk levels:

• Low-risk customers: These are individuals or entities with transparent financial activities and clear sources of income whose past transactions align with their profiles.
• Medium-risk customers: This category includes customers with slightly elevated risk levels, possibly due to connections to regions or industries known for financial discrepancies.
• High-risk customers: Customers requiring in-depth due diligence, possibly due to unclear funding sources or significant political connections, fall under this category.
• Prohibited category: Individuals or organizations with a history of financial crimes are barred from engaging with financial institutions.

Understanding and implementing these elements to your customer risk assessment enables you to manage customer risk effectively, ensuring a stable and secure financial environment.

How Long Does It Take to Assess a Customer?

Navigating the digital landscape, businesses, especially non-financial ones, are faced with a critical dilemma: the need to accelerate user actions, like signing up or making purchases, while also mitigating risks and ensuring security. This delicate balance is pivotal as companies strive to eliminate churn, friction and barriers, enhancing user experience. 

Traditionally, acquiring financial services like bank accounts or insurance required submitting extensive personal data for customer risk assessment, often resulting in prolonged wait times. However, with modern advancements, these processes can now be nearly instantaneous, provided you’ve set up the right system in place.

Utilize SEON’s identification technology and advanced APIs to create an onboarding process that is low-friction and high-compliance.

SEON’s 5 Steps To Customer Risk Assessment

Identifying and mitigating risks is paramount for businesses seeking to safeguard their operations and adhere to regulatory standards. SEON offers a comprehensive solution designed to enhance your AML customer risk assessment processes. 

Initial data gathering

The moment a visitor lands on your site, SEON springs into action and starts gathering vital information. 

• IP analysis:  Examine and analyze the user’s IP address to discern their geographic location, detect any use of Tor or VPNs and identify attempts to mask their connection. 
• Device fingerprinting :  A robust method that unveils the unique combination of software and hardware your visitors use to access your site. By understanding the intricacies of their device configuration, browser specifics, and more, SEON not only recognizes returning users but also detects impersonators.
• Digital footprinting:  An additional layer of verification which involves collecting and analyzing information generated by an individual’s entire online presence, utilizing real-time data and checking for a broad range of social and digital signals.

Analyzing the gathered data

SEON also does email analysis, which can unravel significant insights – from the age of the email account and its domain provider to any previous blacklist instances. Similarly, phone number analysis helps determine the type of line, the accuracy of the geographic match, and the authenticity of the network. 

Combining all the above data points, SEON helps you find correlations and anomalies and turn all the gathered information into meaningful insights that lead to faster and more accurate risk assessment. 

PEPs & Sanction Screening

SEON’s AML API  enables businesses to screen their customers’ names against a broad and regularly refreshed array of relevant watchlists. These lists cover all key compliance areas, encompassing checks for politically exposed persons (PEPs), sanctions, and criminal watchlists.

Monitoring transaction for AML

SEON’s  transaction monitoring  proactively safeguards transactions like transfers and withdrawals by analyzing customer data and behaviors to spot potential money laundering signs. It helps you manage transaction volumes and escalate high-risk cases to your fraud teams for further examination. Combining machine learning and human analysis, you can notice patterns in vast data sets, enabling prompt, informed decisions. Enhanced with proprietary data and a user-friendly interface, this approach streamlines compliance and accelerates response to potential risks.

Evaluating risk

The culmination of the assessment is deciding on the risk level associated with a user. In the past, this decision heavily relied on the acumen and intuition of fraud managers. However, with the right risk assessment tool, the process is significantly refined through the use of sophisticated risk scores. These scores are derived from various rules – some pre-established for specific industries, others custom-made or even AI-recommended.

Ultimately, the power lies in your hands. You decide on the balance between stringent security measures, which might increase false positives and a more lenient approach that could allow some fraud risks. SEON empowers you with the flexibility to tailor your fraud and risk prevention strategy to your business’s unique needs, ensuring you maintain control over how you mitigate risk.

Frequently Asked Questions

Customer risk assessment is crucial not only for banks but for any business involved in online transactions, including fintechs, crypto exchanges, online casinos, loan companies, and traditional financial institutions, as it helps differentiate between profitable customers and those who pose potential risks.

To conduct an AML risk assessment, first, individuals and entities must be differentiated to identify distinct risk factors. Review customer affiliations, financial behaviors, and geographic connections for potential risks. Then, evaluate the nature of the services customers seek, like frequent cash transactions or international transfers. Finally, assign a risk score to categorize customers into low, medium, high, or prohibited risk levels based on their profiles and activities. This process ensures effective risk management and compliance in the financial sector.

If your business is involved in financial transactions or services where there’s a risk of money laundering, you’re required to comply with AML regulations, your customers engage in high-risk transactions, or if you operate in sectors or regions prone to financial crimes, a risk assessment tool is essential to identify, evaluate, and mitigate potential risks effectively.

You might be interested in:

• SEON: Guide to Transaction Monitoring Software | Tools & Tips
• SEON: Best-Rated Fraud Detection Software in 2024
• SEON: How AML Case Management Drives Efficiency in Financial Investigations

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Tamas kadar.

Tamás Kádár is the Chief Executive Officer and co-founder of SEON. His mission to create a fraud-free world began after he founded the CEE’s first crypto exchange in 2017 and found it under constant attack. The solution he built now reduces fraud for 5,000+ companies worldwide, including global leaders such as KLM, Avis, and Patreon. In his spare time, he’s devouring data visualizations and injuring himself while doing basic DIY around his London pad.

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Flushing out the money launderers with better customer risk-rating models

Money laundering is a serious problem for the global economy, with the sums involved variously estimated at between 2 and 5 percent of global GDP. 1 “Money-laundering and globalization,” United Nations Office on Drugs and Crime, unodc.org. Financial institutions are required by regulators to help combat money laundering and have invested billions of dollars to comply. Nevertheless, the penalties these institutions incur for compliance failure continue to rise: in 2017, fines were widely reported as having totaled $321 billion since 2008 and $42 billion in 2016 alone. 2 Gavin Finch, “World’s biggest banks fined $321 billion since financial crisis,” Bloomberg , March 2, 2017, bloomberg.com. This suggests that regulators are determined to crack down but also that criminals are becoming increasingly sophisticated.

Customer risk-rating models are one of three primary tools used by financial institutions to detect money laundering. The models deployed by most institutions today are based on an assessment of risk factors such as the customer’s occupation, salary, and the banking products used. The information is collected when an account is opened, but it is infrequently updated. These inputs, along with the weighting each is given, are used to calculate a risk-rating score. But the scores are notoriously inaccurate, not only failing to detect some high-risk customers, but often misclassifying thousands of low-risk customers as high risk. This forces institutions to review vast numbers of cases unnecessarily, which in turn drives up their costs, annoys many low-risk customers because of the extra scrutiny, and dilutes the effectiveness of anti–money laundering (AML) efforts as resources are concentrated in the wrong place.

In the past, financial institutions have hesitated to do things differently, uncertain how regulators might respond . Yet regulators around the world are now encouraging innovative approaches to combat money laundering and leading banks are responding by testing prototype versions of new processes and practices. 3 The US Treasury and banking agencies have together encouraged innovative anti–money laundering (AML) practices; see “Agencies issue a joint statement on innovative industry approaches,” US Office of the Comptroller of the Currency, December 3, 2018, occ.gov. In China, the Hong Kong Monetary Authority has backed the wider use of regulatory technology, and in the United Kingdom, the financial regulator has established a fintech sandbox to test AML innovations. Some of those leaders have adopted the approach to customer risk rating described in this article, which integrates aspects of two other important AML tools: transaction monitoring and customer screening. The approach identifies high-risk customers far more effectively than the method used by most financial institutions today, in some cases reducing the number of incorrectly labeled high-risk customers by between 25 and 50 percent. It also uses AML resources far more efficiently.

Best practice in customer risk rating

To adopt the new generation of customer risk-rating models, financial institutions are applying five best practices: they simplify the architecture of their models, improve the quality of their data, introduce statistical analysis to complement expert judgment, continuously update customer profiles while also considering customer behavior, and deploy machine learning and network science tools.

1. Simplify the model architecture

Most AML models are overly complex. The factors used to measure customer risk have evolved and multiplied in response to regulatory requirements and perceptions of customer risk but still are not comprehensive. Models often contain risk factors that fail to distinguish between high- and low-risk countries, for example. In addition, methodologies for assessing risk vary by line of business and model. Different risk factors might be used for different customer segments, and even when the same factor is used it is often in name only. Different lines of business might use different occupational risk-rating scales, for instance. All this impairs the accuracy of risk scores and raises the cost of maintaining the models. Furthermore, a web of legacy and overlapping factors can make it difficult to ensure that important rules are effectively implemented. A person exposed to political risk might slip through screening processes if different business units use different checklists, for example.

Under the new approach, leading institutions examine their AML programs holistically, first aligning all models to a consistent set of risk factors, then determining the specific inputs that are relevant for each line of business (Exhibit 1). The approach not only identifies risk more effectively but does so more efficiently, as different businesses can share the investments needed to develop tools, approaches, standards, and data pipelines.

2. Improve data quality

Poor data quality is the single biggest contributor to the poor performance of customer risk-rating models. Incorrect know-your-customer (KYC) information, missing information on company suppliers, and erroneous business descriptions impair the effectiveness of screening tools and needlessly raise the workload of investigation teams. In many institutions, over half the cases reviewed have been labeled high risk simply due to poor data quality.

The problem can be a hard one to solve as the source of poor data is often unclear. Any one of the systems that data passes through, including the process for collecting data, could account for identifying occupations incorrectly, for example. However, machine-learning algorithms  can search exhaustively through subsegments of the data to identify where quality issues are concentrated, helping investigators identify and resolve them. Sometimes, natural-language processing (NLP) can help. One bank discovered that a great many cases were flagged as high risk and had to be reviewed because customers described themselves as a doctor or MD, when the system only recognized “physician” as an occupation. NLP algorithms were used to conduct semantic analysis and quickly fix the problem, helping to reduce the enhanced due-diligence backlog by more than 10 percent. In the longer term, however, better-quality data is the solution.

3. Complement expert judgment with statistical analysis

Financial institutions have traditionally relied on experts, as well as regulatory guidance, to identify the inputs used in risk-rating-score models and decide how to weight them. But different inputs from different experts contribute to unnecessary complexity and many bespoke rules. Moreover, because risk scores depend in large measure on the experts’ professional experience, checking their relevance or accuracy can be difficult. Statistically calibrated models tend to be simpler. And, importantly, they are more accurate, generating significantly fewer false-positive high-risk cases.

Building a statistically calibrated model might seem a difficult task given the limited amount of data available concerning actual money-laundering cases. In the United States, suspicious cases are passed to government authorities that will not confirm whether the customer has laundered money. But high-risk cases can be used to train a model instead. A file review by investigators can help label an appropriate number of cases—perhaps 1,000—as high or low risk based on their own risk assessment. This data set can then be used to calibrate the parameters in a model by using statistical techniques such as regression. It is critical that the sample reviewed by investigators contains enough high-risk cases and that the rating is peer-reviewed to mitigate any bias.

Experts still play an important role in model development, therefore. They are best qualified to identify the risk factors that a model requires as a starting point. And they can spot spurious inputs that might result from statistical analysis alone. However, statistical algorithms specify optimal weightings for each risk factor, provide a fact base for removing inputs that are not informative, and simplify the model by, for example, removing correlated model inputs.

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4. continuously update customer profiles while also considering behavior.

Most customer risk-rating models today take a static view of a customer’s profile—his or her current residence or occupation, for example. However, the information in a profile can become quickly outdated: most banks rely on customers to update their own information, which they do infrequently at best. A more effective risk-rating model updates customer information continuously, flagging a change of address to a high-risk country, for example. A further issue with profiles in general is that they are of limited value unless institutions are considering a person’s behavior as well. We have found that simply knowing a customer’s occupation or the banking products they use, for example, does not necessarily add predictive value to a model. More telling is whether the customer’s transaction behavior is in line with what would be expected given a stated occupation, or how the customer uses a product.

Take checking accounts. These are regarded as a risk factor, as they are used for cash deposits. But most banking customers have a checking account. So, while product risk is an important factor to consider, so too are behavioral variables. Evidence shows that customers with deeper banking relationships tend to be lower risk, which means customers with a checking account as well as other products are less likely to be high risk. The number of in-person visits to a bank might also help determine more accurately whether a customer with a checking account posed a high risk, as would his or her transaction behavior—the number and value of cash transactions and any cross-border activity. Connecting the insights from transaction-monitoring models with customer risk-rating models can significantly improve the effectiveness of the latter.

While statistically calibrated risk-rating models perform better than manually calibrated ones, machine learning and network science can further improve performance.

5. Deploy machine learning and network science tools

The list of possible model inputs is long, and many on the list are highly correlated and correspond to risk in varying degrees. Machine-learning tools can analyze all this. Feature-selection algorithms that are assumption-free can review thousands of potential model inputs to help identify the most relevant features, while variable clustering can remove redundant model inputs. Predictive algorithms (decision trees and adaptive boosting, for example) can help reveal the most predictive risk factors and combined indicators of high-risk customers—perhaps those with just one product, who do not pay bills but who transfer round-figure dollar sums internationally. In addition, machine-learning approaches can build competitive benchmark models to test model accuracy, and, as mentioned above, they can help fix data-quality issues.

Network science is also emerging as a powerful tool. Here, internal and external data are combined to reveal networks that, when aligned to known high-risk typologies, can be used as model inputs. For example, a bank’s usual AML-monitoring process would not pick up connections between four or five accounts steadily accruing small, irregular deposits that are then wired to a merchant account for the purchase of an asset—a boat perhaps. The individual activity does not raise alarm bells. Different customers could simply be purchasing boats from the same merchant. Add in more data however—GPS coordinates of commonly used ATMs for instance—and the transactions start to look suspicious because of the connections between the accounts (Exhibit 2). This type of analysis could discover new, important inputs for risk-rating models. In this instance, it might be a network risk score that measures the risk of transaction structuring—that is, the regular transfer of small amounts intended to avoid transaction-monitoring thresholds.

Although such approaches can be powerful, it is important that models remain transparent. Investigators need to understand the reasoning behind a model’s decisions and ensure it is not biased against certain groups of customers. Many institutions are experimenting with machine-based approaches combined with transparency techniques such as LIME or Shapley values that explain why the model classifies customers as high risk.

Moving ahead

Some banks have already introduced many of the five best practices. Others have further to go. We see three horizons in the maturity of customer risk-rating models and, hence, their effectiveness and efficiency (Exhibit 3).

The journey toward sophisticated risk-rating models

Getting started: how to move from horizon one to two.

Assemble a team of experts from compliance, business, data science, and technology and data.

Establish a common hierarchy of risk factors informed by regulatory guidance, experts, and risks identified in the past.

Start in bite-size chunks: pick an important model to recalibrate that the team can use to develop a repeatable process.

Assemble a file-review team to label a sample of cases as high or low risk based on their own risk assessment. Bias the sample to ensure that high-risk cases are present in sufficient numbers to train a model.

Use a fast-paced and iterative approach to cycle through model inputs quickly and identify those that align best with the overarching risk factors. Be sure there are several inputs for each factor.

Engage model risk-management and technology teams early and set up checkpoints to avoid any surprises.

Becoming an industry leader: How to move from horizon two to three

Begin to build capabilities in machine learning, network science, and natural-language processing by hiring new experts or identifying potential internal transfers.

Construct a network view of all customers, initially building links based on internal data and then creating inferred links. This will become a core data asset.

Set up a working group to identify technology changes that can be deployed on existing technology (classical machine learning may be easier to deploy than deep learning, for example) and those that will require longer-term planning.

Design and implement customer journeys in a way that facilitates quick updates to customer data. An in-person visit to a branch should always prompt a profile update, for example. Set up an innovation team to continuously monitor model performance and identify emerging high-risk typologies to incorporate into model calibration.

Most banks are currently on horizon one, using models that are manually calibrated and give a periodic snapshot of the customer’s profile. On horizon two, statistical models use customer information that is regularly updated to rate customer risk more accurately. Horizon three is more sophisticated still. To complement information from customers’ profiles, institutions use network analytics to construct a behavioral view of how money moves around their customers’ accounts. Customer risk scores are computed via machine-learning approaches utilizing transparency techniques to explain the scores and accelerate investigations. And customer data are updated continuously while external data, such as property records, are used to flag potential data-quality issues and prioritize remediation.

Financial institutions can take practical steps to start their journey toward horizon three, a process that may take anywhere from 12 to 36 months to complete (see sidebar, “The journey toward sophisticated risk-rating models”).

As the modus operandi for money launderers becomes more sophisticated and their crimes more costly, financial institutions must fight back with innovative countermeasures. Among the most effective weapons available are advanced risk-rating models. These more accurately flag suspicious actors and activities, applying machine learning and statistical analysis to better-quality data and dynamic profiles of customers and their behavior. Such models can dramatically reduce false positives and enable the concentration of resources where they will have the greatest AML effect. Financial institutions undertaking to develop these models to maturity will need to devote the time and resources needed for an effort of one to three years, depending on each institution’s starting point. However, this is a journey that most institutions and their employees will be keen to embark upon, given that it will make it harder for criminals to launder money.

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Daniel Mikkelsen is a senior partner in McKinsey’s London office, Azra Pravdic is an associate partner in the Brussels office, and Bryan Richardson is a senior expert in the Vancouver office.

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The Essential Guide to Customer Risk Assessment

When you bring in new customers, it's essential to do a customer risk assessment. This helps pinpoint people who might pose a higher risk, and it allows us to take the right steps to prevent money laundering through appropriate measures. In today's fast-changing business environment, it's crucial to understand and manage these risks to ensure ongoing success. This guide delves into the broader concept of risk assessment, emphasizing its significance and the specific factors that impact customer risk.

What Is a Risk Assessment?

Customer risk assessment in the context of Anti-Money Laundering (AML) refers to the process of evaluating the level of risk associated with a particular customer or client within the financial system. AML is a set of regulations and practices designed to prevent the illegal generation of income through activities such as money laundering and terrorism financing. Customer risk assessment is a crucial component of AML compliance and is undertaken by financial institutions to identify, understand, and mitigate potential risks associated with their customers.

Here are key aspects to consider when discussing customer risk assessment in terms of AML:

1. Customer Due Diligence (CDD):

Financial institutions are required to conduct thorough due diligence on their customers to assess the risk they pose. This involves collecting and verifying information about a customer's identity, purpose of the account, nature of the business relationship, and the source of funds.

2. Risk Factors:

Various risk factors contribute to the overall risk assessment of a customer. These factors include the customer's geographical location, type of business, transaction volume, and the complexity of the financial transactions. Customers engaging in high-risk activities or residing in high-risk jurisdictions are subject to more scrutiny.

3. Enhanced Due Diligence (EDD):

In cases where the risk is deemed higher, financial institutions may need to apply enhanced due diligence measures. This could involve obtaining additional information about the customer, monitoring transactions more closely, and assessing the potential exposure to money laundering or other illicit activities.

4. Transaction Monitoring:

Continuous monitoring of customer transactions is essential to detect unusual or suspicious activities. Automated systems are often employed to analyze transaction patterns and identify deviations from the norm, triggering further investigation.

5. Politically Exposed Persons (PEPs):

Individuals holding prominent public positions, known as politically exposed persons, are considered higher risk due to the potential for corruption and misuse of their positions. Financial institutions are required to subject PEPs to enhanced scrutiny and monitoring.

6. Customer Risk Profiles:

Financial institutions categorize customers into different risk profiles based on their assessment. These profiles help determine the level of monitoring and due diligence required. Low-risk customers may undergo standard procedures, while high-risk customers may require more rigorous scrutiny.

7. Documentation and Record-Keeping:

AML regulations mandate the maintenance of comprehensive records of customer due diligence, risk assessments, and monitoring activities. Proper documentation is crucial for regulatory compliance and serves as evidence of the institution's efforts to mitigate AML risks.

8. Ongoing Monitoring:

Customer risk analysis is not a one-time process; it is an ongoing activity. Financial institutions must continuously monitor their customers, regularly update customer information, and reassess risk levels to ensure the effectiveness of their AML compliance programs.

Importance of Assessing Customer Risk

Assessing customer risk is of paramount importance in various industries, particularly in the financial sector, and it serves several crucial purposes. Here's an expansion on the importance of assessing customer risk:

1. Compliance with Regulatory Requirements:

Anti-Money Laundering (AML) regulations require financial institutions to implement robust customer risk assessment processes. Failure to comply with these regulations can result in severe penalties, legal consequences, and reputational damage. By assessing customer risk, institutions demonstrate their commitment to complying with regulatory standards.

2. Prevention of Money Laundering and Terrorism Financing:

Customer risk assessment is a key component in detecting and preventing money laundering and terrorism financing. By evaluating the risk associated with each customer, financial institutions can identify unusual or suspicious transactions that may indicate illicit activities.

3. Protection of Financial Institutions' Reputation:

Inadequate risk assessment can expose financial institutions to reputational risks. If a customer engages in illicit activities, it can tarnish the institution's reputation and erode the trust of clients, investors, and regulatory bodies. Effective risk assessment measures help protect the integrity and standing of the financial institution.

4. Enhanced Operational Efficiency:

Consumer risk management allows financial institutions to allocate resources efficiently. By focusing more on higher-risk customers, institutions can optimize their monitoring efforts and investigative resources, ensuring that resources are deployed where they are most needed.

5. Prevention of Fraud and Financial Crimes:

Assessing customer risk aids in the early identification of potential fraudulent activities. This includes not only money laundering but also other financial crimes such as identity theft, credit card fraud, and cybercrime. Timely detection helps prevent financial losses and protects the interests of both the institution and its customers.

6. Strengthening National Security:

Customer risk assessment plays a crucial role in preventing the financing of terrorism. By identifying and monitoring customers who may be involved in or funding terrorist activities, financial institutions contribute to national and international security efforts.

7. Customer Relationship Management:

Understanding customer risk allows financial institutions to tailor their services based on the risk profile of each customer. This ensures that higher-risk customers receive the appropriate level of scrutiny and that services are provided in a manner that aligns with regulatory requirements.

8. Global Risk Management:

In an interconnected global financial system, assessing customer risk is essential for managing cross-border transactions. It helps financial institutions navigate the complexities of international regulations, cultural differences, and diverse risk environments.

9. Data-Driven Decision-Making:

Customer risk assessments provide valuable data that can inform strategic decision-making within financial institutions. This data-driven approach allows for the continuous improvement of risk management strategies and the adaptation of policies to evolving threats.

10. Prevention of Regulatory Sanctions:

Regular customer risk assessments contribute to ongoing compliance with changing regulatory requirements. This proactive approach helps financial institutions avoid regulatory penalties and sanctions, ensuring a smoother operational environment.

Customer Risk Factors

Customer risk factors encompass various elements that financial institutions consider when evaluating the level of risk associated with a particular customer. These factors help in determining the likelihood of a customer being involved in money laundering, fraud, or other illicit activities.

1. Geographic Location:

Customers residing in jurisdictions known for high levels of corruption, weak regulatory frameworks, or a history of financial crimes may pose a higher risk. Financial institutions often assess the risk associated with a customer based on their geographic location.

2. Business Type and Industry:

Certain industries are inherently more susceptible to money laundering and other financial crimes. Businesses involved in cash-intensive activities, high-value transactions, or those lacking transparent financial structures may be considered higher risk.

3. Transaction Patterns:

Unusual or complex transaction patterns, particularly those inconsistent with a customer's known business activities, may raise red flags. Rapid and significant changes in transaction volumes, frequency, or size can indicate potential risks.

4. Source of Wealth and Income:

Understanding the legitimate source of a customer's wealth is crucial. If the source of income or wealth is unclear, unverifiable, or inconsistent with the customer's profile, it can be indicative of higher risk. Financial institutions often scrutinize large, unexpected inflows of funds.

5. Customer Behavior:

Unusual behavior, such as frequent changes in account information, reluctance to provide necessary documentation, or attempts to avoid regulatory scrutiny, may signal potential risk. Behavioral analysis is a crucial component of customer risk assessment.

Customer Risk Levels

Customer risk levels refer to the categorization of customers based on the assessment of factors that may expose them to potential financial crimes, such as money laundering, fraud, or terrorism financing. The goal is to stratify customers according to their risk profiles, allowing financial institutions to allocate resources and implement appropriate risk mitigation measures.

1. Low-Risk Customers:

Characteristics : Customers with transparent and verifiable sources of income, a clear business purpose, and a history of compliance with regulatory requirements are typically considered low risk.

Risk Mitigation : Low-risk customers may undergo standard due diligence procedures. Transaction monitoring is conducted with a standard level of scrutiny, and routine reviews of customer profiles are performed periodically.

2. Medium-Risk Customers

Characteristics : Customers with moderate risk may have some factors that warrant closer attention, such as involvement in industries prone to money laundering or transactions with certain risk indicators.

Risk Mitigation : Enhanced Due Diligence (EDD) measures are applied to medium-risk customers. This may involve more in-depth verification of identity, additional documentation requirements, and increased transaction monitoring.

3. High-Risk Customers:

Characteristics : High-risk customers exhibit multiple risk factors, such as complex ownership structures, involvement in high-risk industries, or transactions that deviate significantly from established patterns.

Risk Mitigation : High-risk customers are subject to rigorous scrutiny and monitoring. Enhanced Due Diligence (EDD) is applied extensively, involving thorough background checks, source of funds verification, and continuous transaction monitoring. These customers may require senior management approval for onboarding or continued engagement.

4. Politically Exposed Persons (PEPs):

Characteristics: PEPs, due to their public positions, are considered inherently high risk. This includes government officials, diplomats, and individuals with close associations to such positions.

Risk Mitigation: PEPs are subject to the highest level of scrutiny. Enhanced Due Diligence measures are mandatory, and transactions are monitored with extreme diligence. Regular reviews and reporting obligations are intensified for PEPs.

5. Emerging Risk or Changing Risk Levels:

Characteristics : Customers may experience changes in their risk profile due to evolving business activities, regulatory changes, or shifts in ownership.

Risk Mitigation : Financial institutions must proactively monitor and reassess customer risk levels. If there are changes in a customer's circumstances, appropriate measures are taken, such as updating due diligence information, conducting additional investigations, and adjusting risk mitigation strategies accordingly.

6. Automated Risk Scoring:

Characteristics : Some financial institutions employ automated risk-scoring systems that use algorithms to assess various risk factors and assign a numerical score to customers.

Risk Mitigation : Based on the automated risk score, customers are categorized into risk levels. Higher scores may trigger additional scrutiny, while lower scores may result in standard due diligence procedures.

7. Dynamic Risk Assessment:

Characteristics : Risk levels are not static and can change over time based on customer behavior, market conditions, or regulatory developments.

Risk Mitigation : Regular and ongoing monitoring allows for dynamic risk assessment. Financial institutions continuously update customer profiles, reassess risk levels, and adjust risk mitigation measures as needed.

Dynamic AML Customer Risk Assessment

Dynamic AML customer risk assessment refers to an approach where the evaluation of a customer's risk is not a one-time activity but an ongoing and adaptable process. It involves continuously monitoring and reassessing the risk associated with customers based on evolving factors, such as changes in customer behavior, market conditions, regulatory developments, and other relevant circumstances. Here's an expansion on the concept of dynamic AML customer risk assessment:

1. Continuous Monitoring:

Dynamic AML customer risk assessment involves the continuous monitoring of customer transactions, behavior, and other relevant activities. Automated systems and analytics are often employed to detect patterns and anomalies in real-time or near-real-time.

2. Real-Time Data Analysis:

The use of advanced data analytics allows financial institutions to analyze vast amounts of data in real-time. This includes transaction data, customer information, and external data sources to identify unusual patterns or behaviors that may indicate increased risk.

3. Behavioral Analysis:

Dynamic risk assessment places a strong emphasis on behavioral analysis. By establishing a baseline of normal customer behavior, financial institutions can quickly identify deviations that may signal potential risks. Unusual transaction patterns, changes in account activity, or unexpected shifts in behavior trigger further scrutiny.

4. Trigger Events:

Trigger events, predefined indicators or thresholds, are set to automatically prompt a reassessment of customer risk. These triggers can be based on transaction amounts, frequency, geographic locations, or other relevant factors. For example, a sudden increase in transaction volume may trigger a reevaluation.

5. Event-Driven Updates:

Changes in a customer's profile or external events, such as regulatory updates or sanctions, trigger automatic updates to the customer's risk assessment. This ensures that risk levels are promptly adjusted in response to changes in the customer's circumstances or the external environment.

Tookitaki's Dynamic Risk Scoring Solution

Tookitaki's Dynamic Risk Scoring solution is a game-changer in the world of risk management for financial institutions. By adopting a data-driven approach, this solution allows for continuous improvement and adaptation of risk management strategies in response to evolving threats. One of the key benefits of this solution is the prevention of regulatory sanctions. By conducting regular customer risk assessments, financial institutions can ensure ongoing compliance with changing regulatory requirements.

This proactive approach helps them avoid penalties and sanctions, creating a smoother operational environment. The solution takes into account various customer risk factors, such as geographic location, business type and industry, transaction patterns, source of wealth and income, and customer behavior. By analyzing these factors, financial institutions can categorize customers into different risk levels, from low-risk to high-risk customers and politically exposed persons (PEPs). This allows them to allocate resources and implement appropriate risk mitigation measures based on each customer's risk profile.

Additionally, the solution incorporates automated risk scoring systems and dynamic risk assessment to ensure that risk levels are continuously monitored and adjusted as needed. With its focus on continuous monitoring, real-time data analysis, behavioral analysis, trigger events, and event-driven updates, Tookitaki's Dynamic Risk Scoring solution provides financial institutions with the tools they need to effectively manage customer risk and stay compliant in an ever-changing regulatory landscape.

Customer risk assessment is a cornerstone of effective risk management for businesses. By understanding and evaluating the potential risks associated with individual customers, businesses can protect their financial interests, comply with regulations, and foster a secure and trustworthy environment. Embracing a dynamic approach to customer risk assessment ensures that businesses stay ahead of evolving risks, contributing to long-term success.

1. What is a customer risk assessment?

A customer risk assessment is the process of evaluating and analyzing the potential risks associated with engaging with a particular customer.

2. How to identify the need for customer risk assessment?

The need for customer risk assessment arises from the desire to safeguard financial interests, comply with regulatory requirements, and create a secure business environment.

3. How can technology assist in customer risk assessment?

Technological tools, such as data analytics, artificial intelligence, and machine learning, play a crucial role in customer risk assessment.

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Five Steps to Managing AML Customer Risk

Managing AML risk at the customer level is a fundamental component of an effective Anti-Money Laundering process. Here are five steps to ensure you remain on top of your AML obligations.

Managing AML risk at the customer level is a fundamental component of an effective Anti-Money Laundering strategy. Here are five steps to ensure you remain on top of your AML obligations.

A risk-based approach (RBA) to anti-money laundering is proven to be the most effective and efficient methodology and is the cornerstone of AML guidelines published by international organisations such as the FATF, and local organisations such as CCAB. In terms of customer due diligence, the RBA means that not all clients should be assigned the same level of checks and scrutiny. Clients should be divided into risk categories such that more attention can then be given to high-risk clients. Whether you work for a financial institution or a DNFBP in sectors such as accountancy, legal, tax advisory or real estate, this article explores five steps that can be followed to ensure that customer risk is being handled efficiently and effectively.

Step 1: Define the Customer Risk Assessment (CRA) Methodology

The starting point is defining the Customer Risk Assessment methodology. This includes identifying the factors that go into a risk assessment, the scores allocated to each risk factor and how the various risk scores are rolled up into an overall customer risk score.

Risk factors typically include attributes related to the client, jurisdiction, service/product, transaction, and delivery channel. In case of corporate clients, you also need to decide how related parties effect the risk of the client. For example, the residence of the UBO is typically included as a factor within the jurisdiction risk associated with a company. But what about directors or minority shareholders? This depends on your risk policy.

For every risk factor you also need to define what values are to be considered high, medium, or low risk. This is usually done via a scoring system of 1-10 or 1-100 where each option is assigned a score based on how risky it is.

Finally, a formula that defines how the individual risk factors and their scores are used to generate the overall risk score needs to be defined. This could be a weighted average across all factors, or could include rules where certain risk factors automatically trigger an overall high-risk result at the customer level (e.g., if the client is a PEP, the customer’s risk should automatically be considered to be high).

The CRA methodology defines the blueprint that is used to eventually stratify clients by risk. If you are using a manual system, you will probably define this methodology as a set of rules and formulas within a spreadsheet which you would then use as a template to generate a CRA for every client. A manual or spreadsheet-based system is not always a good idea . A better approach would be to have specialised AML software which allows you to configure all the rules within your CRA methodology. Such software needs to be easy to set up but also powerful enough to support complex rules and flexible enough to adapt to your methodology.

Step 2: Stratify Clients by Risk

The next step is to stratify all clients into different risk classifications. The inputs to this process are the CRA methodology explained in the previous step and the client information collected.

The good news about this step is that using the right technology, this process can be simplified drastically. To begin with, you should have a centralised customer database containing all the information required to be able to generate the risk score. This information should include structure charts to be able to identify shareholding and ultimate beneficial owners.

Once all the data is collected, specialised AML software can automate the process of applying the CRA methodology across the entire client base and generate a risk classification for each client .

Step 3: Enforce checks based on risk

The biggest advantage of categorising clients by risk is that you can now give more focus to those with a higher risk classification. This is where concepts such Enhanced Due Diligence (EDD) come into play. High-risk clients may trigger the need to collect additional documentation, collect more detailed information or implement more frequent reviews. There are a variety of ways in which this can be implemented but let’s limit ourselves to just one example.

If you have been commissioned to assist in the setting up of a company, you need to understand what the proposed business activities will be and whether the individuals have the right background that enables them to run such a business. During onboarding you may have asked about their professional experience and left it at that. However, if the CRA results in this client being classified as a high risk, you may need to go into more detail. For example, you may want to ask for a detailed CV; references from past business associates or employers; or proof of previous employment.

Once again, technology can simplify this process drastically by immediately triggering a set of warnings and tasks to be carried out as soon as a client is stratified as a high-risk client.

Step 4: Reviewing the bigger picture

Once you have your risk policy in place, your clients classified, and your EDD processes applied, you can generate meaningful reports that can highlight interesting trends across your client base. For example, you could identify how many of your clients are high-risk, how many cash-intensive business are in your portfolio or the breakdown of your clients and their UBOs by jurisdiction.

This information can help drive decisions to reduce your AML exposure; or highlight areas that might need further attention. One key metric, for example, is the ratio between high risk and low risk clients. A very low ratio (or a scenario where there are no clients identified as high risk at all), may indicate the need for a tweak in the risk policies. This is because having a set of clients that are deemed to pose a higher risk is a good thing – it allows you to focus more effort on this subset of clients, in line with the concept of a risk-based approach. On the other hand, a high ratio of high-risk clients may be outside your organisation’s level of comfort and may be exposing your organisation to a higher level of risk. In this case, you may want to embark on a de-risking exercise to bring the client base within acceptable parameters in line with your risk appetite.

Having a process, or applying the right technology, to generate such statistics allows you to improve your processes and become more efficient. Moreover, in some jurisdictions, such reports can also be useful in answering queries or questionnaires issued by authorities, supervisors or oversight bodies.

Step 5. Ongoing Monitoring

Managing the AML risk that clients pose to your organisation is not a one-time activity. Processes and customer risk scores need to be maintained via a process of ongoing monitoring. There are several scenarios that you need to consider throughout your business relationship with a client. For example:

• You need to keep tracking changes to client information and update your risk classification accordingly. For example, a share transfer from a UBO in a low-risk jurisdiction to one in a high-risk jurisdiction may move the client into a different risk category and unlock new requirements in line with your Enhanced Due Diligence policies.
• External changes could also trigger a client moving from one risk category to another. For example, the inclusion of a country in the FATF list of High-Risk and Other Monitored Jurisdictions , may mean that clients with UBOs from this jurisdiction are now considered to carry a higher risk.
• You need to make sure that all documentation remains up to date , including collecting identification documents when the copies you have on file have expired.
• External sources of information about your client also need to be monitored to determine whether there are sanctions issued against the client; if any adverse media about the client has been published; or if some client has become politically exposed .
• There may also be instances that trigger an internal policy or methodology change . For example, once your processes mature you may want to start considering more variables within the CRA methodology or you may want to treat a scenario differently. Once a change to the CRA methodology is implemented, clients may need to be reassessed and the outcome may trigger EDD requirements for a new set of clients.

Ongoing monitoring is one of the more costly elements of managing client risk because it involves a lot of repetition however it is a prime candidate for automation. Specialised AML software can go a long way in simplifying these tasks. For example, changes to client data, internal risk policies or external factors such as jurisdiction reputation, can automatically identify the clients that require a different risk classification. Another use of technology is alerts. These can be set up to automatically provide advance warning in cases where documents are about to expire. Finally, a fundamental piece of technology is the ability to set up ongoing daily monitors of your clients against sanction lists, PEP and adverse media databases.

These five steps capture a comprehensive process to managing customer risk, and include tasks related to planning the risk methodology, classifying clients, applying EDD measures as applicable, reporting, tweaking the risk methodology parameters, and keep on top of any changes via ongoing monitoring. While some steps may seem complex and daunting, leveraging specialised AML software such as InScope-AML can reduce the effort required at all steps, making it easier to stay on top of your compliance obligations.

For more information about InScope-AML, please download our eBook here .

If you are interested in scheduling a one-to-one discussion with one of our consultants,  you can book a call here.

O r schedule a demo for an in-depth look into InScope-AML.

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A Comprehensive Framework for AML Risk Assessment

Table of contents

Identifying and mitigating aml risks, the importance of aml risk assessment, challenges associated with an aml risk management program, developing a framework to implement an effective aml risk assessment program, optimize your approach to risk assessments with finscan, about the author.

In today’s data-driven world, financial institutions have unprecedented access to vast amounts of information about their customers and transaction activities. However, effectively using this data landscape to assess financial crime risk poses a significant challenge.

Many organizations grapple with poor data quality and struggle to build a risk scoring model that accurately evaluates the risk of financial crime within each business relationship. While the promise of data science and artificial intelligence (AI) hold immense potential for the future, financial institutions still rely on rules-based models that aggregate data from multiple sources to derive a risk rating. These models require regular fine tuning to gauge their efficacy in evaluating financial crime risk.

In this article, we explore the essential relationship between data quality and risk scoring models, introducing a framework that bolsters the accuracy of Anti-Money Laundering (AML) risk evaluation. Leveraging 20+ years of experience in AML consulting and technology, we present actionable insights, industry best practices, and advanced methodologies to help organizations unlock the full potential of their risk assessment.

AML risk assessment is a thorough, systematic process designed to detect, evaluate, and mitigate the risks of money laundering and terrorist financing linked to a business relationship. This involves identifying and examining crucial risk factors to understand the AML risk exposure of financial institutions. This allows them to pinpoint customers with a higher money laundering risk and implement appropriate, risk-based strategies for preventing money laundering. Assessing customer risk is a fundamental component of a financial institution’s overall AML risk evaluation.

By implementing an effective AML risk assessment framework, financial institutions can proactively identify and assess the likelihood and potential impact of financial crimes within their operations. This enables them to allocate resources, implement proper controls, and prioritize their efforts to effectively manage and mitigate the risks related to money laundering and terrorist financing.

Central to the customer AML risk assessment is a risk model that calculates a risk score, or a risk rating, such as high, medium, or low. This risk score or rating provides the AML Officer and the business line with a clear image of the risks the customer relationship and activities pose to the institution.

An AML risk assessment enables organizations to adopt a risk-based approach to combat financial crime and meet regulatory expectations. Through thorough assessments, organizations demonstrate their commitment to compliance while efficiently allocating resources and applying enhanced scrutiny to high-risk customers. This strategic approach not only ensures regulatory compliance but also strengthens the organization’s ability to detect and prevent financial crime, safeguarding the integrity of the financial system.

Establishing and supporting an effective AML risk management program comes with various challenges that can affect its success. These challenges need careful consideration and proactive measures to ensure compliance and better manage financial and reputational risks. Key challenges associated with effective AML risk management programs include:

• Data quality : AML risk assessment is dependent on accurate and comprehensive customer and transaction data. Inadequate, inconsistent, or inaccurate data can impede the effectiveness of risk assessments.
• Infrequent data updates : Regular updates of customer information, such as occupation, industry, and address and externally sourced information such as adverse media are vital to supporting accurate risk assessments and avoiding reliance on obsolete data.
• Data integration challenges : Integrating data from various internal and external sources, such as customer databases and transaction records, can be challenging due to differences in formats, systems, and data quality issues.
• Risk scoring models : Risk scoring models must be robust, well-designed, fully documented, and regularly validated and refined to ensure full and effective risk assessments.
• Real-time risk detection : The ability to refresh risk profiles in real time based on continuous monitoring activities, including analyzing transactions, screening against watchlists, and assessing changes to customer attributes, is pivotal for dynamic AML risk assessment.
• Resource limitations : Comprehensive risk assessments demand competent personnel, a robust technological infrastructure, and access to reliable data sources. These requirements can be challenging to resource constrained organizations.

To set up an effective AML risk assessment program, financial institutions should adhere to a structured framework. This framework can enhance an institution’s risk assessment capabilities and help align it with regulatory requirements. It is important to remember that AML risk assessment is an iterative process necessitating regular revisions and continuous improvement.

• Develop the risk assessment framework and method : Outline the risk assessment’s scope, goals, and methodology. Determine the assessment frequency, responsible personnel, and available resources. Ensure compliance with regulatory mandates and industry-leading practices. For help, contact our FinScan AML consulting team.
• Identify risk factors : Identify the relevant risk factors that apply to your institution, considering aspects like the nature of your business, customer demographics, products/services, delivery channels, geographic locations, transaction monitoring alerts, and watchlist screening results.
• Collect and evaluate data : Gather relevant data from internal and external sources. This may include customer information, transaction data, external risk indicators, typologies, industry reports, regulatory guidance, and intelligence sources. Ensure data quality and completeness for accurate risk assessment.
• Assess inherent risk : Evaluate each identified risk factor to determine its inherent risk level. Consider the probability and potential impact of money laundering and terrorist financing activities associated with each factor. Use historical data, industry trends, typologies, and regulatory guidance to define the best level of risk assessment.
• Build a risk model : Develop a risk scoring method to quantify the identified risks. Assign risk scores or ratings to each risk factor based on its significance, likelihood, and potential impact. This aids in prioritizing risks and allocating resources effectively. Include both qualitative and quantitative factors in the scoring process.
• Mitigate and control risks : Identify and implement suitable risk mitigation measures for each risk profile. These might include enhanced customer due diligence, transaction monitoring, sanctions screening, staff training, internal controls, and governance practices. Implement controls that are proportional to the risk level and comply with regulatory requirements.
• Monitor and review : Continuously monitor and review the effectiveness of risk mitigation measures and the overall risk assessment framework. Regularly update risk assessments to accommodate changes in the institution’s risk profile, regulatory landscape, emerging risks, and industry best practices. Maintain a feedback loop to improve the risk assessment process over time.
• Report : Generate reports for management, regulators, and internal stakeholders to communicate risk exposure, mitigation actions, and the effectiveness of the AML program.

Establishing an effective AML risk assessment framework can seem daunting. The obligation to comply, manage risk factors, and evaluate their potential influence on money laundering activities can often seem overwhelming. Moreover, gathering data from multiple sources, often in an imperfect state, and building a model that accurately represents the level of money laundering risk, can add to this complexity.

However, the process doesn’t have to be so complicated. At FinScan, we provide a unique combination of AML consulting services , data quality proficiency, and advanced risk scoring technology . We help organizations in setting up a robust risk assessment program that not only fulfills regulatory requirements but also delivers a comprehensive understanding of their exposure to money laundering risks. Our team of experts will guide you through the entire process, from identifying pertinent risk factors to creating a customized risk model tailored to your specific needs.

Steve Marshall is the director of FinScan Advisory Services. He brings more than 40 years’ experience in the area of risk management, specializing in anti-money laundering (AML) compliance. Having served in a number of roles at US and global financial institutions, Steve honed his skills navigating the complex landscape of regulatory compliance in financial services. His reputation as a trusted advisor to organizations worldwide was further solidified in his subsequent role as a principal in the financial crimes enforcement group at a Big 4 firm, where he guided the successful implementations of AML programs within the banking and financial services sector.

At the helm of FinScan’s Advisory Services, Steve leverages his wealth of experience to assist organizations in establishing robust AML programs. Recognizing the vital role that data quality plays in driving effective watchlist screening, Steve emphasizes the critical importance of utilizing good data in conjunction with cutting-edge technology to drive AML program effectiveness.

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How to Conduct an AML Risk Assessment

All UK businesses have a responsibility to prevent money laundering and other forms of financial crime.

Risk assessments are a key component of any firm's anti-money laundering (AML) tool kit, and can help businesses to measure the likelihood that they will inadvertently support or engage in criminal behaviour.

The Money Laundering, Terrorist Financing and Transfer of Funds (Information on the Payer) Regulations 2017 (MLR 2017) made it a legal requirement for UK businesses in the regulated sector to adopt a risk based approach to their anti-money laundering efforts. This not only helps reduce the damage done by money laundering to the UK economy but gives companies flexibility in how they design and deploy their anti-money laundering procedures; as such risk assessments can vary between companies and sectors.

This guide explains what risk assessments are, and how any business can apply them to combat money laundering while meeting their regulatory compliance obligations.

What is an AML Risk Assessment?

A money laundering risk assessment is a process that analyses a business's risk of exposure to financial crime. The process aims to identify which aspects of the business put it at risk of exposure to money laundering or terrorist financing. It achieves this by monitoring and assessing known vulnerabilities, also commonly referred to as Key Risk Indicators (KRIs). 

Anti-money laundering risk assessments form part of the required risk based approach . They should form part of, and tie into, a company’s overarching strategy to avoid facilitating the laundering of illicit funds.

There are two types of risk assessments required as part of a risk based approach. These are a companywide risk assessment and risk assessments of individual transactions.

A company-wide risk assessment is a floor to ceiling review of a business to identify what external risks of money laundering they face and where in their business is at risk of being exploited by criminals seeking to launder illicit funds. Once this is done it is used as the foundation for a company to design their risk assessment and anti-money laundering processes.

After identifying and highlighting the money laundering risks their company is facing, directors then must design an appropriate risk assessment procedure to ensure they identify any potential transaction that is part of a money laundering scheme.

Why are AML Risk Assessments Required

Certain businesses are required to conduct anti-money laundering risk assessments under Regulation 18 of the Money Laundering, Terrorist Financing and Transfer of Funds (Information on the Payer) Regulations 2017 (MLR 2017).

On a practical level, a risk assessment could help a business to:

• use a risk-based approach to identifying and preventing money laundering.
• understand the risks associated with various business relationships and commercial activities.
• create policies, procedures, and controls that actively reduce the risk of financial crime.
• make more informed decisions about employees and clients.
• identify transactions and relationships that involve an at-risk or sanctioned country.
• Evaluate risk reduction measures.

Ultimately, an AML checks risk assessment can help businesses to reduce the risk of money laundering and terrorist financing. These measures are an essential part of any anti-money laundering compliance program, and can help organisations to stay on the right side of the law

Money Laundering Risk Indicators

Businesses can conduct a money laundering risk assessment by monitoring key risk indicators. International authorities generally apply five primary categories of risk indicator that businesses should assess:

• The size, nature, and complexity of a business.
• The type of customer involved (e.g. B2B or B2C).
• The types of products and services involved in a transaction.
• The methods used to onboard new customers and communicate with existing ones.
• Geographical factors

By assessing these individual factors, businesses can allocate a risk rating to a transaction or customer relationship. Ratings of low, medium, and high can be used when applying a simple risk range, whereas more advanced risk ranges extend to very low and very high ratings.

How to do a Company-wide risk assessment

  The first step of this assessment is for directors and employees to work together to identify how their business could be used to facilitate money laundering and how likely this is to happen. It is important to note that UK regulation requires that staff have sufficient training to be able to spot these risks. There is no set way that this assessment has to be carried out but it must review every aspect of the business. Once this has been done sufficient procedures should be designed and put in place to negate these risks.

It important that this process be well documented; as a company may be asked to prove it is compliant with UK anti-money laundering regulations , especially if it has been implicated in a money laundering scheme.

Things to consider in a companywide risk assessment are:

• The risks posed to their industry
• The risks posed by their business structure
• The risks posed by their products and/or services
• The risks posed by their business processes
• The risks posed by the geographical areas they operate in
• The risks posed by their distribution and payment channels. E.g. cash over the counter, bank transfers etc
• The risks posed by their customer base

This process should be reviewed every 12 to 18 months, or if a business undergoes any significant changes, and any necessary changes to internal procedures made.

How to perform an Anti-Money Laundering risk assessment

An anti-money laundering risk assessment’s purpose is to gauge if a transaction, and any individual involved in it, is possibly involved in money laundering and if any anti-money laundering checks need to be carried out or even if the transaction should not be performed at all.

The companywide risk assessment will have highlighted the greatest areas of risk and in these cases thorough anti-money laundering checks should be performed as a matter of course. Risk assessments should still be applied to transactions that were decided to be low risk in the companywide risk assessment.

A risk assessment is largely based on intuition and knowledge of how criminals exploit the private sector to launder money as well as proscribed business processes. It is therefore imperative, and a company’s responsibility, that the staff performing these assessments have the adequate training and tools to perform them.

There are some general key risk drivers that should be considered in each risk assessment:

• Clients seeking undue anonymity or secrecy and not willingly revealing their identity
• Clients acting through a third party
• A third party not being transparent about who they are acting on behalf of or who the ultimate beneficiary is
• Clients introduced to you by a third party, as you do not know the due diligence that has taken place
• Clients you have not obtained via the methods usual to the business
• Clients involved with cash based businesses
• Clients from abroad, especially from countries with low regulatory standards, high corruption or sanctions
• Clients from outside the usual customer base
• Clients involved in emerging sectors or who’s business has recently pivoted
• Clients with, or operating for an individual with, high net worth
• Clients wanting to deal in cash
• Clients with a criminal history
• Politically exposed clients
• Large transactions
• One off transactions

If the risk assessment finds any of these key risk drivers, any other risk drivers specific to a business as found in its companywide risk assessment or has any concerns then the company’s anti-money laundering check procedures should be followed.

Regardless of whether a risk is found or not, the findings of and methods applied in the risk assessment should be recorded.

Assessing High-Risk Activities

Businesses must pay particular attention to any high-risk activities when conducting a risk assessment. Each year, the UK government publishes a National Risk Assessment (NRA) that outlines the latest trends in money laundering and terrorist financing. This can help when prioritising certain activities as part of a risk-based approach to compliance.

In the UK's 2020 NRA, the following activities were identified as high-risk:

• conveyancing
• client account services
• trust and company formation
• financial technology services
• cash-related services
• the use of crypto assets and virtual money

Businesses should carefully consider whether their compliance framework does enough to identify and address these risks.

At the same time, organisations must pay close attention to the warning signs of money laundering and adjust their policies, controls, and procedures accordingly. This is especially true when dealing with customers and transactions that involve jurisdictions classified as high-risk by the Financial Action Task Force (FATF).

Risk Assessment during Customer Onboarding

A risk assessment can form a substantial part of the customer onboarding process. This opportunity should be used to conduct thorough due diligence before forming closer ties with an individual or organisation.

As part of an onboarding risk assessment, customers should be vetted for money laundering and terrorist financing risk factors. This process should include screening for adverse media, sanctions, and politically exposed persons (PEPs).

In addition to the above, businesses ought to be cautious when dealing with customers that perform actions that are at odds with their profile. This might happen if a customer suddenly attempts to enter into a high-value transaction, pay via a previously unrelated entity, or engage in a transaction that makes no commercial sense.

If a risk assessment flags any of these factors it may be necessary to ask further questions of a potential customer, or even to file a suspicious activity report (SAR).

Improve Your Approach to Risk Assessments with Red Flag Alert

Risk assessments are essential for businesses that need to comply with anti-money laundering regulations. Not only can they help to protect the economy from the threat of financial crime, but they can also prevent financial and reputational damage to the organisations involved.

Red Flag Alert can improve your risk assessment process by providing your business with fast access to reliable data on over 6.5 million businesses. With over 100,000 updates every day, users can trust this data to vet potential customers and verify any claims they make. Credit check any company and conduct AML checks  efficiently with one easy-to-use platform.

To discuss how Red Flag Alert can help to streamline your approach to risk assessments, get a free trial today

or see our guide on how to perform an AML risk assessment. 

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How can you elevate your AML risk assessment?

In our ever-evolving digital world, technology has changed the way that we make payments, as well as our ability to send money at any time, anywhere in the world.  It has also made it easier for fraudsters to conceal the origins of illegally obtained funds, making them appear to come from a legitimate source.  Indeed, with money laundering schemes costing some 2-5% of the global GDP – up to 30% of that figure originating in the US alone (costing upwards of $300B a year) – it’s pertinent that businesses respond appropriately to the guidance of authoritative and regulatory bodies worldwide. This is where the anti-money laundering (AML) risk assessment comes in.

Let’s take a deep dive into why an AML risk assessment is necessary and the best practices for conducting an effective AML risk assessment as part of a larger AML compliance program.

What is an AML risk assessment?

An AML risk assessment is a key component of any AML tool kit, enabling businesses to measure the likelihood that a customer or client is involved with money laundering or terrorist financing. An AML risk assessment will measure the risk level of each client, performing due diligence to minimize any potential involvement in a money laundering scheme.

Who conducts an AML risk assessment?

Ultimately, an AML risk assessment is a worthwhile process for any organization that conducts financial transactions. Regulators worldwide have made it mandatory for financial institutions under the AML and Counter-Terrorism Financing (CTF) laws and regulations to take the appropriate preventative measures against such financial crimes, or else risk serious penalties and regulatory audits.

How is AML regulated?

To combat AML worldwide, the Financial Action Task Force (FATF), an inter-governmental body that sets standards to guide countries to develop and update their AML and CTF laws, has been created. The FATF includes 39 members and 37 member jurisdictions , as well as the European Commission and the Gulf Cooperation Council.

Specifically (and for example), the USA has the Bank Secrecy Act ( BSA ) and the US Patriot Act , Canada has the Proceeds of Crime (Money Laundering) and Terrorist Financing Act,  Australia has the AML/CTF Act , while Europe is guided by a series of legislative directives, including the most recently released Sixth AML Directive ( 6AMLD ).

The five steps to performing an AML risk assessment

While completing an AML risk assessment is necessary to comply with regulations, understanding the risk level of each client and transaction also protects your business and your reputation.  Below are five steps to follow to ensure compliance and protection.  

1. Document key risk indicators

The first step for conducting an AML risk assessment is to create the appropriate documentation regarding key risk indicators (KRIs) and, in turn, how they relate to your business. This documentation will outline the support for the risk analysis. Remember – document everything, including your thought processes. As information changes and evolves, it helps to have everything cataloged to be sure your processes stay up-to-date and relevant.

Common categories of KRIs that should be documented include:

Clients/Customers/Business entities:  Which type of individuals do you do business with? Are they who they say they are? Some will have a higher risk, such as:

• Politically Exposed Persons (PEPs)
• Non-Resident Aliens
• Professional Service Providers

Be sure to complete a sanction screening to confirm that any individual you are working with is not on any sanction lists. And remember, doing business with PEPs is not necessarily banned, it is simply deemed high risk.

Meanwhile, if your client is a business entity, ask yourself who ultimately controls or benefits from their activities? Be sure to cross-reference any information on file with records kept at the company’s house and other beneficial ownership registers.

Products/Services:  It’s important to understand and analyze the risks associated with the products and services you offer. For example, the following comes with higher risk:

• Remote deposits
• Probate services
• Gambling services
• Cryptocurrency services
• ATM and cash services
• Foreign correspondent accounts
• Loan portfolios
• Online account opening and access

When providing a higher-risk service, keep a lookout for any red flags associated with your customer’s behavior. For example, ask yourself: Are the services they require consistent with their business rationale?

Delivery channels:  It’s a good idea to remember that some delivery channels can increase money laundering risk, especially if they can disguise the true identity of the client’s activity. Remember to consider whether the service/product will be delivered in person or remotely or provided directly or via an intermediary.

Geographic location:  A core component of any AML risk assessment is identifying the geographic locations that pose a higher risk. For example, do you operate in an area where there are higher rates of drug trafficking? To be thorough, confirm geographic risk through a list from the FATF or other such organizations.

And don’t forget, your customer doesn’t need to be in a foreign land to set off a red flag. If they are in a different city or province, enquire as to why they are coming to you instead of seeking a similar service closer to them, geographically.

Transactions:  Naturally, an AML risk assessment will involve the evaluation of the type of transactions your business engages in. For example, how does the number of international wire transfers compare to domestic ones? Or what is the volume of loan transactions and private ATM customers?

2. Employ dedicated staff

No matter the size of your organization, ensuring adequate staff is employed to dedicate time to compliance is essential when conducting your AML risk assessment.  

3. Identify the inherent risk

Inherent risk represents the exposure your business will have to money laundering risk should you not put any processes in place to mitigate them. This step of identifying the inherent risk builds upon your documentation process in step one.

Once you have identified the inherent risks to your organization, you need to implement controls to reduce them. These can be broken down simply into three categories: weak, adequate and strong.

4. Determine the residual risk

Once you have identified the inherent risk to your organization and, in turn, the effectiveness of the internal control environment you have in place, you can move on to determining the residual risk. This category of risk is defined as the risk that remains once controls have been put in place to mitigate the inherent risk. In other words, what gaps in your controls are present that could enable money laundering?

5. Rate the risk

Best practice involves applying a three-tier rating scale to assess the risk of money laundering or terrorism funding occurring, identified as high risk, moderate risk or low risk. Should the risk be rated high, your mitigation efforts are not effective enough and additional risk management measures should be implemented immediately. Ultimately, the strength of your controls can help determine the risk score. For example, when there are adequate controls in place, risk ratings might reduce from a three to a two.

Furthermore,  best practice dictates one assess the risk at all levels of AML-regulated business. This means that a risk assessment should be conducted at the following levels:

• The transaction level (by whomever is dealing with the transaction)
• The customer/client level (by whomever is dealing with the customer)
• The business level (by the appropriate individual in senior management/legal/compliance)

Finally, when appropriate, it never hurts to go one step further and perform a risk assessment at the sectoral level, the national level and the international level.

Cultivate a culture of compliance

Remember, the AML risk assessment process is an ongoing one. By cultivating a culture of compliance and conducting regular audits of your processes, you can be sure your organization remains aligned with regulatory changes and minimizes the likelihood of risk affecting your business and reputation.

Unfortunately, despite the risk assessments, controls and strict processes we implement, financial fraud is evolving faster than ever. In fact, in 2022, financial services businesses saw a 79% increase in document fraud compared to the previous year. Given the state of the current economic climate, this situation isn’t predicted to settle anytime soon.

Therefore, in an environment so fraught with fraud, going beyond the regulated assessment requirements is recommended. As we have discussed in previous blogs dedicated to KYC compliance , embracing a digital transformation strategy is a must. What this means is balancing your obligations to AML assessments and compliance with innovative, digital identity verification that can help protect your business against the latest sophisticated fraud trends without impacting the customer experience.

In fact, by enhancing your approach to AML (and KYC) compliance with comprehensive online capabilities like digital identity verification pre-AML risk assessment, you will not only better mitigate sophisticated fraud attacks, such as synthetic identities , but also provide an even more seamless customer experience from the very first touchpoint – account creation.

Want to discover how you can go beyond best practices for conducting your AML risk assessment with digital identity verification? Contact us today.

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CDD AML Risk Assessment Process – Customer Risk Rating Methodology

CDD AML Risk Assessment Process | Customer Risk Rating Methodology

When conducting AML customer due diligence, financial institutions perform AML risk assessment to determine the overall risk rating for the customer.

Below is an overview of the various variables that are analyzed as part of a CDD AML risk assessment.

The overall customer risk assessment and subsequent rating is based on a risk rating methodology that is developed using industry standards or customized rules defined by the AML Compliance Officer.

Image Source: Risk Assessed Variables – Customer Due Diligence

Customer Risk Rating Tool and Methodology

AML KYC BSA risk assessment and rating is performed during the client onboarding phase and also throughout the life of the customer.

A customer risk rating tool or solution is normally utilized in conducting due diligence and risk assessment on each customer prior to opening the account.

In most cases, after developing a risk rating methodology, it needs to be approved by both the firm’s Compliance and the Business senior management, before it is configured into the risk rating tool.

Generating a Customer Risk Rating

The below customer elements need to be risked assessed by entering into the risk rating tool to generate an overall customer risk rating of: Low, Medium or High [The firm may also use a risk category of Low or High, without the “Medium rating”]

When the risk rating tool generates a final rating, the AML Compliance Officer will be sent a notification for approval. Approval may be evidenced in writing or electronically. The AML Compliance Officer functions may be delegated to a designee. In such a situation, the designee needs to be a highly trained in AML compliance matters.

After risk rating the customer, all high risk cases will need to be escalated for further analysis.

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List of Key Assessment Factors

An overall rating is to be assigned based on the evaluation of the client and account characteristics as presented below.

The rationale supporting each of the elements listed below must be clearly documented.

In the event that a risk rating platform is used to calculate the rating, reliance may be placed on the platform so long as the criteria conform to the requirements herein.

• Customer’s name
• Customer’s address and country
• Type of customer (Domestic, foreign, LLC, Corp, regulated, high-cash business, etc.)
• Industry in which the customer does business
• Anticipated account activities
• The customer’s source of asset / wealth
• Reputation of the customer (Cigarette company? Weapons dealer? Etc.)
• The account’s beneficial owners (individuals or corporations that benefit from or have controlling rights over the account)
• Purpose of the account

As part of a firm’s AML BSA operating model, there needs to be well-defined procedures and processes that list the key assessment factors that will be assessed when conducting customer due diligence.

Assessments and Risk Rating Methodology

Each assessed variable is assigned a low, medium or high risk rating.

For example, a customer’s domicile (country or operations or registration) might be rated low if the customer is domiciled in a low risk country (i.e., the US) or rated high if the customer is located in a high risk country (i.e., Colombia or Cuba).

In addition, customers involved in high cash usage businesses (e.g., gambling centers) might have a “customer type variable” that is rated high being that they possess a higher probability of money laundering risk. Drug cartels have been known to attempt to launder money via gambling centers.

The overall goal of customer risk assessment is to generate a single overall customer risk rating of low, medium or high. This overall risk rating is normally a cumulative average of the ratings of the respective “assessed variables”.

However, financial firms are responsible for designing their respective risk rating methodologies, which might differ from the one presented here. For example, rather than taking a cumulative average of the individual ratings, a firm might weight some individual ratings higher than others

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Managing AML Risk Assessment: Tools for Customer Evaluation

As technology advances, the financial and crypto-asset sectors have become more complex. At the same time crimes have become more sophisticated and technologically advanced. This has made detection and AML risk assessment with AML risk scoring more difficult.

This leaves firms vulnerable to criminal activity. The question of how to “separate the wheat from the chaff” and find the bad actors among a bunch of perfectly legitimate customers is more important than ever. The risk-based approach and perfectly working AML risk assessment could be the answer. But only if it is applied correctly. By staying prepared, you can ensure compliance with the regulator and provide uninterrupted service to your customers

This blog explores tools and techniques for evaluating anti-money laundering  (AML) risk management solutions. It considers regulatory requirements and the need to prevent criminal use. Also, it addresses the business need for the best customer experience. Operating profitably is another crucial aspect to consider.

Understanding the importance of AML risk assessment in AML/CFT compliance programs

In simple terms, the risk-based approach is just a fancy term for segmenting your customer portfolio into groups. It filters out potential wrongdoers from those without concerns, helping to identify any links to criminal activity.

AML risk assessment is another word combination used by the regulator that indicates the same approach – don’t be chaotic, use your resources wisely, don’t bother customers who are not risky and focus your efforts on customers who are possible criminals or associated with criminal activity.

A risk-based approach is at the heart of any AML/CFT compliance programme and rests on two pillars. Holistic (enterprise-wide or business-wide) risk assessment and targeted (individual customer) risk assessment. ML/TF risk assessment should be an integral part of the firm’s overall risk management framework. And it should target the basic steps of risk management: risk identification, risk assessment, risk control, and risk mitigation or avoidance (the latter should be used carefully and should not lead to de-risking of the entire client group).

Sounds simple? In theory it is but putting it into practice raises a number of issues. The wrong risk-based approach can result in unhappy customers burdened with unnecessary due diligence. It can also waste resources and miss criminal activity.

Here are some tips from our experts on how to approach risk assessment.

Holistic customer segmentation or Enterprise-Wide Risk Assessment (EWRA)

If you have a ‘chicken or the egg’ conundrum, the answer is simple – enterprise-wide risk assessment always comes first. If you are a start-up, the holistic view of your ML/TF risks should be based on your business plan. Update this later with actual data.

Enterprise-wide risk assessment (or EWRA) is not a standalone exercise undertaken simply to satisfy the regulator. If done properly, EWRA could give you an answer on your target customer profile based on “peer grouping”. And this already sets some thresholds for further individual customer risk scoring and transaction monitoring . EWRA could give you some insight into:

• The ML/TF risks of your target customers and the weaknesses in the AML/CFT controls applied to these customers (or possible risks and possible controls if you are in the start-up phase);
• How to establish individual customer ML/TF risk assessment criteria, including criteria for triggering enhanced due diligence;
• How to tailor your transaction monitoring model: setting thresholds and limits for certain rules, customizing the frequency and intensity of transaction monitoring for certain customer groups;
• Determine the basis for calculating the actual resources required to implement the necessary AML/CFT controls.

Importance of quantitative data

Quantitative data should form the basis of the assessment of inherent ML/TF risks (either actual data over the selected business period or business plan). Data quality, including data accuracy, must therefore be ensure. So that the company can be confident that it is implementing the necessary AML/CFT controls:

Accuracy, so that the company can be confident that material distortions of the actual AML/TF results are avoided;

Completeness (including data from all business units).

The larger companies are using more sophisticated tools to obtain statistics from their internal databases. However it is still a challenge to ensure that accurate and complete data would feed into the EWRA results.

To properly assess the residual ML/TF risk, an overview of AML/CFT controls is required. Compliance reports, audit reports, reports on the results of monitoring back-testing, reports on operational risk incidents. These could be the source that the firm would be willing to examine before deciding whether the controls are adequate.

The assessment of residual risk is subject to the risk assessment model used by the entity. Validate the risk assessment model used for EWRA, as with all risk assessment models.

Targeted risk assessment or individual customer risk assessment

The data collected from customers (Know Your Customer, or KYC data) forms the basis for the individual customer risk assessment. When developing KYC questionnaires, the firm should use the results of the EWRA and consider having more comprehensive questionnaires for those customer segments that are exposed to higher risks and possibly simplified KYC questionnaires for those that do not raise concerns.

However, an individual client poses an individual risk relative to his or her peer group. And this should also be considered. Let’s take the example of a corporate customer domiciled in a low-risk country and using only domestic payment initiation services. It may pose a different risk to the same customer that expands its services to include cross-border payments to and from high-risk countries.

The higher risk clients will be subject to enhanced due diligence procedures, which will include not only additional data collection (e.g., on source of funds and assets), but also enhanced monitoring and senior management involvement in the client onboarding decision process. Therefore, to avoid overburdening the business with additional processes, you may be willing to have an accurate client AML risk scoring tool that addresses ML/TF risks in a way that satisfies the regulator and keeps the process as burdensome as possible for the business and, later, its clients.

Things to consider when developing AML risk scoring model

In developing a AML risk scoring model, you may wish to consider:

whether the AML risk scoring model meets all the mandatory criteria set by the regulator (client, geography, product, channel);

whether the AML risk scoring model takes into account the mandatory high-risk situations set by the regulator (e.g. an automatic high-risk score could be applied if the customer is a politically exposed person, registered in the high-risk country, etc.);

if the weighting of the risk criteria is not unduly influenced by a single factor and/or does not lead to a situation where it is impossible to classify any business relationship as high risk;

if it is possible to override the automatically generated risk score if necessary;

where the individual customer risk score is reviewed on a regular basis or when trigger events occur (e.g. when the customer wishes to take out a new product or service, when a certain transaction threshold is reached, etc.);

where the customer re-scoring is applied when there are significant changes to the AML risk scoring model or when there are significant changes to components of the AML risk scoring model (e.g. significant changes to the geographical risk score due to external factors such as inclusion of the country on the FATF grey or black list).

Although KYC data is an important part of the risk assessment, the company should consider including internal and external data sources. This could be as additional information that could be evaluated as additional customer risk criteria, such as customer behavior, transaction history, internal investigation data, adverse media screening information, regulatory or law enforcement inquiries.

Validation of risk assessment models

Assessing the risk of money laundering in a business or financial institution. By analyzing customer and transaction data, AML risk assessment helps organizations determine the likelihood of money laundering activities and implement effective risk management strategies to mitigate these risks.

The primary objective of AML risk assessment is to identify potential risks and vulnerabilities. And to indentify them in an organization’s operations, systems and processes. This process enables organizations to develop risk management plans. Thise that address any weaknesses and vulnerabilities and prevent or mitigate money laundering risks. Effective AML risk assessment and management plans can help organizations avoid hefty fines, reputational damage and legal repercussions.

Risk assessment tools

To effectively manage AML risks, organizations can use a variety of tools. One of the most common techniques is risk scoring, which involves assigning scores to customers based on their risk level. By analyzing data such as transaction history, location and occupation, organizations can identify customers who pose a higher risk of money laundering.

Transaction monitoring is another tool that enables businesses to assess and flag suspicious transactions in real time. This can be achieved using algorithms that look for patterns and anomalies that may indicate money laundering activity.

Risk management techniques

Once organizations identify money laundering risks, they must implement effective risk management techniques. These techniques are crucial for mitigating the identified risks effectively. Rules-based monitoring is one such technique that organizations can use to identify suspicious transactions. This involves creating specific rules to help identify suspicious transactions based on pre-defined criteria.

Another effective risk management technique is to train employees to identify and report suspicious activity. Achieve this through regular training sessions. Educate employees about money laundering risks and spotting red flags.

Statistics on AML risks in the digital age

Criminals are increasingly using digital channels to launder money, according to a report by the Financial Action Task Force (FATF) . The report states that “the number of cases in which virtual assets have been used for money laundering has increased rapidly in recent years”. This highlights the importance of implementing effective AML risk assessment and management strategies in the digital age.

The report also identifies some of the key challenges organizations face in managing AML risk in the digital age. These challenges include the complexity of digital transactions, the lack of regulation in some jurisdictions and the use of new technologies. Such as virtual currencies and online payment systems.

In today’s fast-paced digital world, money laundering poses a significant threat to businesses and financial institutions. But with the right tools and techniques, it’s possible to stay one step ahead of potential risks. Organizations can protect themselves and their customers from the damaging effects of fraudulent activity. They can do that by implementing AML risk assessment and management strategies,

Effective AML risk assessment and management plans can help businesses avoid hefty fines, reputational damage and legal repercussions. So whether you’re a seasoned financial professional or just starting out in the industry, now is the time to take action and protect your business from the ever-evolving threats of money laundering. Remember, an ounce of prevention is worth a pound of cure!

Conclusions

You should consider the mandatory criteria set by the regulator. But should also take into account the specifics of your business model:

• When considering customer risk, you may want to consider what part of your business will be focused on individual customers and what part of your business will be focused on businesses. When analyzing the latter, consider the type of companies you will be serving (e.g. private or public companies), the industries these companies represent (e.g. gambling, finance, real estate, precious metals, crypto asset exchanges, sports, cash-intensive businesses, etc.) and other possible customer groups.
• When considering product/service risk, carefully analyze their business model and cash flow schemes. When analyzing products and services, ask yourself whether you really understand the AML/CFT requirements. Those associated with the services you provide (e.g. if you offer BaaS or related banking services).
• Different countries may pose different challenges. This is due to differences in AML/CFT frameworks. So you may want to consider the location of the target customer. And also the direction of the money flow, and the ML/TF risks associated with it.
• Do not forget to include them when considering the service channel risk. Do so if you are planning to use the network of agents or intermediaries.
• When analyzing the customer group, the firm should identify possible ML/TF risks associated with this group and identify control weaknesses in order to take the necessary risk mitigation measures.

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• Customer Risk Assessment

Customer Risk Assessment is a crucial step in ensuring compliance with anti-money laundering (AML) regulations. This article explores the importance of conducting thorough customer risk assessments and how Kyros AML Data Suite empowers AML professionals with advanced tools and capabilities to streamline and enhance this process.

Welcome to our  profound dictionary article on “Customer Risk Assessment.” In the ever-evolving landscape of anti-money laundering (AML) compliance, customer risk assessment plays a pivotal role in identifying and mitigating potential risks associated with clients. This article will provide a comprehensive definition of customer risk assessment, practical examples of its application, relevant statistics and numbers, and an introduction to Kyros AML Data Suite—a powerful AML compliance software designed to enhance customer risk assessment processes. AML professionals will find valuable insights and information to strengthen their compliance efforts and protect their organizations from financial crimes.

Customer Risk Assessment refers to the process of evaluating the level of risk posed by customers or clients in relation to potential involvement in money laundering, terrorist financing, fraud, or other illicit activities. It involves gathering and analyzing relevant information about customers, their activities, and the jurisdictions in which they operate to determine the level of risk they pose to the organization. The assessment helps financial institutions and other regulated entities to categorize customers into risk segments and apply appropriate measures to manage and mitigate identified risks effectively.

Practical Examples

In the world of anti-money laundering (AML) compliance, customer risk assessment plays a crucial role in identifying and managing potential risks associated with clients. This article will delve into practical examples of how customer risk assessment is applied in practice, providing AML professionals with valuable insights to strengthen their risk assessment processes. By understanding real-world scenarios and applying effective risk assessment techniques, AML professionals can proactively mitigate risks and safeguard their organizations from financial crimes.

High-Risk Jurisdiction

One practical example of customer risk assessment is evaluating clients from high-risk jurisdictions. High-risk jurisdictions refer to countries or regions with a higher likelihood of financial crimes, corruption, or weak AML regulations. When conducting customer risk assessments, AML professionals carefully consider the jurisdiction where a customer is located or operates.

This involves gathering relevant information such as the country’s political stability, regulatory framework, and financial transparency. By categorizing customers from high-risk jurisdictions, organizations can apply enhanced due diligence measures, such as additional identity verification, source of funds documentation, and ongoing monitoring. This helps mitigate the potential risks associated with customers operating in jurisdictions that pose a higher AML risk. A robust customer risk assessment process ensures that AML professionals are vigilant in their efforts to identify and address any potential red flags, protecting their organizations from financial crimes and regulatory non-compliance.

Politically Exposed Persons (PEPs)

Another practical example of customer risk assessment is the identification and evaluation of Politically Exposed Persons (PEPs). PEPs are individuals who hold prominent public positions or have close associations with influential figures, such as government officials, heads of state, or senior executives of state-owned enterprises. These individuals may have access to substantial resources and may be more susceptible to corruption, bribery, or money laundering activities. AML professionals conduct thorough customer risk assessments to identify PEPs within their client base and assess the potential risks associated with these relationships. This process involves gathering information on the individual’s political connections, sources of wealth, and business activities.

By identifying PEPs and conducting enhanced due diligence, AML professionals can ensure appropriate risk mitigation measures are in place. This includes ongoing monitoring of PEP relationships, rigorous transaction monitoring, and implementing enhanced controls to prevent the misuse of the financial system for illicit purposes. Customer risk assessment plays a crucial role in safeguarding organizations against the risks associated with PEP relationships and helps maintain regulatory compliance and financial integrity.

Unusual Transaction Patterns

Another practical example of customer risk assessment is the identification and analysis of unusual transaction patterns. AML professionals closely monitor customer transactions to detect any abnormal or suspicious activities that may indicate potential money laundering, terrorist financing, or other illicit financial activities. Unusual transaction patterns can include frequent large cash deposits or withdrawals, structuring transactions to avoid reporting thresholds, rapid movement of funds between accounts, or transactions involving high-risk jurisdictions or individuals.

By analyzing these patterns, AML professionals can identify potential risks and take appropriate actions to mitigate them. This may involve conducting additional due diligence on the customer, filing suspicious activity reports (SARs), or implementing enhanced transaction monitoring measures. Unusual transaction pattern analysis is a critical component of customer risk assessment as it helps identify potential red flags and enables organizations to proactively address potential money laundering or terrorist financing risks.

Complex Business Structures

Complex business structures are another practical example of customer risk assessment in the field of anti-money laundering (AML). A complex business structure refers to the intricate organization and ownership arrangements of companies, often involving multiple layers of subsidiaries, branches, and offshore entities. These structures can be deliberately designed to obscure the true ownership and control of funds, making it challenging to trace the origin of funds and identify potential money laundering activities. AML professionals need to assess the risk associated with customers involved in complex business structures to ensure compliance with regulatory requirements and mitigate the potential for illicit financial activities.

This involves conducting thorough due diligence, including researching the ownership structure, understanding the business activities, and evaluating the transparency and legitimacy of the organization. By analyzing complex business structures, AML professionals can identify potential risks, such as layering or funneling illicit funds through the network of companies and implement appropriate measures to mitigate those risks. This may involve enhanced due diligence procedures, ongoing monitoring, and reporting suspicious activities to the relevant authorities. Assessing the risk associated with complex business structures is crucial for effective customer risk assessment and plays a vital role in preventing money laundering and financial crimes.

Statistics and Relevant Numbers

In the realm of customer risk assessment, understanding the relevant statistics and numbers is essential for AML professionals to make informed decisions and develop effective risk mitigation strategies. While the specific statistics may vary based on jurisdiction and industry, there are some general figures that provide valuable insights into the importance of customer risk assessment. For example, according to a report by the Financial Action Task Force (FATF), customer due diligence failures, including inadequate risk assessments, were identified as one of the primary reasons behind money laundering and terrorist financing cases.

In another study, it was found that financial institutions that implemented robust customer risk assessment frameworks experienced a significant reduction in the number of suspicious transaction reports filed. Additionally, industry reports suggest that around 80% of money laundering cases involve the exploitation of customer accounts and identities. These numbers highlight the critical role of customer risk assessment in the overall AML efforts. By conducting thorough risk assessments, financial institutions can better identify and mitigate potential risks associated with customers, ensuring compliance with regulations, and protecting themselves from financial crimes.

Kyros AML Data Suite: Empowering AML Professionals

Kyros AML Data Suite is a powerful and comprehensive AML compliance software that empowers AML professionals in their fight against financial crimes. This advanced software solution is designed to streamline and enhance various aspects of the AML process, providing a range of benefits to financial institutions and AML teams.

First and foremost, Kyros AML Data Suite offers robust risk assessment capabilities. With its sophisticated algorithms and data analytics, it enables AML professionals to conduct thorough customer risk assessments. The software leverages advanced technology to analyze vast amounts of data, including customer profiles, transaction history, and external data sources, to identify potential high-risk individuals and entities. By providing accurate and comprehensive risk assessments, Kyros AML Data Suite enables AML professionals to make informed decisions and allocate their resources effectively, focusing on the areas that require the most attention.

Furthermore, Kyros AML Data Suite enhances transaction monitoring and suspicious activity detection. The software employs advanced machine learning algorithms to analyze transactional data in real-time, identifying patterns and anomalies that may indicate potential money laundering or other illicit activities. It helps AML professionals in identifying unusual transaction patterns, complex money laundering schemes, and suspicious activities that may go unnoticed through manual monitoring. By automating these processes, Kyros AML Data Suite enables AML professionals to efficiently identify and investigate suspicious transactions, ensuring compliance with regulatory requirements and mitigating financial risks.

Lastly, Kyros AML Data Suite offers comprehensive reporting and audit functionalities. It generates customizable reports and audit trails, allowing AML professionals to demonstrate their compliance efforts to regulators and internal stakeholders. The software provides real-time monitoring and reporting dashboards, enabling AML professionals to track key performance indicators, identify trends, and take proactive measures to strengthen their AML program. With its user-friendly interface and intuitive reporting features, Kyros AML Data Suite simplifies the reporting process and saves valuable time and resources for AML professionals.

In conclusion, customer risk assessment is a critical component of any robust anti-money laundering (AML) program. By conducting thorough assessments, AML professionals can identify and mitigate risks associated with their customers, safeguarding their institutions and the financial system as a whole. The examples discussed in this article, such as high-risk jurisdictions, politically exposed persons (PEPs), unusual transaction patterns, and complex business structures, highlight the importance of evaluating customer risks from various angles.

To effectively carry out customer risk assessments, AML professionals can leverage advanced technologies like the Kyros AML Data Suite. This powerful AML compliance software offers a range of benefits, including accurate risk assessments, enhanced transaction monitoring, and comprehensive reporting functionalities. By harnessing the power of data analytics and machine learning, AML professionals can gain deeper insights into customer behavior and quickly identify potential risks and suspicious activities.

By integrating Kyros AML Data Suite into their AML programs, professionals can strengthen their ability to detect and prevent financial crimes. The software empowers them to stay ahead of evolving regulatory requirements and combat emerging threats effectively. With its user-friendly interface and powerful features, Kyros AML Data Suite provides AML professionals with the tools they need to streamline their processes, allocate resources efficiently, and maintain a robust and effective AML program.

In a constantly evolving landscape of financial crimes, customer risk assessment remains a vital aspect of AML efforts. AML professionals must continue to adapt and leverage innovative technologies to effectively evaluate and mitigate risks associated with their customer base. With the support of advanced solutions like Kyros AML Data Suite, AML professionals can strengthen their AML programs, protect their institutions, and contribute to the global fight against money laundering and other financial crimes. For more information visit kyrosaml.com

• AML Dictionary
• aml software
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• kyros aml dashboard
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• BSA/AML Manual
• BSA/AML Risk Assessment

BSA/AML RISK ASSESSMENT

Objective: Review the bank’s BSA/AML risk assessment process, and determine whether the bank has adequately identified the ML/TF and other illicit financial activity risks within its banking operations.

Examiners must develop an understanding of the bank’s ML/TF and other illicit financial activity risks to evaluate the bank’s BSA/AML compliance program. This is primarily achieved by reviewing the bank’s BSA/AML risk assessment during the scoping and planning process. This section is designed to provide standards for examiners to assess the adequacy of the bank’s BSA/AML risk assessment process.

BSA/AML Risk Assessment Process 

To assure that BSA/AML compliance programs are reasonably designed to meet BSA regulatory requirements, banks structure their compliance programs to be risk-based. While not a specific legal requirement, a well-developed BSA/AML risk assessment assists the bank in identifying ML/TF and other illicit financial activity risks and in developing appropriate internal controls (i.e., policies, procedures, and processes). Understanding its risk profile enables the bank to better apply appropriate risk management processes to the BSA/AML compliance program to mitigate and manage risk and comply with BSA regulatory requirements. The BSA/AML risk assessment process also enables the bank to better identify and mitigate any gaps in controls. The BSA/AML risk assessment should provide a comprehensive analysis of the bank’s ML/TF and other illicit financial activity risks. Documenting the BSA/AML risk assessment in writing is a sound practice to effectively communicate ML/TF and other illicit financial activity risks to appropriate bank personnel. The BSA/AML risk assessment should be provided to all business lines across the bank, the board of directors, management, and appropriate staff. 

The development of the BSA/AML risk assessment generally involves the identification of specific risk categories (e.g., products, services, customers, and geographic locations) unique to the bank, and an analysis of the information identified to better assess the risks within these specific risk categories. 

Identification of Specific Risk Categories

Generally, the first step in developing the risk assessment is to identify the bank’s risk categories. Money laundering, terrorist financing, or other illicit financial activities can occur through any number of different methods or channels. A spectrum of risks may be identifiable even within the same risk category. The bank’s BSA/AML risk assessment process should address the varying degrees of risk associated with its products, services, customers, and geographic locations, as appropriate. Improper identification and assessment of risk can have a cascading effect, creating deficiencies in multiple areas of internal controls and resulting in an overall weakened BSA/AML compliance program.

The identification of risk categories is bank-specific, and a conclusion regarding the risk categories should be based on a consideration of all pertinent information. There are no required risk categories, and the number and detail of these categories vary based on the bank’s size or complexity, and organizational structure. Any single indicator does not necessarily determine the existence of lower or higher risk. 

The subsections within Risks Associated with Money Laundering and Terrorist Financing provide information and discussions on certain products, services, customers, and geographic locations that may present unique challenges and exposures, which banks may need to address through specific policies, procedures, and processes. 

Analysis of Specific Risk Categories

Generally, the second step in developing the BSA/AML risk assessment entails an analysis of the information obtained when identifying specific risk categories. The purpose of this analysis is to assess ML/TF and other illicit financial activity risks in order to develop appropriate internal controls to mitigate overall risk. This step may involve evaluating transaction data pertaining to the bank’s activities relative to products, services, customers, and geographic locations. For example, it may be useful to quantify risk by assessing the number and dollar amount of domestic and international funds transfers, the nature of private banking customers or foreign correspondent accounts, the existence of payable through accounts, and the domestic and international geographic locations where the bank conducts or transacts business. A detailed analysis is important, because the risks associated with the bank’s activities vary. Additionally, the appropriate level and sophistication of the analysis varies by bank. 

The following example illustrates the value of the two-step risk assessment process. The information collected by two banks in the first step reflects that each sends 100 international funds transfers per day. Further analysis by the first bank shows that approximately 90 percent of its funds transfers are recurring well-documented transactions for long-term customers. Further analysis by the second bank shows that 90 percent of its funds transfers are nonrecurring or are processed for noncustomers. While these percentages appear to be the same, the risks may be different. This example illustrates that information collected for purposes of the bank’s customer identification program and developing the customer due diligence customer risk profile is important when conducting a detailed analysis. Refer to the Customer Identification Program , Customer Due Diligence , and Appendix J – Quantity of Risk Matrix sections for more information. 

Various methods and formats may be used to complete the BSA/AML risk assessment; therefore, there is no expectation for a particular method or format. Bank management designs the appropriate method or format and communicates the ML/TF and other illicit financial activity risks to all appropriate parties. When the bank has established an appropriate BSA/AML risk assessment process, and has followed existing policies, procedures, and processes, examiners should not criticize the bank for individual risk or process decisions unless those decisions impact the adequacy of some aspect of the bank’s BSA/AML compliance program or the bank’s compliance with BSA regulatory requirements.

Updating the Risk Assessment

Generally, risk assessments are updated (in whole or in part) to include changes in the bank’s products, services, customers, and geographic locations and to remain an accurate reflection of the bank’s ML/TF and other illicit financial activity risks. For example, the bank may need to update its BSA/AML risk assessment when new products, services, and customer types are introduced or the bank expands through mergers and acquisitions. However, there is no requirement to update the BSA/AML risk assessment on a continuous or specified periodic basis.

Assessing the Bank’s BSA/AML Risk Assessment

When evaluating the BSA/AML risk assessment, examiners should focus on whether the bank has effective processes resulting in a well-developed BSA/AML risk assessment. Examiners should not take any single indicator as determinative of the existence of a lower- or higher-risk profile for the bank. The assessment of risk factors is bank-specific, and a conclusion regarding the risk profile should be based on a consideration of all pertinent information. The bank may determine that some factors should be weighted more heavily than others. For example, the number of funds transfers may be one factor the bank considers when assessing risk. However, to identify and weigh the risks, the bank’s risk assessment process may need to consider other factors associated with those funds transfers, such as whether they are international or domestic, the dollar amounts involved, and the nature of the customer relationships. Regardless of the bank’s approach, sound practice would be to document the factors considered, including any weighting.

Examiners should assess whether the bank has developed a BSA/AML risk assessment that identifies its ML/TF and other illicit financial activity risks. Examiners should also assess whether the bank has considered all products, services, customers, and geographic locations, and whether the bank analyzed the information relative to those risk categories. 

For the purposes of the examination, whenever the bank has not developed a BSA/AML risk assessment, or the BSA/AML risk assessment is inadequate, examiners must develop a BSA/AML risk assessment for the bank based on available information. An examiner-developed BSA/AML risk assessment generally is not as comprehensive as one developed by the bank. Examiners should have a general understanding of the bank’s ML/TF and other illicit financial activity risks from the examination scoping and planning process. This information should be evaluated using the two-step approach detailed in the BSA/AML Risk Assessment Process subsection above. Examiners may also refer to Appendix J - Quantity of Risk Matrix when completing this evaluation.

Developing a BSA/AML Compliance Program Based on the BSA/AML Risk Assessment

The bank structures its BSA/AML compliance program to address its risk profile, based on the bank’s assessment of risks, as well as to comply with BSA regulatory requirements. Specifically, the bank should develop appropriate policies, procedures, and processes to monitor and control its ML/TF and other illicit financial activity risks. For example, the bank’s monitoring system to identify, research, and report suspicious activity should be risk-based to incorporate any necessary additional screening for higher-risk products, services, customers, and geographic locations as identified by the bank’s BSA/AML risk assessment. Independent testing (audit) should review the bank’s BSA/AML risk assessment, including how it is used to develop the BSA/AML compliance program. Refer to Appendix I - Risk Assessment Link to the BSA/AML Compliance Program for a chart depicting the expected link of the BSA/AML risk assessment to the BSA/AML compliance program.

Consolidated BSA/AML Risk Assessment

Banks that choose to implement a consolidated or partially consolidated BSA/AML compliance program should assess risk within business lines and across activities and legal entities. Consolidating ML/TF and other illicit financial activity risks for larger or more complex banking organizations may assist senior management and the board of directors in identifying, understanding, and appropriately mitigating risks within and across the banking organization. To understand ML/TF and other illicit financial activity risk exposures, the banking organization should communicate across all business lines, activities, and legal entities. Identifying a vulnerability in one aspect of the banking organization may indicate vulnerabilities elsewhere. Refer to the BSA/AML Compliance Program Structures section for more information.

Table of Contents

• Introduction
• Scoping and Planning
• Assessing the BSA/AML Compliance Program
• Developing Conclusions and Finalizing the Exam
• Assessing Compliance with BSA Regulatory Requirements
• Office of Foreign Assets Control
• Program Structures
• Risks Associated with Money Laundering and Terrorist Financing

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Unlocking Success: Optimizing AML Risk Assessment Methodology for Results

Understanding AML Risk Assessment

AML (Anti-Money Laundering) risk assessment is a crucial process for financial institutions and organizations to identify and manage the risks associated with money laundering and other illicit financial activities. It involves evaluating the potential risks and vulnerabilities within a business and implementing measures to mitigate them.

Importance of AML Risk Assessment

The importance of AML risk assessment cannot be overstated. It provides financial institutions and organizations with a systematic approach to identify, assess, and manage the risks associated with money laundering. A comprehensive risk assessment enables institutions to develop effective strategies, policies, and procedures to prevent and detect financial crimes, ensuring compliance with regulatory requirements.

By conducting an AML risk assessment, institutions can:

• Identify and understand the specific risks they face, including product-related, customer-related, and geographic risks.
• Tailor their AML compliance programs to address the identified risks effectively.
• Allocate resources and implement controls based on the level of risk.
• Detect and report suspicious transactions or activities that may be indicative of money laundering or other financial crimes.
• Demonstrate to regulators and stakeholders their commitment to mitigating AML risks and maintaining a robust compliance framework.

Challenges in Traditional Risk Assessment

Traditional AML risk assessment methodologies have their limitations. They often rely on annual reviews and static risk scoring, which may not capture the dynamic and evolving risks faced by institutions today. This approach can result in increased exposure to financial crime and regulatory penalties. To address this, institutions are increasingly adopting more dynamic approaches to risk assessment in the modern financial landscape ( NorthRow ).

Some of the challenges associated with traditional risk assessment include:

• Inability to capture real-time risks : Annual reviews may fail to capture emerging risks, leaving institutions exposed to new and evolving money laundering methods.
• Lack of granularity : Static risk scoring may not provide a detailed understanding of specific risks within products, services, customer segments, or geographic locations.
• Limited visibility into customer behavior : Traditional risk assessment methods may not capture changes in customer behavior or transaction patterns in real-time, making it difficult to detect suspicious activities promptly.
• Resource-intensive : Annual reviews require significant resources, both in terms of time and personnel, to conduct assessments and update risk profiles.
• Regulatory compliance : Traditional methods may not adequately meet the evolving regulatory requirements and expectations for robust risk assessment processes.

To overcome these challenges, institutions are exploring modernized AML risk assessment methodologies that leverage automation, advanced data analytics, and machine learning techniques. These dynamic approaches enable institutions to assess risks in real-time, adapt to changing regulatory landscapes, and make informed decisions to combat financial crimes effectively.

In the next sections, we will explore how technology can enhance AML risk assessment, the benefits of modernizing risk assessment methodologies, and specific frameworks and regulations related to AML risk assessment.

Enhancing AML Risk Assessment with Technology

To effectively combat money laundering and meet regulatory obligations, financial institutions are increasingly turning to technology to enhance their AML risk assessment methodologies. By leveraging automation, artificial intelligence (AI), real-time transaction monitoring, big data analytics, and biometric authentication, institutions can strengthen their AML compliance efforts.

Role of Automation and AI

Automation and AI play a transformative role in strengthening AML risk assessment processes. Advanced algorithms and AI streamline the onboarding process, customer due diligence , and transaction monitoring. By reducing manual errors and ensuring efficiency in compliance procedures, automation enhances the overall effectiveness of AML risk assessment ( LinkedIn ).

Real-Time Transaction Monitoring

Real-time transaction monitoring is a crucial component of effective AML risk assessment. Technology allows institutions to swiftly identify and respond to potential AML risks. Alerts can be triggered by predefined thresholds or anomaly detection algorithms, enabling proactive prevention of illicit transactions. Real-time monitoring enables financial institutions to stay vigilant and take immediate action to mitigate money laundering risks ( LinkedIn ).

Leveraging Big Data Analytics

Leveraging big data analytics is another powerful tool for enhancing AML risk assessment. By analyzing vast amounts of data, financial institutions can uncover hidden patterns and trends that help identify complex money laundering networks, terrorist financing channels, and emerging risks. Big data analytics enable more effective AML efforts, allowing institutions to stay ahead of evolving threats and protect against illicit activities ( LinkedIn ).

Biometric Authentication and Digital Verification

Technologies such as biometric authentication, facial recognition, and digital document verification have significantly improved the accuracy and efficiency of customer onboarding processes. These technologies make it more challenging for criminals to conduct illicit transactions using falsified identities. By implementing biometric authentication and digital verification, financial institutions enhance the security of their AML risk assessment processes and strengthen their ability to detect and prevent money laundering activities.

Incorporating these technological advancements into AML risk assessment methodologies is essential for financial institutions to stay ahead of sophisticated financial criminals and comply with stringent regulatory requirements. By embracing automation, real-time monitoring, big data analytics, and biometric authentication, institutions can enhance their ability to detect and prevent money laundering activities, ensuring a safer and more secure financial ecosystem.

Modernizing AML Risk Assessment Methodologies

As the financial landscape evolves, so must the methodologies for Anti-Money Laundering (AML) risk assessment. Traditional approaches to AML risk assessment have limitations that can hinder their effectiveness in capturing the real risks faced by institutions today. These limitations include the reliance on annual reviews and static risk scoring, which may not adequately address the dynamic nature of financial crime.

Limitations of Traditional Approaches

Traditional AML risk assessment methodologies often fall short in capturing the dynamic and evolving risks in the financial sector. Relying on annual reviews and static risk scoring may result in increased exposure to financial crime and regulatory penalties. To effectively mitigate these risks, modern AML risk assessment methodologies need to be more dynamic and adaptable to changing circumstances ( NorthRow ).

Benefits of Dynamic Risk Assessment

Dynamic risk assessment is a key component of modernizing AML risk assessment methodologies. By adopting a dynamic approach, institutions can better identify and respond to emerging risks in real-time. This allows for more proactive and effective risk mitigation strategies. Dynamic risk assessment takes into account various factors such as transaction patterns, customer behavior, and external data sources to continuously evaluate and reassess the level of risk.

Incorporating Advanced Data Analytics

Leveraging advanced data analytics is crucial for enhancing AML risk assessment methodologies. By analyzing large volumes of data, financial institutions can uncover hidden patterns and trends that aid in identifying complex money laundering networks, terrorist financing channels, and emerging risks. This enables more effective AML and Counter Financing of Terrorism (CFT) efforts.

Machine Learning in Risk Assessment

Machine learning plays a significant role in modernizing AML risk assessment methodologies. By utilizing machine learning algorithms, financial institutions can enhance the quality of data and improve the accuracy of risk assessments. Machine learning algorithms can detect complex financial crimes, identify high-risk entities with greater precision and speed, and assist in updating client profiles for effective risk assessment. Additionally, statistical analysis and machine learning can help identify red flags in customer behavior and streamline risk models.

Incorporating these modern approaches into AML risk assessment methodologies is essential for financial institutions to stay ahead of sophisticated financial criminals and comply with stringent regulatory requirements. By embracing dynamic risk assessment, leveraging advanced data analytics, and utilizing machine learning algorithms, institutions can enhance their ability to detect and mitigate money laundering risks in an increasingly complex and interconnected world.

Quantitative Risk Assessment

One of the key components of Risk Assessment is the quantitative risk assessment. This approach involves assigning numerical values to various risk factors and control measures, which are then used to calculate an institution’s overall AML risk score. The risk factors considered in the assessment include business sector risk, geographic risk, product/service delivery risk, customer risk, and other risk factors associated with money laundering and terrorist financing activities.

The quantitative risk assessment provides a standardized and objective framework for evaluating AML risk. By assigning numerical values to different risk factors, institutions can prioritize their efforts and allocate resources effectively. This approach allows organizations to focus on areas with the highest risk and implement targeted risk mitigation measures.

Evaluating Risk Factors

Several risk factors are evaluated to assess an institution’s AML risk profile. These risk factors include:

• Business Sector Risk : This factor considers the level of inherent risk associated with the specific industry or sector in which the institution operates. Some sectors, such as financial services or money service businesses, may inherently pose higher AML risks.
• Geographic Risk : Geographic risk evaluates the risk associated with the countries or regions in which an institution conducts business. Certain jurisdictions may have higher levels of money laundering or terrorist financing activities, requiring increased scrutiny.
• Product/Service Delivery Risk : This factor assesses the risk posed by the specific products or services offered by the institution. Some products or services, such as correspondent banking or cross-border transactions, may present higher AML risks.
• Customer Risk : Customer risk evaluates the risk posed by the institution’s customer base. Factors such as the type of customers, their geographic locations, and their transactional behavior are considered to assess the level of AML risk associated with the customers.

By evaluating these risk factors, institutions can gain a comprehensive understanding of their AML risk exposure and develop targeted risk mitigation strategies.

Regulatory Frameworks and AML Risk Assessment

In the world of AML compliance, regulatory frameworks play a crucial role in setting standards and ensuring effective risk assessment. Let’s explore the role of FINRA in risk assessment, the requirements for BSA/AML risk assessment, and the approach to AML risk assessment in Nigeria.

Role of FINRA in Risk Assessment

The Financial Industry Regulatory Authority (FINRA) is a regulatory organization that oversees brokerage firms and registered securities representatives in the United States. While FINRA’s primary focus is on market integrity and investor protection, it also plays a significant role in AML risk assessment within the financial industry.

FINRA provides various platforms and utilities tailored to different user groups, including member firms and industry professionals. These platforms, such as FINRA Gateway and FinPro, offer tools and resources to facilitate compliance tasks, access filings and requests, run reports, and manage industry CRD records. By leveraging these platforms, compliance professionals can effectively assess AML risks and ensure adherence to regulatory requirements.

BSA/AML Risk Assessment Requirements

The Bank Secrecy Act (BSA) and Anti-Money Laundering (AML) regulations require banks to establish a risk-based approach to AML compliance. One of the critical components of this approach is conducting a comprehensive BSA/AML risk assessment. Banks must identify and understand the money laundering, terrorist financing, and other illicit financial risks they face.

During the risk assessment process, banks should identify specific risk categories unique to their operations, such as products, services, customers, and geographic locations. This allows for a thorough analysis of risks associated with different aspects of banking activities ( BSA/AML Manual ). By evaluating transaction data and other relevant information, banks can develop appropriate internal controls to mitigate ML/TF and other illicit financial risks.

It’s important for banks to document their risk assessment in writing to effectively communicate the identified risks to appropriate personnel. While there is no specific requirement for continuous or periodic updates, risk assessments should be updated to reflect changes in product offerings, services, customer types, and geographic locations. This ensures that the risk assessment remains current and reflective of the bank’s AML and illicit financial activity risks.

AML Risk Assessment in Nigeria

In Nigeria, the Central Bank of Nigeria (CBN) published AML/CFT regulations in 2013, which introduced a robust AML framework for financial institutions. These regulations aim to strengthen AML compliance and mitigate risks effectively.

Under the CBN’s regulatory framework, financial institutions are required to implement comprehensive AML policies and procedures. This includes conducting thorough risk assessments to identify and manage ML/TF risks specific to their operations ( Tookitaki ). By understanding the unique risks they face, financial institutions can tailor their compliance efforts to address these risks effectively.

The AML risk assessment process in Nigeria involves assessing various factors, including the nature of the institution’s business, its customers, products, services, and geographic locations. By analyzing this information, financial institutions can develop risk-based controls and measures to prevent money laundering and terrorist financing activities.

By closely adhering to regulatory frameworks and conducting robust risk assessments, financial institutions can strengthen their AML compliance efforts and contribute to a more secure and transparent financial system.

In the next section, we will explore the importance of customer risk assessment and the pillars that support effective customer risk assessment in the context of AML compliance.

Customer Risk Assessment

A crucial component of effective AML compliance is conducting thorough customer risk assessment . By assessing the risks associated with each customer, financial institutions and businesses can implement appropriate anti-money laundering measures. In this section, we will explore the importance of customer risk assessment, the pillars of customer risk assessment, and the role of dynamic risk assessment in AML compliance.

Importance of Customer Risk Assessment

Customer risk assessment is a standardized technique used to determine the level of risk posed by a customer. It involves examining various factors, including the customer’s identity, source of funds, intended use, and behavior, to assess the risk of money laundering or financial crimes ( Source ). By identifying the risk associated with each customer, businesses can apply the appropriate level of due diligence and implement measures to mitigate potential risks.

A consistent customer risk assessment methodology is essential for firms. It should establish criteria for customer risk scoring and weighting mechanisms, providing a rationale for risk assessments. This allows for the identification of risks associated with business relationships or occasional transactions, with more complex interactions requiring a more rigorous assessment.

Financial institutions are mandated by regulatory frameworks to establish and maintain written policies and procedures for customer due diligence. The US Financial Crimes Enforcement Network’s (FinCEN) CDD Final Rule emphasizes the importance of thorough customer risk assessments in preventing money laundering and financial crimes ( ComplyAdvantage ).

Pillars of Customer Risk Assessment

There are four main pillars to consider in a customer risk assessment:

• Customer Identity Verification : Verifying the identity of customers is a crucial step in assessing risk. This involves collecting and verifying identification documents, conducting background checks, and ensuring compliance with know your customer (KYC) requirements.
• Customer Behavior Analysis : Analyzing customer behavior helps identify unusual or suspicious activities that may indicate potential money laundering or illicit financial transactions. Monitoring customer transactions and behavior patterns allows for the detection of red flags and the implementation of appropriate risk mitigation measures.
• Transaction Monitoring : Monitoring customer transactions in real-time is essential in detecting and preventing money laundering activities. Automated transaction monitoring systems can flag suspicious transactions based on predefined risk parameters and patterns.
• Geographical Risk Assessment : Assessing the risk associated with a customer’s geographical location can provide insights into potential money laundering risks. Countries or regions with higher levels of corruption, weak regulatory frameworks, or known money laundering activities may pose higher risks.

By considering these pillars, firms can gain a comprehensive understanding of the money laundering risks posed by each customer and implement effective AML measures to mitigate those risks ( ComplyAdvantage ).

Dynamic Risk Assessment in AML Compliance

To effectively mitigate money laundering risks, firms need to embrace dynamic AML customer risk assessments. Static risk assessments may not be sufficient in a rapidly evolving financial landscape. Dynamic risk assessment involves leveraging data and technology, adopting statistical analysis, and utilizing machine learning algorithms to enhance the quality of data and update customer profiles regularly based on behaviors and additional factors ( ComplyAdvantage ).

By continuously monitoring customer behavior and leveraging advanced analytics, financial institutions can identify and respond to emerging risks in a timely manner. Dynamic risk assessment allows for more accurate risk profiling, enabling businesses to allocate resources effectively and focus on high-risk customers.

Implementing machine learning algorithms can enhance the quality of data and assist in regularly updating client profiles for effective risk assessment. Statistical analysis can help identify red flags in customer behavior, enabling businesses to take appropriate measures to mitigate potential risks ( Source ).

By prioritizing customer risk assessment and implementing dynamic risk assessment methodologies, businesses can strengthen their AML compliance efforts, reduce the risk of money laundering, and contribute to a safer financial ecosystem.

Challenges in AML Risk Assessment

When it comes to AML risk assessment, financial institutions and organizations face several challenges that impact their ability to effectively detect and prevent money laundering activities. These challenges include the complexity of money laundering methods, lack of cooperation and information sharing, deficiency in data and technology resources, increased governance and compliance standards, and a shortage of skilled personnel.

Complexity of Money Laundering Methods

Money laundering methods are constantly evolving, making it challenging for financial institutions to keep up with the sophisticated techniques used by criminals. These methods may include the use of shell companies, offshore accounts, and digital currencies to obfuscate the source of funds. Criminals may also employ intricate transactions like layering and integration to evade detection.

Lack of Cooperation and Information Sharing

Cooperation and information sharing between financial institutions and regulatory authorities are crucial in combating money laundering. However, concerns about legal liability and reputational risks can hinder effective information sharing. Additionally, regulatory authorities may lack the capacity to analyze the data provided by financial institutions. Initiatives, such as those implemented by the European Banking Authority (EBA) in 2022, aim to enhance collaboration among stakeholders for improved AML efforts ( Sanction Scanner ).

Deficiency in Data and Technology Resources

Financial institutions often face challenges related to the deficiency in data and technology resources when it comes to AML risk assessment. Inadequate access to customer data, analytics tools, or compliance teams can hinder the detection and prevention of money laundering activities. This gap is particularly evident in smaller to medium-sized companies that may also face resource constraints for outsourcing solutions.

Increased Governance and Compliance Standards

The ever-increasing governance and multi-jurisdictional compliance standards pose significant challenges for banks and financial institutions. Compliance with diverse AML regulations across jurisdictions is a complex task. The rise in customer diligence requirements demands institutions to gather more information on customers and beneficial owners, potentially becoming resource-intensive ( Sanction Scanner ).

Shortage of Skilled Personnel

A shortage of skilled personnel is a critical challenge in AML compliance. Finding proficient professionals in the field can be arduous due to high demand and a shortage of qualified candidates. The onboarding expenses, training needs, and dealing with high turnover rates further compound this challenge for organizations. Continuous efforts are required to recruit, train, and retain competent AML professionals ( Sanction Scanner ).

In order to overcome these challenges, financial institutions and organizations must invest in advanced technologies, enhance collaboration and information sharing mechanisms, improve data and technology resources, stay updated with regulatory requirements, and prioritize training and development programs for their AML professionals. By addressing these challenges, organizations can strengthen their AML risk assessment methodologies and effectively combat money laundering activities.

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Understanding Money Laundering and Terrorist Financing

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• Managing a compliant and operationally efficient Customer Risk Assessment

The key challenges and enhancements to support the future of Customer Risk Assessment

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• Andrew Gill , Senior Manager |
• Adam Jack , Assistant Manager |

Worldwide, regulatory requirements and the macro-environment are ever-evolving and so too is the need to adopt rigorous controls to monitor the risks that organisations are exposed to.

The Money Laundering and Terrorist Financing (Amendment) Regulations 2019 reinforced the need for a risk-based approach, putting focus on understanding risk exposure and proportionate controls to mitigate this risk. Fundamental to this is the application and maintenance of a Customer Risk Assessment (CRA) methodology.

In LexisNexis’ 2023 report on the True Cost of Compliance , it is estimated that financial crime compliance costs for the UK will rise to over £30 billion and with developments to the macro-environment such as the Russia-Ukrainian war, costs to firms are consistently increasing with no clear end in sight.

Key Challenges

Implementing and maintaining an effective CRA methodology is key to remaining compliant in a cost-effective way. However, the CRA has several interwoven dependencies which need to be managed to achieve effective compliance. We outline below some of the familiar barriers that impact the application of a risk-based Anti-Money Laundering (AML) compliance program.

1. Evolving Regulation and Risk Maintenance 

Understanding and managing risk is a key component of CRA, but ensuring continued effectiveness requires ongoing maintenance particularly when macro-economic and sector specific developments continue to impact risk exposure. When implemented and maintained appropriately, the CRA supports a risk-based approach, allowing for application of controls proportionate to customer risk.

The ongoing maintenance of the CRA methodology continues to be a challenge for many firms without the necessary expertise and processes in place to effectively monitor changes to risk. This significantly hampers the implementation of the CRA methodology and is further compounded by the ever-evolving regulatory and macro landscape, such as the fast evolving sanctions landscape, the UK Government’s navigation Brexit and the Economic Crime and Corporate Transparency Bill .

2. Data Quality and Completeness 

In our article last month, focusing on the challenges and operational efficiencies in Customer Due Diligence (CDD), we detailed the importance of data quality within the overall customer lifecycle. Data quality and completeness is a key challenge that hampers many organisations in the application/implementation of a CRA.

Inaccurate or incomplete data can impact the CRA through erroneous records and the need for default value application. This is a common challenge for many firms, with legacy systems, data issues as well as incomplete CDD policies having a downstream effect on risk assessment.

This leads to implications on complementary controls, such as the application of electronic Know Your Customer (eKYC) and risk-based customer monitoring. A failure to address critical data quality issues can compromise the effectiveness of controls and undermine the overall risk assessment process.

3. Ineffective Processes and Customer Journey Inefficiency 

Policy or procedural ambiguity can result in operational inefficiency and has a downstream impact on risk assessment quality. It is essential to establish clear and consistent processes to support effective CRA.

The frequency and requirements of CDD Review cycles should be tailored to the risk presented by customers to ensure that the customer journey is managed effectively. This not only prevents the need for unnecessary customer outreach, but also ensures AML risk is monitored appropriately.

However, despite this, many organisations struggle defining and maintaining effective processes, continuing to apply those that don’t align with the inherent customer risk and failing to support the ongoing requirements for CRA.

4. Tuning & Calibration Processes

Model tuning is an essential driving force behind ensuring the CRA model accurately reflects customer risk. It’s essential to have a well-governed, risk-based tuning process to gain confidence that the model outcome is appropriate and capable of supporting AML controls.

Definition of an appropriate tuning methodology continues to be a common issue, with many organisations struggling to apply a risk-based and evidenced approach to tuning. In particular, model over-fitting is common, with many CRA models tuned on too small a customer sample with limited understanding of model performance across the wider customer-base.

5. Implementation & Integration

The CRA sits at the centre of the financial crime control framework, enabling the application of monitoring in a risk-based manner. This allows for a greater focus of controls and resourcing on higher risk customers and supports informed decision-making in areas such as customer onboarding, customer segmentation, Transaction Monitoring (TM) and ongoing monitoring.

Whilst it’s common to apply targeted periodic review cycles, many organisations struggle with the integration of CRA more widely within the financial crime control framework, with some of the common challenges including:

• Applying tailored controls based on risk level, including applying more stringent thresholds to higher risk customers.
• Implementing a feedback loop between the CRA and relevant controls, required to trigger changes following CRA reviews and trigger events.
• Tailoring ongoing monitoring and data requirements based on risk-exposure.

Enhancing the Operation

Given the wide range of challenges faced by financial services firms and the rising costs associated with compliance, it’s more important than ever to have the tools in place to remain operationally efficient.

We detail below four key operational enhancements capable of driving a more sustainable approach to compliance.

1. Target Operating Model (TOM) Development and Enhancement

A well-defined and governed CDD TOM is central to ensuring there is an effective strategy and approach to CDD/CRA. A well-defined TOM should provide the baseline for appropriate CDD, with a range of benefits, including:

• Providing a platform for completion and maintenance of a Business-Wide Risk Assessment (BWRA), both a key component and beneficiary of an effective CRA.
• Applying robust procedures for review and maintenance of CDD/CRA risk alignment.
• Risk-aligned policies and procedures catalysing CDD updates on either a periodic basis, or through trigger-based review.
• Implementation of risk-centric controls. For instance, applying more stringent TM thresholds to higher risk customers (a topic we’ll explore in more detail in an upcoming article on our TM Alert Classifier).

2. Risk-based Design and Tuning

Without aligning the design and implementation of a CRA methodology with risk exposure it’s almost impossible to get comfort that customer risk level is accurately set. A TOM provides a platform through the definition of a BWRA and procedures to regularly maintain the CRA, but this is only half of the challenge.

It’s important to remember that there is no ‘one size fits all’ approach to CRA. Each firm is exposed to bespoke risks and, as a result, risk factors and scoring methodology should be defined with that in mind. An appropriate risk-based tuning methodology should be applied to ensure model parameters are set at the necessary levels.

Ensuring there’s a comprehensive approach to defining and maintaining the risk assessment and scoring methodology which accounts for continual developments in risk is vital.

3. Governance Enhancements 

The importance of a comprehensive governance structure is often overlooked. However, given the potential cost implications of mis-performing CRA models, having a robust control structure is a key component of success.

Like many of the enhancements, this is only a piece of the CRA puzzle, but without it, firms struggle to ensure that the necessary steps have been taken to validate the CRA and subsequent iterations. A comprehensive governance structure should provide the controls surrounding CRA definition, design, and implementation, including the oversight and sign-off procedures required to support maintenance of the model.

4. Outcome Testing

Testing is key to ensuring the CRA model performs in line with expectations. Inefficient/ineffective CRA models have the potential to significantly impact resourcing and risk exposure leading to increased staff costs, potential fines, and the possibility of remediation.

Many firms struggle to gain comfort in the risk ratings and the level of controls/resourcing required. Outcome testing serves to provide comfort, assessing the range of model performance with a particular focus on:

• Effectiveness – Assessing how effectively a model identifies higher risk customers. Model ineffectiveness exposes firms by under-classifying customer risk level.
• Efficiency – Identifying how accurately a model classifies lower risk customers. Model inefficiency drives increased costs with unnecessary controls applied to customers incorrectly classified as high-risk.

Future of CRA compliance

*Naturally, we’re already seeing a move towards the ‘future’ of CRA. As firms begin to better understand risk exposure and build improved controls, enhancements are being made to the way customer risk is defined and the complexity of models used.

As with many other areas of AML, one of the biggest shifts is the move to more data driven, automated approaches to defining customer risk. With this, many firms are investigating the use of network analysis, machine learning and dynamic risk assessment, amongst other techniques to better understand their customers and increase the accuracy and comfort in the way risk is assessed.

With technology ever evolving, the changes we’re seeing to risk assessment are set to continue, but are hindered by the challenges, outlined above, that many firms face. Without first countering these and implementing solid foundations, progress will be limited.

For information on how we continue to help clients better understand risk exposure and apply both effective and efficient financial crime controls, please contact Adam Jack or  Andrew Gill  

Andrew Gill

Senior Manager, FS Forensics

Assistant Manager, KPMG in the UK

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1 june 2024 changes to aml/cft regulations to impact all reporting entities.

• 1 June 2024 changes to AML CFT regulations to impact all reporting entities

All reporting entities should be ready for the significant changes to regulations under the Anti-Money Laundering and Counter-Financing of Terrorism Act 2009 (the ‘Act’) taking effect on 1 June 2024. 

The changes comprise the second stage of regulatory amendments (Stage 2) resulting from the Ministry of Justice (MoJ) review of the Act, released in November 2022 (MoJ Report). We have previously  discussed in more detail the wide-ranging nature of the Stage 2 amendments and the first stage of regulatory amendments, which came into effect on 31 July 2023.

The MoJ Report recommended, both in the short and long term, regulatory amendments to enable relief for businesses and where appropriate, clarification of their existing AML/CFT obligations. However, the practical reality of the Stage 2 changes is overall they impose additional obligations on reporting entities. Whether this is an unintended consequence or a necessary prelude to regulatory relief down the line is yet to be seen. But for now, reporting entities should be aware of what is changing under Stage 2, and in particular, how some of these changes will impact customer due diligence (CDD) requirements and AML/CFT policy and programme documents.

Updated customer due diligence requirements

The AML supervisors have recently released the below guidelines (all are updated versions of previously released guidelines, other than the new Limited Partnerships Guideline):

• Customer Due Diligence: Companies Guideline 
• Customer Due Diligence: Trusts Guideline 
• Customer Due Diligence: Limited Partnerships Guideline
• Enhanced Customer Due Diligence Guideline 
• Beneficial Ownership Guideline

The guidelines are essential reading for reporting entities, with the following key points to be aware of:

Standard CDD expanded – legal structure

From 1 June 2024, reporting entities must obtain and, according to risk level, verify, information relating to a customer’s: 

• legal form and proof of existence;
• ownership and control structure; 
• powers that bind and regulate the customer;
• if a limited partnership, the existence and name of any nominee general partners; and
• if a company, the existence and name of any nominee directors or nominee shareholders.

The supervisors’ view is the above requirements will help reporting entities accurately identify the beneficial owners and in turn, the level of risk associated with the customer. Although each of the above requirements are set out separately, the supervisor guidance states the requirements can be read in combination with each other as there are likely to be overlapping elements across the various limbs. For example, identifying the powers that bind and regulate a company is likely to help with understanding the company’s ownership and control structure. 

Additionally, information should be obtained to show how each relevant person meets the Act’s definition of ‘beneficial owner’ and whether this is through ownership and/or effective control. By way of example, for a company, this could involve considering information in respect of the identity of any directors, shareholders, capital contribution, shareholding interests, distribution rights, voting rights and/or powers that may be obtained from the company’s constitution or shareholders’ agreement (if any). This potentially involves a more detailed consideration of these documents to sufficiently meet this requirement. 

To determine the existence of nominee directors or shareholders or nominee general partners, the onboarding process for customers may need to change to ensure this issue is covered. This could mean adding direct questions to specifically address this requirement during the onboarding process.

The Stage 2 changes also confirm reporting entities are obligated to undertake ongoing CDD and account monitoring under s31 of the Act depending on the level of risk involved. In determining the extent of this obligation, the reporting entity must consider when CDD was last conducted in relation to the customer and the adequacy of the CDD information held.

Verification

Reporting entities should already have policies, procedures, and controls (PPCs) in place to determine the level of risk and how this impacts the level of verification needed. The updated guidance provides a few useful clarifications on how verification may be actioned for the new CDD requirements:

• For verification of the existence of a nominee director or shareholder or nominee general partner, verification may be done by using information from a reliable source (it does not need to be independent).
• For verification of legal form and proof of existence, ownership and control structure and any powers that bind and regulate a customer, the verification must be from a reliable source. Again, this does not necessarily mean the source needs to be independent (except if verifying settlors or protectors of a trust), and in all cases, should be according to the level of risk involved.
• In the case of a trust, for verification of legal form and proof of existence, ownership and control structure and any powers that bind and regulate it, it may be possible to rely on written confirmation from a professional acting for a trust (such an accountant or lawyer) which sets out the verification information required. Note that this is only referenced in the guidance and relates to the verification aspect of CDD only – this is not an expansion of sections 33-35 of the Act regarding reliance by reporting entities on agents or third parties.

Enhanced CDD

The new regulations expand the scope of enhanced CDD (ECDD) by requiring it to be conducted for existing customers, or customers conducting an occasional transaction or activity, where there are grounds to report suspicious activity as described in the Act. The reporting entity must conduct ECDD as soon as practicable after becoming aware that it must make a suspicious activity report.

The Stage 2 amended regulations also set out the below four additional (but non-exhaustive) ECDD measures:

• obtaining further information from the customer in relation to a transaction.
• examining the purpose of a transaction.
• enhanced monitoring of a business relationship.
• obtaining senior management approval for transactions or to continue the business relationship.

The above must be carried out before establishing a business relationship with a customer requiring ECDD, and during a business relationship should the need for ECDD arise, and will be necessary where information regarding the source of wealth and/or source of funds may not be sufficient to manage the AML/CFT risk.

Prescribed requirements for risk assessments and compliance programmes

Every reporting entity should review and update its risk assessment and compliance programmes to reflect the amendments and the new guidelines. Key action points (this is not an exhaustive list of the Stage 2 changes) for reporting entities relate to:

• Risk assessment:  must take into account new or developing technologies, products, or mechanisms.
• the source of the funds or the source of the wealth of a customer; and
• both the source of the funds and the source of the wealth of the customer.
• for any reporting entity who may be an intermediary or beneficiary institution for an international wire transfer, its compliance programme must set out adequate and effective PPCs for the reasonable steps it will take if the international wire transfer does not contain the originator or beneficiary information required under the Act, and what risk-based policy or process will apply if an international wire transfer does not have that information.
• any functions carried out by an agent of the reporting entity as part of the programme;
• vetting agents who carry out functions of the reporting entity;
• training agents of the reporting entity on AML/CFT matters; and
• maintaining a list of agents of the reporting entity acting in the AML/CFT programme.

There are also other changes that will require operational attention to ensure compliance, such as the new requirement to keep records of prescribed transaction reports for at least five years after the end of the business relationship with the customer and around transactional matters, such as the minimum information needed for international wire transfers.

Stage 2 means more than a review of CDD and AML/CFT programme documentation

In this article, we have mainly focused on the impact of the Stage 2 changes in respect of CDD and AML/CFT programme documents. These two areas represent only a couple of aspects of the Stage 2 changes and are not, and should not, be treated as giving a full picture of the changes. The scope of the Stage 2 changes are broad and varied, and reporting entities will need to assess these and how they may impact their current AML/CFT PPCs to maintain compliance.

In releasing the new and updated CDD and ECDD guidance, the supervisors communicated that their approach to the Stage 2 changes will be “broadly educative and constructive.” This approach is both welcome and practical, given there will be a period of bedding in needed and the release of the guidelines is relatively recent. However, reporting entities should not use this approach to delay acting on or addressing the Stage 2 changes - it is likely to only be an initial approach by the supervisors and will not last indefinitely.

Ongoing reform continues

Beyond the Stage 2 changes, the AML/CFT regime continues to evolve:

• The third and final stage of the current amendment regulations comes into effect on 1 June 2025. This third stage is fairly limited in scope and mainly introduces obligations on sectors (such as internet auction providers) that were not previously subject to AML/CFT obligations and a requirement give and record a risk rating to new customers.
• The MoJ is still working on implementing the twelve proposed changes featured in the initial exposure draft AML/CFT amendment regulations, but which were not included in the finalised amendment regulations. These amendments need to be effected by way of an amendment to the Act itself. Among others, these proposals include relaxing the requirement to verify an address for standard CDD and extensions to prescribed transaction report reporting times, both of which will be very welcome in easing the compliance burden. At the time of writing, there has been no firm indication by the MoJ as to when these amendments will be advanced, but in our view, these cannot come too soon.
• In addition to the above, more substantive reform to the Act itself to address further recommendations in the MoJ Report remains on the government’s agenda. Associate Minister of Justice Nicole McKee has recently commented the AML/CFT reform work is one of her priorities for this parliamentary term, but the scope of the reform work continues to be discussed within government. According to the CoFR regulatory initiatives calendar for Q1 2024 , a new AML/CFT bill is planned to be introduced in the second quarter of 2026.

Navigating the Act and the regulations to distil what is practically required of reporting entities remains extremely challenging. Both some immediate relief to many from the overly prescriptive compliance requirements and an overhaul of the Act and its regulations remain both overdue and urgently needed. The focus of further reform must be to ensure that the regime delivers on the ‘risk-based approach’ recommended by the MoJ. The Stage 2 changes fail to deliver any of the relief that had been hoped for from that recommendation.

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