case study examples banking

Investment Banking Case Study Examples – A Guide

If you are preparing for an investment banking interview, you’ll probably need to conquer a case study interview. because case studies are a very crucial component in the investment banking hiring process. particularly if you have never completed a case study before, that will be very challenging for you to get into the investment banking field. this article has covered everything you need to know about investment banking and potential investment banking case studies. there are also tips and practice investment banking case study questions with examples of how to resolve them..

What is Investment Banking?

Investment banks are financial firms that perform a variety of tasks, including underwriting, assisting companies with the issuance of stock and debt securities through initial public offerings or fixed-priced offerings enabling mergers and acquisitions on both the buy side and sell side of the deal, corporate restructuring and many other tasks. 

To efficiently complete these significant deals, a firm turns into an investment banker when it requires finance services. With some of the best benefits in the businesses, it is an extremely competitive industry.

How Does Investment Banking Work?

Investment banking offers services and serves as the middleman between businesses and investors and focuses mostly on shares and stock exchanges. 

Investment banking services help big businesses and organizations in developing a successful investment strategy that includes accurate financial instrument valuation.

When a company conducts an IPO or initial public offering, an investment bank purchases the majority of the shares immediately on the firm’s behalf.

The investment bank, which is now serving as a stand-in for the company then sells these shares on the market. The investment bank improves the company’s revenue in this way while also making sure that all governing rules are observed.

The investment bank makes money by marking up the initial price of shares when selling them to investors, helping the organization in making the most profit possible from this activity.

If a circumstance in the market emerges where the stock becomes overpriced, the investment bank also runs the risk of losing money by selling the stock at a lower price. 

An organization should assess its requirements and carefully consider all of its possibilities before seeking guidance from an investor banker. Before the company visits an investment bank, there are a few crucial considerations including the amount of capital being raised and the level of market competition. When the business has clarity in these areas, it can enlist the assistance of investment bankers to find new businesses to invest in.

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Benefits of Investment Banking

Investment banking assists big businesses in a variety of ways to make crucial financial decisions and make sure they maximize revenues. That’s the reason, Investment banks are a prevalent financial institution among these businesses and even governments.

Here Are Some of the Advantages of Investment Banking:

• Investment banks effectively manage their client and provide them with the information they require regarding the advantages and disadvantages of investing their money in other businesses or organizations.
• These banks serve as a bridge between the company and the investor, ensuring a rise in financial capital by helping in major financial transactions like mergers and acquisitions.
• It conducts an in-depth analysis of the deal and project that will be undertaken by its customer to ensure that the client’s money is invested safely and helps to reduce the risks involved with the mentioned deal or project.

What is an Investment Banking Case Study?

You must have solved case studies during your investment banking training. 

Analyzing a business condition is done in case studies during investment banking interviews.

You would be provided with all the necessary data and have adequate time to examine broad case studies. There you would be asked for your opinion on business-related issues.

Your Task Includes,

• Make the necessary deduction.
• Investigate the matter, which is typically a client’s business.
• Give suggestions for resolving the current issue along with an explanation.

Investment banking case studies are frequently used to evaluate a job candidate’s potential performance in real circumstances, where your interviewers would give you a problem and ask for a detailed recommendation.

By presenting them with a hypothetical scenario similar to those experiences while working in the field, your job is simply to analyze the scenario and give them justified reasons. 

Case studies are typically presented at the end of the application process, most frequently at the final interview or during the assessment center.

The majority of questions in investment banking case studies revolve around acquisition, capital raising, or business growth.

What Are the Types of Case Studies?

Take home investment banking case study.

• You will probably receive the case in advance so you have more time to work on it before the assessment day.
• In the case of take-home case studies, you are given a few days to work on them, complete your analysis, and showcase your recommendation to the bankers over a 30-45-minute presentation.
• It involves a much deeper analysis including merger/LBO modeling, company procedures, and valuation.

On the Spot or Blind Investment Banking Case Study

• On the day of your assessment center, the case can be presented to you blindly with little time for preparation.
• These are given to you on the day of your interview and within an hour or two you are supposed to present it on the spot. 
• The time split for this process would usually be 45-60 minutes of preparation, 10 minutes of presentation followed by a round of question and answer.
• It would not involve such deep study.
• Some case studies on investment banking may occasionally be given as a group task, where the employer will use this as an opportunity to examine the candidate’s analytical skills and teamwork qualities.

Why You Should Prepare for an Investment Banking Case Study?

The theory behind these case studies is that because the qualification for various professions varies, bankers don’t trust the conventional method of interviewing applicants.

Case studies are preferred by banking recruiters as a better way to evaluate applicants because they show how you should perform in the workplace. 

You don’t need to worry about whether your response is right or wrong in this situation because the interviewer is more interested in how the candidate thinks and how well they can use logic and analysis to come up with an innovative answer to the challenge at that time.

Investment banking case study writers aim to inspire applicants to come up with their ideas and apply critical thinking.

Candidates for these positions must have a variety of skills, but problem-solving ability is one of the most important. 

Recruiters are interested in learning how you would approach difficult circumstances and use your intelligence, education, and professional experience to handle them successfully.

Additionally, candidates get an amazing chance to practice their other abilities including presentation, communication, and interpersonal skills.

These factors make case studies significantly more important than the other methods of evaluating applicants in the investment banking hiring process.

How to Prepare for Case Studies Before Assessment Day?

• Read as much deal news as you can while preparing and going through the daily market and business news in popular publications.
• Discover the many valuation methods, how they are calculated, and how they are evaluated then try out your calculations after watching YouTube videos or reading information on valuation methods.
• You must prepare a structure using PowerPoint and Excel consistently, especially for modeling and valuation-based case studies.
• Also, improve your familiarity with software like Microsoft Excel so that you can use spreadsheets effectively.
• You should practice the kinds of questions you might get during your presentation. 
• Real case study interview questions used by banks might not be available to you.
• But, knowing that you need to practice, consider contacting a colleague or friend, or mentor you know who has gone through case study rounds for the types of questions they were asked.

How to Solve It and Perform Well During Assessment Day?

• To solve the case study, take an organized strategy.
• Before making a conclusion or deciding how to solve the problem, carefully analyze the case and the questions.
• Professionally prepare Excel and PowerPoint while modeling case studies.
• Every assentation you make should be supported by solid logical arguments, and the first few points should address that case’s most important issues.
• Even if is not necessary, it would be advantageous to have a specialized understanding of the industry being studied.
• Do not beat around the bush as you have limited time and hence be precise as you speak.

Investment Banking Case Study Examples and Answers

The decision-making case and the financial modeling case are two main types of case studies used in investment banking assessments.

Modeling – Investment Banking Case Study

Modeling case studies are typically take-home tasks that require you to perform straightforward valuation and financial modeling.

So rather than being a case study, it is more of a modeling exam.

The investment banker gives an overview of creating models as well as developing a variety of methods for an in-depth and useful understanding of the subject.

The modeling case study will either use a simpler merger or leveraged buyout model or a free cash flow to the business valuation. 

To assess whether the firms are overvalued or undervalued, you would be asked to examine their valuation multiples.

In most cases, you will be given a few days to finish your analysis. Then on the day of the interview, you must spend 30-45 minutes presenting your case to the bankers. 

Because you will have more time to work on it, the analysis will be considerably more in-depth than in a client case or decision-making case study.

Evaluating Strategic Alternative: Case Study 1 

To maximize shareholder value, a magazine publisher is deciding whether to sell, grow organically or make tiny “tuck-in” acquisitions. It is looking for an investment bank to assist it with its alternatives and has asked for a presentation from your company.

Given Materials: 

They would provide you with a firm summary with financial statements and five-year forecasts, a ten-page market analysis with main competitors, minor acquisition candidates, and recent transactions.

• First, go through everything to get a sense of the industry, where it’s going, and how much this firm is worth in comparison.
• Complete a quick assessment using publicly available rivals prior transactions and a DCF.
• Evaluate the figures provided by the value, the company’s potential for organic growth, and the availability of suitable targets for acquisition.

Decide what to do, in most cases it is advisable to say “Sell” unless the industry is expanding rapidly (Above 10% annually) the company is completely undervalued, or these are acquisition candidates that will increase revenue or profit by at least 20-30%.

If you are analyzing scenarios like this during a 30-minute presentation, choose 10 slides with 3-4 important themes each and attempt to spend 3-4 minutes on each slide.

If you choose to write “Sell the company”, consider the following steps in preparing a presentation:

• List the three main reasons for recommending selling
• Overview of the industry- Is it expanding? Falling off? Or Being Inactive?
• Position of the company in the industry? Leader or Second level position? Or is it strong or weak?
• What would organic growth look like in five to ten years? How much larger or more valuable would the company be?
• Prospective tuck-in acquisition candidates
• Why organic growth and acquisition are not the answers.
• Why selling now will generate the most shareholder value
• Show prior transactions and public comparable valuations
• Display the DCF output and the sensitivity chart valuation
• Summary- State again that the best course of action is to sell your company right away and that neither organic development nor the acquisition of smaller firms would increase your company’s valuation in five to ten years.

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Decision Making- Investment Banking Case Study

Case studies that include decision-making are more common than case studies that involve modeling.

In this kind of case study, the applicant is required to decide for their client and offer advice.

The client case study can center on locating financial sources or determining whether or not a proposed merger should go forward.

At the interview, you should be prepared for these questions. Because you will have a set amount of time in which to examine and present the case. You will be given a total of 45-60 minutes to prepare and beforehand 10 minutes presentation with a Q&A round.

Case Study 1

A customer owns her company fully and wants to release some liquidity while keeping a stake in it (Worth £400 million) what suggestions would you provide the client to get the best possible price?

Given Materials:

A corporate overview and details about the company’s performance over the last three years are provided.

Examine all financial information thoroughly and forecast the company’s organic development.

Consider the breakdown of the present valuation if you are provided with the relevant facts.

Think about the client’s industry and the expected trends for that market.

• How does the valuation stack up against others in the field?
• Is the current valuation backed up by reliable industry forecasts?
• Given the slow development of the industry, would it be wise to give up more equity?
• Is it expected that this industry will keep growing?

Consider present customer portfolios, projects, etc., while deciding whether any actions could be performed to boost the company’s value.

Think about suggestions for the client’s negotiation strategy:

• How much equity should they be prepared to give up?
• What number should the client choose as their actual reserve price, in your opinion?

Case Study 2

A publicly traded firm contacts you in the hope to raise money. Analysts’ expectations were met by recent profits and the latest financial report, but the company’s market values are lowest throughout the year. The management of the company has developed a project that it hopes would significantly boost EBIDTA and is looking to raise funding for it. What should the business do to raise the required capital?

Given material:

A summary of the business and its financial statements will be provided to you to prepare for this question.

You must think about whether the organization should raise debt or stock.

Think about the market capitalization, share count, and share price:

• How would the company be affected in this environment if it issued fresh shares?
• In terms of dilution of ownership, would equity financing be an appropriate option?
• How would the effect currently differ from what it would be if the share price were back to normal?
• Would increasing debt be a better course of action if they are actually under management’s predictions?
• How much they could possibly raise?
• What potential problems could a debt increase bring about?
• How could the cost of interest be reduced?

Prepare your presentation by organizing your ideas clearly and go through your questions and thought process to get at your recommendation.

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Potential Acquisition: Case Study 3 

A software company is considering a large acquisition. It has chosen the company it wishes to acquire and has contacted a number of investment banks to obtain their thoughts on the transaction and how much they should pay. Based on these presentation, it will choose an advisor and decide what to do.

Two page summaries of the buyers and seller, each containing financial data as well as statistics and multiples for similar organization.

With a recommendation on whether to move forward with the acquisition and if so, how much to pay for the target, create five minute presentation.

For the very first, you should consider this two question to solve this,

• Should they purchase that target business?
• What price should they want for the target business?

For an example,

Let’s assume that the comparable companies are trading at EBITDA multiples that range from 4 to 8 times, with the median at 6 times and the 75th percentile at 7 times, respectively. You choose the 25th to 75th percentile range of 5x-7x and apply it to the target company’s $10 million EBITDA since the target company’s profit margins and revenue growth are comparable.

If you have access to a computer, you can also design a DSF, but if you are short of time, keep it straightforward and use multiples.

To answer the question “Should they buy?” take note of the following:

• Will the buyer be able to purchase the seller with enough cash, debt, or stock issuances?
• Will the vendor increase the buyer’s revenue and profit?
• Will the buyer benefit from new consumers, new goods, new markets, or other kinds of benefits as a result of the seller’s acquisition?

After concluding these, you can complete your presentation.

Investment Banking Case Study: FAQs

Q. what is an investment banking case study in short terms.

By presenting candidates with a hypothetical scenario that is comparable to those they might face on the job, investment banking case studies are frequently used to evaluate how the candidate would function in real circumstances.

Q. Which skills are tested in investment banking case study interviews?

Candidates’ analytical and financial skills as well as problem-solving, presentation skills, critical thinking, and interpersonal skills are tested during investment banking interviews.

Q. Is there any way to practice investment banking case studies?

There are various tools, financial modeling online courses, and investment banking textbooks accessible to practice investment banking case studies. Additionally, there are certain career services offered at universities and institutions that provide investment banking programs with case studies.

Investment Banking Case Study: Conclusion

The opportunities to demonstrate your abilities and expertise to investment bankers are provided by investment banking case studies, which are a crucial component of an interview process. 

We have covered some of the investment banking case study examples that will help you in preparation for an investment banking interview.

No doubt it is a very competitive yet tough field to break into but we hope, through this article you achieve the success ladder in the investment banking industry.

Author: Swati Varli

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Case Study: Will a Bank’s New Technology Help or Hurt Morale?

• Leonard A. Schlesinger

A CEO weighs the growth benefits of AI against the downsides of impersonal decision making.

Beth Daniels, the CEO of Michigan’s Vanir Bancorp, sat silent as her chief human resources officer and chief financial officer traded jabs. The trio had founded their community bank three years earlier with the mission of serving small-business owners, particularly those on the lower end of the credit spectrum. After getting a start-up off the ground in a mature, heavily regulated industry, they were a tight-knit, battle-tested team. But the current meeting was turning into a civil war.

• Leonard A. Schlesinger is the Baker Foundation Professor at Harvard Business School, where he serves as chair of its practice-based faculty.

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CASE STUDY BBVA

Banking on happy customers

BBVA worked with Accenture to become one of the world’s most customer-centric banks. Using digital technology to reach people where they are has helped them see massive growth.

3-MINUTE READ

Since its founding in 1857, BBVA has sought to stay ahead of the times with forward-thinking innovations. In recent years, they’ve focused heavily on investments in cloud, data and AI to create a data-driven, engaging and differentiated customer experience.

This approach has led to significant growth in new customers and revenue, as well as improved efficiency and profitability. The company realized a staggering 117% growth in new customers in the last few years and posted a profit of €8.02B in 2023, the highest earnings in its history.

As part of its ongoing reinvention journey, BBVA partnered with Accenture to develop a new, comprehensive digital sales model. The result? Nearly 50 million customers now interact with the bank through digital channels, and seven out of 10 sales are made digitally. BBVA’s client onboarding process takes just minutes (versus a few days at most other banks), using AI-based facial recognition and text analytics to verify account applicants via mobile app and real-time connections to external data sources to detect fraud.

A wealth of customer insights

These results would not have been possible had the bank not consistently invested in its digital core, harnessing the power of cloud, data and AI to facilitate the rapid development of new capabilities and insights. For example, bank-wide data, predictive analytics and business intelligence deliver a holistic view of the current and lifetime profitability—and likely behavior—of every customer. BBVA is also using Amazon Web Services to create a new global data platform to provide all business units with a unified view of their data and access to more efficient data processing, analysis and insights.

By combining first-party data with new data sources to deliver a step-by-step view of the customer journey, BBVA’s new digital sales model helps the bank prioritize sales initiatives for new customers and cross-sell to existing customers. The new model incorporates strategy and planning, paid media, search engine optimization, marketing automation, analytics, and content production for BBVA’s digital channels to reach individuals in hyper-personalized ways.

Providing a differentiated, better experience was like discovering a pot of gold.

David Puente / Global Head of Client Solutions, BBVA

The pay off

BBVA’s reinvented sales model is having a massive, positive impact—including a cost-to-income ratio reduction of 41.7%, one of the best among European banks. It’s also helping the bank expand its footprint in specific countries and regions. For instance, in Italy, BBVA opened in new markets with a full digital value proposition that inspired 130,000 new customers to join in the first year alone—a figure which has risen to more than 450,000 in the time since.

Today, with the tools and talent to support continuous reinvention, BBVA can continue to reimagine what’s possible, driving breakthrough experiences that attract and delight customers and add real value.

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BBVA's reinvention

In our built for change podcast, bbva's david puente shares how the bank is succeeding with continuous reinvention., we are making bold moves, together.

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Together, we turned finance operations from transactional to transformational. Moving BT Group’s finance function to the cloud boosted operational cost efficiency by 30%.

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Car brand smart wanted to replace traditional auto sales with a direct-to-consumer experience. We brought a new platform to life—and sold out an entire line of cars in 24 hours.

We’re working with The Good Food Institute and Food System Innovations to reinvent the meat alternatives industry and bring new alternative proteins into the mainstream.

Together, we’re reinventing the places where we work, live and play. Johnson Controls and Accenture are making buildings smarter and greener using the OpenBlue platform.

Changi Airport has been ranked the World's Best Airport twelve times. To take the traveler experience to new heights, we created ChangiVerse, an immersive metaverse experience.

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Digital transformation in banking: A complete guide

August 02, 2023

Digital transformation is a challenge for the banking industry, but it is necessary to adapt to the modern world where customers expect fast, efficient, and convenient services. Traditional approaches no longer meet the needs of the modern consumer. So, banks that want to remain competitive must abandon conservative methods and fully immerse themselves in the process of digital transformation.

This article discusses what is digital transformation in banking, key factors driving digital transformation , and successful examples of digital transformation in the banking industry.

What is digital transformation in banking?

5 key factors driving digital transformation in banking, technologies that drive digital transformation in banking, successful examples of digital transformation in the banking industry, how can soloway tech help you digitally transform your business.

Digital transformation in banking refers to applying new digital technologies and strategies to change and improve banking operations. This includes various changes to increase efficiency, meet customer needs, improve operational effectiveness, and develop new digital products and services.

Mobile applications and personal cabinets on the website are vivid examples of banks’ digital transformation. It is enough to press the buttons on a smartphone or computer to open an account, take out a loan, or order a new plastic card. The services are available not only to individuals but also to legal entities. The accounting departments use client-bank programs to transfer salaries to employees, pay taxes, and receive money from customers.

When you call the hotline of your financial organization, you are answered not by a specialist but by a robot. Virtual assistants have replaced some employees. Moreover, some US banks operate without branches at all. There are employees only in the head office, and customer transactions are conducted exclusively through the Internet.

It is more convenient for people to work with banks remotely, so credit institutions invest a lot of money in digital transformation. It is important that the interface of applications is user-friendly and understandable and transactions are fast. This will attract more customers and, accordingly, increase the profits of the financial company.

Pros and cons of digital transformation in banking

Digital transformation in banking is developing at a rapid pace. It has objective advantages: 

• Services of financial organizations are available from anywhere in the world
• The cost of remote operations is cheaper
• There are no queues
• Improved customer service
• Improved operational efficiency
• Big Data and analytics
• Innovation and new opportunities

But there are disadvantages too: 

• Dependence on the Internet
• Vulnerability of security systems and regular hacker attacks
• Inaccessibility for some customers
• Threat of job losses
• Dependence on technology

Technology should become a tool that will give banks more flexibility in decision-making and reduce risks.

Fintech companies, which have recently created large-scale services with significantly more interaction points with the client than the classic banking business, are taking the lead. Given that over the last 10 years, the banking industry has experienced a serious tightening of regulatory requirements, fintech is becoming a severe competitor for banks. The solution that banks have found is to change their business model with a focus on digitalization, create their own ecosystems, and develop non-financial services.

Ecosystems are a new global standard for business development and a major stage in the development of the economy. They aggregate data on producers and consumers and help optimize the resources of both. There is no turning back. Creating ecosystems seems to be a common vertical integration strategy for banks when related businesses are pulled up to the core business.

We highlight 5 key factors driving digital transformation in banking:

• Customer experience. Providing convenience and personalization for customers is a crucial factor in digital transformation. Banks should develop and implement innovative digital channels, such as mobile apps, online banking, chatbots, and others, to facilitate access to financial services and improve customer satisfaction.
• Automation and process optimization. The use of automation technologies, such as robo-advisors, machine learning, and artificial intelligence, helps reduce routine operations, lower costs, and improve efficiency. This can include automating lending, foreign exchange, internal audit, and more.
• Evolving regulatory landscape. Regulatory changes and initiatives have pushed banks to adopt digital transformation. Open banking regulations, data protection regulations (such as GDPR), and initiatives promoting competition and innovation have compelled banks to invest in technology to comply with regulations, foster innovation, and enhance transparency.
• Competitive pressure. Fintech startups and tech giants have disrupted the traditional banking landscape. These non-traditional players offer innovative and agile financial services, posing a competitive threat to traditional banks. To remain competitive, banks invest in digital technologies to improve their offerings, provide unique value propositions, and stay ahead of the competition.
• Enhanced customer insights. Digital transformation enables banks to gain deeper insights into customer behavior, preferences, and needs. By analyzing customer data, banks can offer personalized services, targeted marketing campaigns, and customized product recommendations, leading to higher customer satisfaction and loyalty.

These factors interact with each other and require a comprehensive approach for successful digital transformation in the banking industry.

An important point is cybercrime. The emergence of new technologies has left hackers with many loopholes for hacking into networks and devices. At the current growth rate, cyberattack damage will amount to about $10.5 trillion annually by 2025 —a 300% increase from 2015.

However, cyber threats are not slowing down digital transformation. On the contrary, they drive it (this applies to banks and other organizations). The search for vulnerabilities is a never-ending process that contributes to developing security systems. 

The main principle of the fight against cybercrime in many banks is that the fight should be at all levels. It means from the protection of external perimeters to specific systems at specific addresses and ports. This includes protection against DoS attacks, firewalls, full control of the bank’s systems, control of viruses to avoid data leakage, etc.

Technologies are evolving at an incredible pace. Artificial Intelligence (AI), Big Data, Blockchain, and other innovations transform how we live, work, and do business.

For example, artificial intelligence allows banks to automate processes and make customer interaction more personalized and efficient. Machine learning can analyze large amounts of data, identify patterns and trends that help make better decisions and predict risks. Machine learning and neural networks also greatly help in document recognition and remote customer verification. 

Big Data analysis is becoming a valuable tool in the banking sector, allowing banks to identify patterns, trends, and useful insights hidden in huge amounts of data. It can be used to develop personalized products and services, improve decision-making, detect fraud , and understand and predict customer behavior.

Blockchain is another innovative technology that can tremendously change the banking industry. Most of the current problems in the banking sector are related to the human factor. In particular, they include high commission costs and time spent on money transfers and transactions, internal and external fraud, human error, leakage of personal data, and much more. There are several main areas where blockchain technology can be used in the banking industry:

• Smart contracts
• International payments, settlements for foreign trade transactions, and internal payments
• Transactions with securities
• National digital currency

Other technologies that drive digital transformation in banking include Cloud Computing, Internet of Things (IoT), Robotic Process Automation (RPA), Biometrics, and Open Banking APIs.

Many success stories of digital transformation in banking demonstrate how digitalization improves customer banking experience and operational efficiency. For example:

• DBS Bank (Singapore). DBS Bank is considered one of the leaders in digital transformation. They have developed a digital platform, DBS Digibank, which provides customers with a wide range of banking services through mobile apps and online banking. They actively use artificial intelligence and analytics to provide personalized recommendations and improve customer experience.
• JPMorgan Chase (USA). JPMorgan Chase has embraced digital transformation to improve operational efficiency and customer service. They have developed their proprietary digital platform, Chase Mobile Banking, which allows customers to perform various banking transactions through mobile devices. They also actively apply machine learning and analytics to better analyze data and deliver services.
• ING Bank (Netherlands). ING Bank has moved from a traditional bank to a digital organization. They provide customers convenient online services and mobile apps and actively use data analytics to provide personalized offers and improve customer experience. They have also implemented digital tools within the bank to streamline processes and improve efficiency.
• BBVA (Spain). BBVA focused on digital transformation and innovation to improve customer experience and banking processes. They developed the BBVA Digital Banking platform, which provides customers with a wide range of services through mobile apps and online banking. They have also implemented blockchain technology to improve the security and efficiency of financial transactions.
• Ally Bank (USA). Ally Bank is an example of a successful digital transformation. They provide a full range of banking services through an online platform, including account opening, lending, investments, and mortgages. Ally Bank actively utilizes digital channels and tools to provide convenience and accessibility to customers.

These examples demonstrate how banks use digital technologies to increase the availability of services, improve customer experience, and optimize their operations.

At SoloWay Tech, we specialize in providing comprehensive digital transformation and consulting services to help businesses thrive in the digital age. With our expertise and industry knowledge, we can guide your organization through the complex digital transformation process, enabling you to unlock new opportunities and achieve sustainable growth. We can:

• Consult regarding the digital transformation of your business
• Develop a digital transformation strategy
• Design digital customer experience
• Optimize business processes
• Automate business processes
• Re-engineer legacy apps
• Develop innovative products and services
• Implement end-to-end ML and AI engines
• Engineer IoT
• Build Big Data infrastructure
• Consult regarding the best implementation of IT infrastructure in your business.

At SoloWay Tech, we understand that each business has unique challenges and requirements. Our collaborative approach, deep industry expertise, and proven methodologies empower us to tailor our services to your specific needs, enabling you to achieve sustainable growth and competitive advantage through digital transformation.

Embark on your digital transformation journey with SoloWay Tech and unlock the full potential of your business in the digital era. Contact us today to learn more about our services and how we can help you drive innovation, efficiency, and success.

Digital transformation has become imperative for the banking industry to adapt to the evolving needs and expectations of customers in the modern world. The shift towards digitalization offers numerous advantages, such as enhanced customer experiences, improved operational efficiency, access to Big Data analytics, and new opportunities for innovation. However, there are also challenges to consider, including cybersecurity risks, potential job losses, and dependence on technology.

To embark on a successful digital transformation journey, businesses may seek the expertise of companies like SoloWay Tech that specialize in assisting organizations in their digitalization efforts. With the right guidance and implementation strategies, banks can harness the power of digital technologies to stay competitive, meet customer expectations, and drive innovation in the ever-evolving banking landscape.

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

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Risk Management in Banking: Case Studies

These case studies involving risk management in banking demonstrate how to handle complex situations successfully. Learn more today.

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Risk Management in banking comes with a significant number of challenges, as banks must stay compliant with endlessly changing rules while making transactions seamless for customers. Eliassen Group is known for our considerable risk and compliance experience in the financial services industry, and we can support teams that must respond to Matters Requiring Attention (MRAs) and other regulatory actions. For proof, look no further than these recent case studies for two top 25 global banks.

Case Study #1: Deployment of Enterprise-Wide Risk Management Framework and Supporting Capabilities

To respond to regulatory actions and MRAs, this global bank needed to deploy an enhanced enterprise-level Risk Management Framework and supporting capabilities across all Front-Line Units with requirements that impacted all lines of business. This would be a daunting task for any company. Luckily, the client had worked with Eliassen Group in the past, and they knew that we could help them embed sustainable, repeatable controls into the client's processes.

We led the deployment of key Risk Management Framework process, system, and policy components across one of the lines of business. Not only did we meet immediate deadlines and go beyond expectations, but aspects of our approach were also adopted by all business groups across the enterprise. During the engagement, we successfully transitioned the program to new executive and workstream leadership as the client made broad organizational changes.

"We focus on helping our clients implement and execute their risk management program, which is why they continue to reach out to us when they need help," said Bill Gienke, Managing Director at Eliassen Group. "I am especially proud of how we collaborated with this client to prioritize and deliver a complex program that met evolving regulatory and internal requirements."

Case Study #2: End-to-End High Risk Client Review via the Enhanced Due Diligence Process To Meet Regulatory Requirements

A second global bank asked Eliassen Group for support with a different but equally difficult scenario – regulators required their wealth management division to improve its Enhanced Due Diligence (EDD) reviews of high-risk customers. After working with Eliassen Group on key risk and compliance initiatives, the client knew that Eliassen Group had financial crimes experience and could help stand up a team to work on the backlog of reviews, train team members, handle quality assurance of risk assessments, make decisions to retain or exit customers, and build a sustainable business as usual process.

Eliassen Group made a powerful impact – the client upgraded their internal audit rating of the Anti-Money Laundering (AML) within their Investment Division for the first time in several years. In addition, we achieved a 99% Quality Control pass rate, and we were recognized as a role model for other teams.

"We are in the business of becoming that trusted strategic partner building client relationships to stand the test of time because when our clients win, we all win," said Jay Gentile, Principal, Client Solutions, at Eliassen Group. "Our progressive delivery models are designed to ensure consistent, repeatable, and sustainable results across processes and teams."

Our in-depth knowledge and willingness to collaborate so we can ultimately train your team to stay on top of regulations help us stand out. Interested in hearing more? Contact us today.

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AI in Banking [10 Case Studies] [2024]

In the rapidly altering finance landscape, AI has emerged as a pivotal significance, extending banks’ abilities and reshaping traditional financial patterns. From enhancing customer experiences to mitigating financial risks, AI’s role in banking is pivotal and transformative. This exploration delves into ten distinct case studies where leading banks have successfully implemented AI to address complex challenges in the industry. These examples showcase AI’s innovative applications and highlight its potential to revolutionize banking operations, improve customer service, and bolster financial security. As we navigate through these case studies, we gain insights into the strategic advantages and practical impacts of AI in the banking sector, underscoring its importance in shaping the future of finance.

Related: High-Paying Banking Jobs & Career Paths

Case Study 1: JP Morgan Chase: Streamlining Loan Approvals

The traditional loan approval process is notoriously cumbersome and slow, heavily reliant on manual data handling. This results in prolonged wait times, leading to significant customer dissatisfaction and increasing operational costs due to the extensive need for human oversight and intervention.

To address these inefficiencies, JP Morgan Chase has implemented an advanced AI system that automates key aspects of the loan approval process. This system utilizes machine learning to swiftly and accurately analyze various data points, including applicants’ credit history, recent transaction data, and current financial behaviors. Doing so enhances the speed and accuracy of creditworthiness assessments, reduces reliance on manual processes, and improves overall customer experience by expediting loan approvals.

Overall Impact:

• Increased Speed:  Loan processing times have dramatically reduced from days to minutes and hours.
• Enhanced Customer Satisfaction:  Faster loan approvals increase customer satisfaction and loyalty.
• Cost Efficiency:  Reduced reliance on manual processes decreases operation expenses and improves profitability.
• Scalable Operations:  The bank can handle more loan applications without significantly increasing staff or resources.

Key Learnings:

• Process Efficiency:  AI drastically cuts down the time required for loan approvals.
• Operational Cost Reduction:  Automation reduces the labor-intensive elements of loan processing.
• Enhanced Risk Management:  AI provides a more accurate and comprehensive loan risk assessment.
• Customer Retention:  Improved process speeds and accuracy improve customer retention rates.

Future Prospects:

AI algorithms could be enhanced for faster processing, achieving near-instant approval times. Future iterations may further integrate broader economic indicators to refine credit risk assessments, enhancing personalized lending strategies.

Case Study 2: Bank of America: Erica, the AI-Powered Financial Assistant

As digital banking gains traction, customer expectations are also evolving. Users now demand personalized services on-demand and easily accessible through their digital devices. This shift has pushed banks to find innovative solutions to meet these new customer demands without compromising service quality.

Bank of America responded to this digital shift by launching Erica, an AI-driven virtual assistant designed to enhance the mobile banking experience. Accessible via mobile apps, Erica offers a wide range of functionalities that cater to the modern banking customer’s needs. These include handling transaction queries, updating credit reports, and providing proactive financial advice. Erica’s capabilities are powered by sophisticated algorithms that analyze user behavior and large datasets, enabling customized and efficient service that meets the high expectations of today’s bank customers.

• Personalized Customer Interaction:  Erica offers tailored banking advice, enhancing user engagement.
• Increased Accessibility:  Round-the-clock availability allows customers to receive instant assistance without waiting for human help.
• Data-Driven Insights:  Erica provides insights based on a deep analysis of user transactions and behaviors, helping customers manage their finances better.
• Operational Efficiency:  The AI assistant handles regular inquiries, leaving humans to deal with more complex issues.
• Enhanced User Experience:  AI-driven tools like Erica improve customer experience by providing quick, personalized service.
• Operational Scalability:  AI can manage increasing volumes of consumer interactions without additional human resources.
• Proactive Service:  AI enables proactive engagement, offering financial advice and alerts that can prevent issues before they arise.
• Customer Data Utilization:  Using AI to analyze customer data effectively can lead to more accurate and useful financial advice.

Erica could develop more sophisticated natural language processing capabilities to manage increasingly complex inquiries and transactions. Integration with IoT devices and other platforms may offer holistic financial management solutions, extending personalized services beyond traditional banking.

Case Study 3: HSBC: Enhancing Anti-Money Laundering Efforts

Money laundering remains a formidable challenge for financial institutions worldwide. Traditional systems designed to detect such activities often struggle under modern financial transactions’ heavy volume and complex nature. These systems can be overwhelmed, resulting in undetected fraudulent activities and significant regulatory penalties for banks.

In response, HSBC has integrated an AI-driven system to bolster its anti-money laundering (AML) efforts. This advanced system employs sophisticated machine learning algorithms to analyze many real-time transactions. By detecting unusual patterns and potential illegal activities, the system can far more effectively differentiate between normal and suspicious activities than traditional methods. This AI-enhanced approach allows HSBC to address the complexities of modern financial crime while improving compliance and reducing the risk of oversight.

• Improved Detection Rates:  The AI system has significantly increased the detection of suspicious transactions, reducing the risk of financial crimes.
• Reduced False Positives:  Enhanced accuracy in distinguishing legitimate from suspicious activities, minimizing disruptions to innocent customers.
• Compliance Efficiency:  AI assists in maintaining compliance with evolving regulatory requirements, adapting more quickly to new rules.
• Cost Reduction:  Automating surveillance reduces the need for extensive manual review teams, lowering operational costs.
• Accuracy in Surveillance:  AI technologies improve the accuracy and efficiency of financial monitoring systems.
• Adaptive Compliance:  AI can adapt quickly to new regulatory changes, aiding compliance efforts.
• Resource Optimization:  Implementing AI reduces the need for large human oversight teams, optimizing resource use.

Future developments may incorporate predictive analytics to detect and predict laundering schemes before they are fully enacted. Integration with international finance monitoring systems could enhance global compliance and tracking capabilities.

Related: Is Banking a stressful job?

Case Study 4: Citibank: Optimizing Customer Service with AI Chatbots

In the fast-paced banking world, high demand for customer service can lead to long wait times and inconsistent service experiences. Such delays and variability often detract from customer satisfaction and can negatively impact customer retention rates. As digital interactions become the norm, banks face the challenge of maintaining high service standards while managing large volumes of customer inquiries efficiently.

Citibank has implemented AI-powered chatbots across its digital platforms to address this challenge. These chatbots are arranged to address a spectrum of consumer inquiries, offer real-time support, and efficiently settle typical issues. By deploying these AI chatbots, Citibank ensures a uniform and agile consumer service experience. The chatbots are equipped to understand and process user queries quickly, offering solutions and guidance instantaneously. This technology reduces the burden on human customer service representatives and enhances overall customer satisfaction by providing timely and reliable support.

• Enhanced Customer Service:  Immediate response to inquiries improves customer satisfaction.
• 24/7 Availability:  Customers receive help anytime without needing human agent availability.
• Consistent Experience:  AI ensures that every customer interaction is handled uniformly, enhancing service reliability.
• Operational Savings:  The chatbots handle routine inquiries, decreasing the workload on human client service agents and decreasing operational costs.
• Service Accessibility:  AI tools can provide constant and consistent consumer service.
• Cost Efficiency:  Automating routine interactions can significantly reduce customer service costs.
• Customer Engagement:  Real-time interactions facilitated by AI can boost customer engagement and loyalty.

AI chatbots could evolve to handle more sophisticated negotiations and problem-solving tasks, further reducing the need for human intervention. Future versions might seamlessly integrate into omnichannel customer service strategies, providing a unified interface across all banking platforms.

Case Study 5: Santander: Predictive Analytics for Loan Default Prevention

Loan defaults pose a great financial risk to banks, affecting their profits and stability. Traditional risk assessment models often fall short in accurately predicting defaults before they occur, primarily because they may not account for dynamic changes in customers’ financial situations or broader economic trends. This limitation leads to unexpected financial losses and inefficient allocation of resources for risk management.

Santander has adopted a proactive approach to this challenge by integrating predictive analytics models powered by AI into its risk management strategy. These models use a combination of historical data analysis and real-time monitoring of account behaviors to detect early warning signs of potential loan defaults. By identifying at-risk customers before defaults occur, Santander can engage with them to offer tailored financial advice, restructuring options, or other support measures. This early intervention helps mitigate risks associated with loan defaults and improves the bank’s and its customers’ overall financial health.

• Reduced Default Rates:  Early identification and intervention have led to a decrease in loan defaults.
• Enhanced Customer Support:  At-risk customers receive tailored advice and restructuring options, improving financial outcomes.
• Operational Efficiency:  The bank optimizes resource allocation by focusing efforts where they are needed the most.
• Improved Risk Management:  Better predictive capabilities allow for more accurate risk pricing and reserve allocation.
• Proactive Risk Management:  Early detection of potential defaults enables more effective mitigation strategies.
• Customer Retention:  Proactive engagement helps maintain customer relationships and loyalty.
• Financial Health:  Improved risk assessment contributes to the bank’s overall financial health and stability.
• Resource Allocation:  AI enables more targeted and efficient use of resources in risk management activities.

Integrating wider socio-economic data could improve predictive models, offering even more precise forecasts of potential defaults. These enhancements allow customized intervention strategies tailored to individual customer profiles and economic conditions.

Case Study 6: Wells Fargo: Fraud Detection Enhancement

Real-time fraud detection in financial transactions presents a major challenge, as traditional methods often lag behind fraudsters’ sophisticated techniques. Wells Fargo faced significant challenges in effectively identifying and preventing fraudulent activities. Their traditional systems struggled to keep up without mistakenly flagging legitimate transactions as fraudulent, leading to customer dissatisfaction and operational inefficiencies.

To address this issue, Wells Fargo implemented an AI-based fraud detection system employing deep learning algorithms to scrutinize real-time transaction patterns. This advanced system is designed to compare each transaction against an extensive database of known fraudulent behaviors, enhancing its ability to make accurate assessments instantly. By doing so, the system significantly improves fraud detection accuracy, minimizing false positives and ensuring that legitimate customer transactions are not disrupted. This method boosts security and enhances the overall customer experience by minimizing delays and errors in transaction processing.

• Improved Fraud Detection: The AI system has a higher accuracy rate in identifying fraudulent transactions, reducing the incidence of fraud.
• Minimized Customer Disruption: Accurate fraud detection means fewer legitimate transactions are flagged incorrectly, ensuring smoother customer experiences.
• Enhanced Security: The system enhances overall transaction security, giving customers greater confidence in using Wells Fargo’s services.
• Cost Efficiency: Decreased fraud incidence reduces financial losses and related costs for the bank.
• Real-Time Processing: AI can process and analyze real-time transactions, offering immediate fraud alerts.
• Data Utilization: Leveraging large datasets enhances the system’s ability to identify and learn from emerging fraud patterns.
• Customer Trust: Improved security measures boost customer trust and satisfaction.

Wells Fargo plans to integrate further enhancements into the AI system, such as adaptive learning capabilities that can evolve with changing fraud tactics. This will allow for even more dynamic and robust fraud prevention mechanisms.

Case Study 7: Barclays: Streamlining Wealth Management

Barclays faced challenges in meeting the high expectations of its high net-worth clients who demand personalized, efficient wealth management services. Traditional methods were slow and often ineffective in providing the customization and rapid service these clients expected, leading to dissatisfaction and operational inefficiencies.

Barclays introduced an AI-driven platform to transform its wealth management services. This platform uses advanced analytics to deeply understand individual client preferences and performance, enabling tailored investment advice and automated portfolio adjustments. This automation enhances service speed and accuracy, improving client satisfaction and streamlining operations.

• Personalized Service: Clients receive highly customized investment advice, improving satisfaction and engagement.
• Increased Efficiency: The AI platform automates routine portfolio management tasks, freeing up advisors to focus on client relationships.
• Better Investment Performance: AI-enhanced analytics provide deeper insights into market trends, aiding better investment decisions.
• Scalability: The platform can efficiently manage many portfolios, scaling as the client base grows.
• Enhanced Customization: AI enables a high degree of personalization in delivering services. This technology tailors interactions to meet individual user needs effectively.
• Advisor Efficiency: Automating routine tasks allows wealth managers to focus more on strategic client interaction.
• Data-Driven Decisions: Utilizing AI for data analysis improves the accuracy and timeliness of investment decisions.

Barclays intends to refine its AI capabilities further, incorporating more comprehensive data sources, including global economic indicators and social trends, to enhance investment strategy recommendations.

Related: Banking Cybersecurity Case Studies

Case Study 8: Deutsche Bank: Optimizing Credit Card Fraud Detection

Credit card fraud poses a major problem for banks, resulting in annual losses amounting to millions and eroding customer trust. This persistent issue challenges financial institutions to enhance their security measures and maintain client confidence. Deutsche Bank faced the challenge of rapidly identifying and mitigating fraudulent credit card activities without affecting genuine transactions.

Deutsche Bank implemented an AI-based solution specifically designed to improve credit card fraud detection. This solution uses advanced machine learning models to monitor and analyze real-time credit card transactions. The system can quickly identify anomalies that suggest fraudulent activity by learning from historical transaction data and continuously adapting to new fraud patterns.

• Increased Detection Accuracy: The AI system significantly enhances the ability to spot fraudulent transactions, reducing financial losses.
• Enhanced Customer Trust: Customers feel more secure using their credit cards, knowing that advanced measures are in place to protect them.
• Operational Efficiency: The automated system allows for faster response times and reduces the workload on manual review teams.
• Reduced False Positives: The system effectively minimizes disruptions to innocent customers by accurately distinguishing between legitimate and fraudulent activities.
• Adaptive Learning: Machine learning models adapting to new data and evolving fraud tactics are more effective than static models.
• Customer Experience: Maintaining a balance between aggressive fraud detection and customer convenience is crucial for customer satisfaction.
• Security as a Priority: Investing in advanced security measures like AI protects the bank’s assets and builds customer loyalty.

Deutsche Bank plans to integrate more granular behavioral analytics to refine the system’s accuracy further. Additionally, collaborating with global financial networks to share fraud intelligence could enhance the system’s predictive capabilities, setting a new standard for fraud prevention in the banking industry.

Case Study 9: Credit Suisse: Enhancing Mortgage Underwriting with AI

Credit Suisse encountered significant challenges in its mortgage underwriting process, which relied heavily on manual input, making it both time-consuming and prone to creating backlogs of applications. This inefficient process delayed loan disbursals and negatively impacted customer satisfaction, as clients experienced lengthy wait times and unpredictable service levels. Streamlining this process was crucial to improving operational efficiency and maintaining customer trust and loyalty.

Credit Suisse adopted an AI-driven approach to transform its mortgage underwriting process. The AI system uses machine learning to assess applicant data such as income, credit score, employment history, market trends, and property evaluations more quickly and accurately than manual methods. This automation allows for faster decision-making and more precise risk assessment.

• Faster Processing Times: The time taken to approve mortgages has been significantly reduced, enhancing customer satisfaction.
• Increased Accuracy: AI provides more accurate assessments of applicant risk profiles, reducing the likelihood of loan defaults.
• Operational Efficiency: Automating routine tasks allows human underwriters to concentrate on handling more complex cases. This shift frees up valuable resources for more critical and detailed work.
• Scalable Underwriting Capacity: The system can handle more applications without additional staff.
• Automation in Risk Assessment: The use of AI for processing and analyzing complex applicant data streamlines risk assessment.
• Improved Customer Experience: Reducing wait times for loan approvals directly impacts customer satisfaction positively.
• Enhanced Decision Making: AI tools provide a deeper insight into potential risks and applicant credibility, aiding better decision-making.

Credit Suisse plans to further enhance the capabilities of its AI system by integrating it with real-time economic indicators and more detailed applicant lifestyle data to predict future financial stability more accurately. This advancement aims to streamline the process and tailor mortgage products more specifically to individual needs, setting a new standard in personalized banking services.

Case Study 10: Standard Chartered: Streamlining Trade Finance Operations

Standard Chartered faced complexities in managing trade finance operations, which involve extensive documentation and verification processes that are traditionally manual and error-prone. These challenges resulted in slow transaction times and higher operational costs, affecting client satisfaction and competitiveness in the global market.

Standard Chartered introduced an AI-driven platform designed to automate and enhance the efficiency of its trade finance operations. Utilizing sophisticated machine learning algorithms, the platform efficiently verifies documents, authenticates data, and streamlines the entire approval process for trade transactions. This integration of advanced technology ensures faster, more accurate handling of the complex documentation and regulatory requirements inherent in trade finance, improving overall transaction speed and reliability. By automating these key steps, the bank has significantly reduced manual errors and sped up the processing of trade finance operations.

• Reduced Processing Time: Transaction times for trade finance operations have been drastically reduced, increasing client satisfaction and transaction volumes.
• Decreased Operational Costs: Automation has minimized the need for extensive manual intervention, significantly cutting operational costs.
• Enhanced Accuracy: The AI system provides a higher level of precision in document verification and data authentication, decreasing the risk of fraud and errors.
• Improved Compliance: The system ensures better adherence to international trade regulations through accurate and automated compliance checks.
• Efficiency through Automation: Automating complex, repetitive tasks can significantly enhance efficiency and accuracy in high-stakes financial operations.
• Client Satisfaction: Quicker processing times and fewer errors directly enhance client relationships and contribute to business expansion.
• Regulatory Compliance: AI tools are vital in ensuring compliance with the continuously changing international trade laws. They help organizations adapt quickly to regulatory updates, maintaining legal integrity across global operations.

Standard Chartered is looking to expand its AI capabilities to include predictive analytics for assessing the potential risks and opportunities in trade finance. Further integration with blockchain technology could enhance security and transparency in international trade transactions, setting new industry standards for efficiency and trust.

Related: Will Banking jobs be Automated?

The integration of AI in banking, as demonstrated through these ten case studies, marks a significant leap toward a more efficient, secure, and customer-centric future in finance. Banks like JP Morgan Chase, Bank of America, HSBC, Citibank, and Santander are at the forefront, harnessing AI to enhance decision-making, streamline operations, and enrich customer interactions. These cases vividly illustrate how AI can effectively address traditional banking challenges, driving significant service delivery and risk management improvements. As the banking industry continues to evolve, the strategic deployment of AI will not only be a competitive advantage but a necessity, paving the way for innovative solutions that meet the complex demands of modern finance.

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Investment Banking Case Studies: Preparation & Strategies for Success

Discover the key strategies and preparation techniques required for success in investment banking case studies.

Posted May 11, 2023

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Table of Contents

Investment banking case studies are an essential part of the investment banking recruitment process. These studies enable investment banks to assess the ability of aspiring investment bankers to analyze complex business situations and develop innovative strategies to solve them. So, if you're preparing to enter the world of investment banking, you need to master the art of preparing for and tackling these studies.

Introduction to Investment Banking Case Studies

Investment banking case studies typically involve analyzing a real-life business situation, evaluating critical financial and operational data, and developing a comprehensive strategy to help the company achieve its goals. These case studies allow investment banks to understand the prospective banker's ability to think critically, analyze data, and develop creative solutions to complex problems.

Importance of Studying Investment Banking Case Studies

If you're looking to pursue a career in investment banking, it's essential to develop a deep understanding of investment banking case studies. Studying them will provide you with a unique perspective that can help you formulate effective strategies in your future role. A strong foundation in case studies can also enhance your problem-solving skills and equip you with the analytical tools necessary for success in the industry.

Moreover, studying investment banking case studies can also help you understand the complexities of the industry. You'll learn about the different types of financial instruments, such as stocks, bonds, and derivatives, and how they are used in various investment strategies. This knowledge can be invaluable when working with clients and making investment decisions.

Additionally, investment banking case studies can provide you with insights into the ethical considerations that arise in the industry. You'll learn about the importance of transparency, accountability, and responsible investing. This knowledge can help you navigate the ethical challenges that you may face in your career and make informed decisions that align with your values.

Types of Investment Banking Case Studies

There are two main types of investment banking case studies: financial analysis and valuation and strategic analysis. Financial analysis and valuation case studies involve assessing the company's financial statements and financial data to determine the company's worth. Strategic analysis case studies typically require analyzing the business's operations and identifying strategies that can help it grow and succeed in the market.

Another type of investment banking case study is the industry analysis case study. This type of case study involves analyzing the industry in which the company operates, including its competitors, market trends, and regulatory environment. This information can help investment bankers advise their clients on potential mergers and acquisitions, as well as other strategic decisions.

Additionally, investment bankers may also conduct due diligence case studies. These case studies involve a thorough investigation of a company's financial and operational history, as well as its legal and regulatory compliance. Due diligence case studies are often conducted when a company is considering a merger or acquisition, to ensure that there are no hidden risks or liabilities that could impact the deal.

Real-life Examples of Successful Investment Banking Case Studies

Investment banking case studies are based on real-life situations that have been solved by investment bankers. Some of the most well-known and successful investment banking case studies include the Red Bull GmbH case study and the Alibaba IPO case study. These studies demonstrate the effectiveness of investment banking strategies in real-life business situations and showcase the importance of case studies in the industry.

Another example of a successful investment banking case study is the acquisition of WhatsApp by Facebook. Investment bankers played a crucial role in facilitating the acquisition, which was valued at $19 billion. The investment bankers advised Facebook on the best approach to acquire WhatsApp and negotiated the terms of the deal. This case study highlights the importance of investment bankers in facilitating mergers and acquisitions and showcases their ability to create value for their clients.

Understanding the Preparation Process for Investment Banking Case Studies

The preparation process for investment banking case studies is critical to ensure your success. It involves identifying the objective of the case study, conducting extensive research, evaluating financial and operational data, and developing creative solutions to solve complex problems. However, it's essential to approach the case study's preparation systematically and logically to ensure your strategy is comprehensive and effective.

One important aspect of the preparation process for investment banking case studies is to practice presenting your solutions. This can be done through mock presentations with peers or mentors, or by recording yourself and reviewing your performance. Practicing your presentation skills will help you communicate your ideas clearly and confidently during the actual case study presentation. Additionally, it will help you identify any areas where you may need to improve, such as speaking too quickly or not providing enough detail. By practicing your presentation skills, you can increase your chances of success in the case study and impress potential employers.

Tips for Conducting Effective Research for Investment Banking Case Studies

Conducting effective research is a critical component of the preparation process for investment banking case studies. It allows you to gain a deep understanding of the business's operations, objectives, strengths, weaknesses, and opportunities. Some tips for conducting effective research include conducting primary and secondary research, analyzing market trends, and considering industry-specific factors that may affect the company's performance.

Another important tip for conducting effective research is to gather information from a variety of sources. This can include financial reports, industry publications, news articles, and interviews with industry experts. By gathering information from multiple sources, you can gain a more comprehensive understanding of the company and its industry.

It is also important to stay up-to-date on current events and trends that may impact the company's performance. This can include changes in government regulations, shifts in consumer behavior, or advancements in technology. By staying informed, you can better anticipate potential challenges and opportunities for the company.

Analyzing Data and Applying it to your Investment Banking Case Study

The ability to analyze data effectively is crucial in developing innovative and effective investment banking strategies. Analyzing financial and operational data can provide critical insights into the business's current position and future potential. Therefore, it's imperative to approach data analysis systematically and logically, using industry-specific benchmarks to evaluate the business's performance.

Furthermore, data analysis can also help identify potential risks and opportunities for the business. By analyzing market trends and competitor performance, investment bankers can develop strategies that capitalize on emerging opportunities and mitigate potential risks. Additionally, data analysis can also aid in identifying areas where cost-cutting measures can be implemented, leading to increased profitability for the business.

The Importance of Creativity in Developing Investment Banking Strategies

Creativity is an essential ingredient in developing effective investment banking strategies. It enables you to think outside the box and develop innovative solutions to complex problems. Therefore, it's crucial to cultivate your creativity by developing unique and original strategies that can set you apart from your competitors.

Moreover, creativity can also help you identify new opportunities and potential risks that may not be immediately apparent. By thinking creatively, you can uncover hidden value in assets and identify new markets that may have been overlooked. This can give you a competitive advantage and help you stay ahead of the curve in a constantly evolving industry.

Developing a Comprehensive Investment Banking Strategy

Developing a comprehensive investment banking strategy involves identifying the problem, conducting research, analyzing financial data, and developing a creative solution that aligns with the business's objectives. It is essential to ensure that your strategy is comprehensive, relevant, and innovative to maximize its effectiveness.

The first step in developing a comprehensive investment banking strategy is to identify the business's financial goals and objectives. This involves understanding the company's current financial situation, its strengths and weaknesses, and its long-term goals. Once you have a clear understanding of the business's financial objectives, you can begin to develop a strategy that aligns with these goals.

Another important aspect of developing a comprehensive investment banking strategy is to stay up-to-date with the latest industry trends and market conditions. This involves conducting ongoing research and analysis to identify emerging opportunities and potential risks. By staying informed about the latest developments in the industry, you can ensure that your strategy remains relevant and effective over time.

Key Components of a Successful Investment Banking Strategy

Successful investment banking strategies typically include identifying key performance indicators, conducting a competitive analysis, developing a viable financial plan, assessing potential risks, and preparing a detailed execution plan. Integrating these key components into your strategy can help ensure its effectiveness and maximize your chances of success.

In addition to these key components, it is also important to establish strong relationships with clients and maintain a deep understanding of market trends and industry developments. This can involve regularly networking with potential clients and staying up-to-date on the latest news and changes in the market. By staying informed and building strong relationships, investment bankers can better position themselves to identify and capitalize on new opportunities.

Implementing Your Strategy: Best Practices for Success

Implementing your investment banking strategy can be challenging and requires careful planning and execution. Some best practices for success include ensuring everyone on the team is aligned with the strategy, monitoring performance regularly, adjusting the strategy as needed, and developing contingency plans to mitigate potential risks. These practices can help ensure that your strategy yields the best possible results for your business.

Another important aspect of implementing your strategy is effective communication. It is crucial to communicate the strategy clearly and consistently to all stakeholders, including employees, investors, and clients. This helps to build trust and confidence in the strategy and ensures that everyone is working towards the same goals. Additionally, it is important to provide regular updates on the progress of the strategy and any changes that may occur. This helps to keep everyone informed and engaged in the process, which can lead to better outcomes.

Measuring the Success of Your Investment Banking Strategy

Measuring the success of your investment banking strategy is critical to ensuring its effectiveness. It's essential to establish key performance indicators and regularly monitor them to determine whether your strategy is yielding the expected results. This data can provide critical insights that can help you adjust your strategy and improve its effectiveness in achieving the business's goals.

Common Pitfalls to Avoid in Your Investment Banking Case Study

There are several common pitfalls that aspiring investment bankers must avoid when tackling investment banking case studies. These include failing to understand the business's objectives, relying too heavily on financial data, neglecting creativity in developing the strategy, and developing a strategy that is too generic to be effective. It's essential to avoid these pitfalls to maximize your chances of success.

Conclusion: Key Takeaways and Actionable Steps for Success

Investment banking case studies are an essential part of the recruitment process and require careful preparation and execution to ensure success. Understanding the preparation process, developing effective research techniques, analyzing data systematically, and cultivating creativity are key components of effective investment banking strategies. Integrating these components and avoiding common pitfalls can help maximize your chances of success and set you apart from the competition. By following these actionable steps, you can become a successful investment banker and achieve your professional goals.

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Investment Banking Case Studies – Job Application

By Ivy Wang |

 Reviewed By Rebecca Baldridge |

November 21, 2022

What are Investment Banking Case Studies

Candidates will normally encounter case studies at the final stage of the application process, likely during an assessment or final-stage interview. Investment banking roles are highly competitive, and candidates must be properly prepared.

Investment banking case studies are commonly used to assess how a job candidate would perform in a real situation by presenting them with a theoretical scenario similar to those encountered on the job.

Most investment banking case study questions center on acquisitions, raising capital, or company expansion. The case may be given to you ‘blind’ on the day of your assessment with only a short amount of time provided for preparation. If the case is likely to involve deep analysis, financial modeling, or a company valuation, you will likely be given the case in advance to give you more time to work on it before the assessment day.

Key Learning Points

• Investment banking case studies are often used to assess how job candidates would perform in a real situation by presenting them with a theoretical scenario like one they would encounter on the job.
• While general questions give the interviewer a superficial impression of the candidate’s skills and fitness, case study questions allow the interviewer to assess the candidate’s ability to handle multiple levels of analysis and problem-solving.
• Case study questions test reasoning and communication skills as well as analytical skills. They are useful for assessing how candidates approach complex issues, make critical judgments, and deliver recommendations.
• With many case study problems, there will be more than one path to success and more than one possible solution.
• Investment banks are looking for decisive candidates who can articulate and logically present their solutions and defend their decisions under scrutiny.

What are the Types of Investment Banking Case Studies?

In general, there are two types of case studies during an investment banking assessment, the decision-making case, and the financial modeling case.

Decision-Making Investment Banking Case Studies

Decision-making case studies appear more frequently than modeling case studies. In this type of case study, the candidate must make decisions for their client and provide advice. The client case studies could be based on finding funding sources, or whether a proposed merger should proceed and why or why not.

You should expect these questions to be given to you at the interview. Therefore, you must analyze and present the case within a given time frame. Throughout, you will have 45-60 minutes of preparation time and a 10-minute presentation, followed by a round of Q&A.

These case studies do not involve in-depth analysis of the case, given the short amount of time available.

Decision-Making Case Study Example

One of your clients is a global corporation that manufactures and distributes a wide range of perfumes. They are contemplating ways to expand their business. They may either introduce a new range of fragrances with their current distribution channels or start a completely new company with different stores.

You need to determine which solution is better for the business. To do so, you need to compare the return on investment and decide on a solution. Be ready to support your reasoning.

Modeling Investment Banking Case Studies

Modeling case studies are usually take-home assignments where you must do financial modeling and a simple valuation. Thus, it is more of a modeling test than a case study. The Investment Banker provides an introduction to building models, developing multiple techniques for a comprehensive and practical understanding of the topic.

The modeling case study will either employ a free cash flow to the firm (FCFF) valuation on a company or require a simple merger or leveraged buyout model. You would be expected to analyze the corporations’ valuation multiples to determine whether they are undervalued or overvalued.

Generally, you will be given a few days to complete your analysis. Then, you need to spend 30-45 minutes on the day of the interview presenting your case to the bankers. The analysis will go much deeper than a client case study because you will have more time to work on it.

Modeling Investment Banking Case Study Example

A pharmaceutical company wishes to make an acquisition. It has identified the target company and approached you to determine how much they should pay. You will be provided with the necessary financial information, metrics, and multiples, as well as the buyer and seller company profiles.

To complete the case study, you need to determine if the acquisition is feasible. Second, what would be the structure and synergies of the deal if the buyer has access to capital? You need to use multiples and valuation metrics to determine the price range for the transaction.

Access the three-statement case study example to practice a modeling case study.

How to Prepare for Investment Banking Case Studies

Regardless of which type of case study a candidate is presented, the thought process and deliverables are the same. The best way to prepare is to:

• Ensure that key business concepts are well understood and that you can use the associated terminology comfortably in a conversation.
• Learn about various valuation techniques, how to employ them, and how to interpret them. Prepare for case studies by mastering valuation for investment banking with the online investment banker course . Learn how and when to utilize key valuation methodologies and the supporting calculations
• Make sure you read business news regularly and focus on discussing the details of banking transactions in the news.
• Read as many case studies as possible so you get the knack of understanding business scenarios and analyzing Especially for modeling and valuation-based case studies, you must be prepared to format your work using PowerPoint and Excel.
• Check company websites to see if sample case studies are available for reference. Investment banks do not tend to publish case study questions for practice. However, it is possible to formulate your own questions by looking at business scenarios involving possible mergers, valuations, or capital raises.
• Candidates must practice streamlining their thought process so judgments can be made under time pressure.
• Read through the scenario carefully before beginning to form an opinion on how the problem should be tackled. This will ensure that no intricacies are missed, and your response addresses all facets of the case.
• Learn how to stand out from the crowd in your interview with the investment banker interview skills course , designed to prepare you for your interviews and enable you to make a great impression.
• Investment Banking Case Study Example

1.     Scenario

A magazine publisher is evaluating whether it should sell, continue to grow organically, or make small “tuck-in” acquisitions to maximize shareholder value. It is selecting an investment bank to advise on its options and has requested a presentation from your bank.

2.     Task

Review the company’s financial and market information and create a 30-minute presentation analyzing its options. Recommend a specific course of action – selling the company, continuing to grow organically, or making smaller acquisitions.

3.     Solution

The answer to this case study is rather subjective. You should take a stand and support it with well thought out reasons. Here’s how you should approach it:

• First, read through everything and get a sense of the industry, where it’s heading, and how much this company might be worth based on comparables. If they don’t give you much information on comparable public companies or precedent transactions, you’ll have to do your own research.
• Complete a brief valuation using public comps, precedent transactions, and a DCF.
• Weigh the numbers the valuation gives you, the company’s organic growth prospects, and whether there are any good companies to acquire.
• Make a decision-it’s usually best to say “sell” unless the industry is growing quickly (over 10% per year), the company is extremely undervalued, or there are acquisition targets that would boost revenue or profit by at least 20-30%.
• Keep this simple and straightforward-the numbers should back up your reasoning, not take over the entire presentation.
• You could get much fancier with the analysis and look at the company’s valuation now, 5 years from now, and if it acquires 1 or 2 companies, but that isn’t necessary and it may just make your presentation more confusing.

4.     Sample Answer

If you decide to sell, you can write:

Slide 1: Recommendation to sell and the three key reasons why.

Slide 2: Industry overview – Is it growing?  Shrinking?  Stagnant?

Slide 3: Company’s position in the industry – Leader?  Tier 2 player?  Where is it strong / weak?

Slide 4: What organic growth would look like 5 or 10 years in the future – how much bigger / more highly-valued would the company be?

Slide 5: Potential tuck-in acquisition candidates.

Slide 6: Why neither organic growth nor acquisitions are the answer.

Slide 7: Why selling now produces the greatest shareholder value.

Slide 8: Valuation – Show public comps and precedent transactions.

Slide 9: Valuation – Show DCF output and sensitivity table.

Slide 10: Conclusion – Reiterate that selling now is the best option and that neither organic growth nor acquiring smaller companies will result in a higher valuation 5-10 years from now.

If you come to a different conclusion – for example, that acquisitions are the best strategy, you would reverse the order and list the solutions you’re not recommending first, concluding with the one you are suggesting.

Investment banking case studies are an important element in the interview process, it is an opportunity to showcase your skills and talent to investment bankers. In general, there are two types of case studies, the decision-making case study and the financial modeling case study. Candidates will need to be confident in their valuation skills. They will also need to display a good level of commercial awareness. Presentation skills are also critical.

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Data Science in Banking – 8 Remarkable Applications with Case Study

The implementation of Data Science in banking is changing the face of the banking industry rapidly. Each and every bank is searching for better ways that will help them to understand the customers for increasing customer loyalty by providing more efficient operational efficiency.

The banks are trying to identify patterns in a large amount of available transaction data for interacting efficiently with their customers.

With Data Science in banking, Banks utilize the data from customer transactions, previous history, trends, communication, and loyalty.

Extracting insights from such a large amount of data is a great challenge because this data is mostly unstructured which is difficult to deal with.

Various methods of data analysis like data fusion and integration , Machine Learning , Natural Language Processing , signal processing , etc. can be used for this purpose. Banks are using Data Science for performing various important tasks like Fraud detection, Customer Segmentation, etc.

In this article, we will walk through the different areas of banking in which Data Science is playing a significant role.

Use Cases of Data Science in Banking

The following are the most important use cases of Data Science in the Banking Industry.

1. Fraud Detection

Fraud Detection is a very crucial matter for Banking Industries. The biggest concern of the banking sector is to ensure the complete security of the customers and employees. Thus, the banks are searching for ways that can detect fraud as early as possible for minimizing the losses .

This is where Data Science is helping the Banking sector in achieving the necessary level of protection and avoiding financial losses.

Data Scientists can improve the level of customer security . This can be done by monitoring and analyzing the different banking activities of the customers so that they can detect any suspicious or malicious activity .

The major steps included in the Fraud Detection process are:

• Collecting a large number of data samples for training and testing the model.
• Training the model for making predictions.
• Testing the accuracy of the results and deployment.

Data Scientists need to have their hands on various data mining techniques like association, clustering, classification, etc. just for working with different datasets and extracting some meaningful insights that can be applied to real-time banking problems.

For example , let us consider a system that holds further transactions if suddenly a large number of transactions occur from a customer’s account until the owner of the account himself verifies them.

Such systems help the customers to keep an eye on their account activities.

2. Managing Customer Data

In today’s era of Big Data, banks have massive datasets to manage. Collecting , analyzing , and storing such an immense amount of data is difficult.

Thus, various banking organizations are using various tools and techniques from Data Science and Machine learning just for transforming this data into such a format that it can be used for knowing their clients better for devising new strategies for better revenue generation.

Nowadays, many terabytes of data are being generated every day because of the increasing popularity and usage of digital banking. The Data Scientists first apply several methods to separate the data which is useful for them. The analysis of this data helps them to gain insights about customer behavior , priorities , etc. This will help them to build efficient models that produce more accurate results.

Applying different Machine Learning algorithms can help banks to derive new opportunities for revenue generation and take some important data-driven decisions.

3. Risk Modeling

The identification and evaluation of risks is a matter of concern for the investment banks. To regulate different financial activities and deciding the right price for various financial instruments banks use Data Science in banking.

A. Credit Risk Modeling

This allows the banks to predict whether a customer will be able to repay their loan by analyzing the previous history and credit reports of the customer.

The credit risk analysis helps to calculate a risk score for each individual case. Then the bank decides whether to sanction the loan or not depending upon the risk score value.

B. Investment Risk Modeling

The investment banks use risk modeling for detecting risky investments. This will help them to give better investment advice to the customers and taking the right decisions for increasing profit. These are the reasons that make Risk Modeling so important for the banks.

Now using Data Science solutions, banking organizations are designing new strategies for effective risk modeling.It will help them to make better data-driven decisions .

4. Customer Lifetime Value Prediction

Customer Lifetime Value Prediction( CLV ) value refers to the predicted value of the net profit. It is a value that a business will gain from a customer during their entire relationship.

The banks employ different predictive analytic approaches to predict the revenue that can be generated from any customer in the future. This helps the banks in segregating the customers in specific groups based on their predicted future values.

Identifying customers with high future values will enable the organization to maintain good relationships with such customers. It can be done by investing more time and resources on them such as better customer care services, prices, offers, discounts, etc.

The most commonly used Data Science tools for this purpose are Classification and Regression Tree(CART) , Stepwise Regression and Generalized Linear Models(GLM) .

Finding and engaging reliable and profitable customers has always been a great challenge for banks. With the increasing competition, the banks need to keep a check on each and every activity of their customers for utilizing their resources effectively. To solve this problem, Data Science in banking is being used by the banks for collecting, cleaning, and analysis of the customer data for extracting actionable insights concerning customer behaviors and expectations.

Using Data Science models for predicting the CLV of a customer will help the organizations to take some suitable decisions for their growth and profit.

5. Customer Support

Providing effective customer support can help companies to engage their customers for a longer period of time.

Customer Support is also a very important part of Customer Services. Helping customers to use the different services provided by the bank can help the banks to have a better interaction with their customers.\ The various customer support services include replying to customer’s questions and complaints as early as possible for understanding your customers in a better way.

Data Science in banking is helping the Banking Industry to automate this service that will provide better and more accurate responses to customers and, will also help the companies to reduce their investment of time and money on the employees.

6. Customer Segmentation

Banks perform the activity of Customer Segmentation for dividing the customers into specific groups. The groups can be formed either on the basis of customer behavior that is called behavioral segmentation o r on the basis of some special characteristics of the customers that are called demographic segmentation .

The demographic segmentation might include factors like religion, gender, age, income, etc.

Customer Segmentation helps the banks to invest their time and resources accordingly. There are different Data Science techniques such as clustering, decision trees, logistic regression, etc . that can help banks. With these, they can predict the CLV for different segments of customers accordingly. Just for identifying high and low-value customer segments.

The customer segmentation helps the organizations to utilize their resources efficiently for increasing their sales by targeting specific customer groups. It is also used for providing better customer services and improving the loyalty of the customers.

7. Recommendation Engines

The key to success in any industry is offering those selected goods and services to the users which they really want.

Different Data Science and Machine Learning tools can help the industries to identify the most suitable items for the customers by analyzing customer activities.

The Data Scientists take all the user data from their previous search history , transaction history , profile data  to analyze them and then predict the most accurate items that might interest the users.

The recommendation engines can be built by using two algorithms. The first one is the collaborative filtering method that can be either customer-centric or item-centric . It evaluates the user behavior to provide recommendations to new users.

The second one is the Content-Based Filtering algorithm, it recommends the most similar items to the user that are inspired by the products. With which they interacted during their previous activities.

Any of the above methods can be used for building a recommendation engine according to your goals and circumstances.

8. Real-Time and Predictive Analysis

In the banking sector, each transaction of the user is a great source of data on which we can apply various analytical methods and derive some useful information to predict future events. Various Data Science and Machine Learning techniques are used for performing analytics in banking.

The increasing amount of data has generated an increased number of opportunities for the Data Scientists to decipher something useful from that data that can help a business.

There are basically two types of analytics used in banking:

• Real-time analytics enables banks to consider the current scenario and take action accordingly.
• Predictive analytics helps the banks to predict something about the future. We can say that it helps the bank to predict a problem that might appear in the near future and take suitable actions. Just to minimize its impact on the business.

Data Science in Banking Case Study

How yes bank used data science.

We all have heard the name of YES bank which is one of the leading private sector banks and currently it is India’s fourth-largest private sector bank. The reason behind the success of Yes Bank is that they always keep their customers in the first place.

Therefore from the past few years, it has made significant investments in the analysis of customer data to design customer-centric business policies. They are focusing on predicting future events that might have some impacts on the real-time business by making use of Data Science in banking.

The Yes bank has made considerable progress and has expanded its business in the past few years by setting up a separate team of Data Scientists for making some important data-driven decisions for providing the best possible services to their clients.

They use various advanced Data Analytics and Data Science techniques for extracting insights from the customer data collected through different sources.

The YES bank performed a Recency , Frequency and Monetary , that is, RFM analysis on the data of customers’ debit card usage so that they can provide a more personalized customer experience by providing targeted offers.

The results of this analysis were so overwhelming for the bank as it increased the average spend of an individual customer by 29% . While the spending of the targeted customers increased by 44% that resulted in an overall 27% growth in the portfolio spend.

For doing this analysis, the Data Science team of the bank developed a predictive model by using a large amount of customer data. This helped them to increase their sales and operational gains .

After exploring the different applications of Data Science in banking, we can say that Data Science is helping all the leading banking organizations. It helps in keeping up with the competition and providing better services to their customers.

Data Science in banking plays a crucial role in various banking activities like fraud detection, developing recommendation engines, providing efficient customer support services, etc.

47 case interview examples (from McKinsey, BCG, Bain, etc.)

One of the best ways to prepare for   case interviews  at firms like McKinsey, BCG, or Bain, is by studying case interview examples. 

There are a lot of free sample cases out there, but it's really hard to know where to start. So in this article, we have listed all the best free case examples available, in one place.

The below list of resources includes interactive case interview samples provided by consulting firms, video case interview demonstrations, case books, and materials developed by the team here at IGotAnOffer. Let's continue to the list.

• McKinsey examples
• BCG examples
• Bain examples
• Deloitte examples
• Other firms' examples
• Case books from consulting clubs
• Case interview preparation

Click here to practise 1-on-1 with MBB ex-interviewers

1. mckinsey case interview examples.

• Beautify case interview (McKinsey website)
• Diconsa case interview (McKinsey website)
• Electro-light case interview (McKinsey website)
• GlobaPharm case interview (McKinsey website)
• National Education case interview (McKinsey website)
• Talbot Trucks case interview (McKinsey website)
• Shops Corporation case interview (McKinsey website)
• Conservation Forever case interview (McKinsey website)
• McKinsey case interview guide (by IGotAnOffer)
• Profitability case with ex-McKinsey manager (by IGotAnOffer)
• McKinsey live case interview extract (by IGotAnOffer) - See below

2. BCG case interview examples

• Foods Inc and GenCo case samples  (BCG website)
• Chateau Boomerang written case interview  (BCG website)
• BCG case interview guide (by IGotAnOffer)
• Written cases guide (by IGotAnOffer)
• BCG live case interview with notes (by IGotAnOffer)
• BCG mock case interview with ex-BCG associate director - Public sector case (by IGotAnOffer)
• BCG mock case interview: Revenue problem case (by IGotAnOffer) - See below

3. Bain case interview examples

• CoffeeCo practice case (Bain website)
• FashionCo practice case (Bain website)
• Associate Consultant mock interview video (Bain website)
• Consultant mock interview video (Bain website)
• Written case interview tips (Bain website)
• Bain case interview guide   (by IGotAnOffer)
• Digital transformation case with ex-Bain consultant
• Bain case mock interview with ex-Bain manager (below)

4. Deloitte case interview examples

• Engagement Strategy practice case (Deloitte website)
• Recreation Unlimited practice case (Deloitte website)
• Strategic Vision practice case (Deloitte website)
• Retail Strategy practice case  (Deloitte website)
• Finance Strategy practice case  (Deloitte website)
• Talent Management practice case (Deloitte website)
• Enterprise Resource Management practice case (Deloitte website)
• Footloose written case  (by Deloitte)
• Deloitte case interview guide (by IGotAnOffer)

5. Accenture case interview examples

• Case interview workbook (by Accenture)
• Accenture case interview guide (by IGotAnOffer)

6. OC&C case interview examples

• Leisure Club case example (by OC&C)
• Imported Spirits case example (by OC&C)

7. Oliver Wyman case interview examples

• Wumbleworld case sample (Oliver Wyman website)
• Aqualine case sample (Oliver Wyman website)
• Oliver Wyman case interview guide (by IGotAnOffer)

8. A.T. Kearney case interview examples

• Promotion planning case question (A.T. Kearney website)
• Consulting case book and examples (by A.T. Kearney)
• AT Kearney case interview guide (by IGotAnOffer)

9. Strategy& / PWC case interview examples

• Presentation overview with sample questions (by Strategy& / PWC)
• Strategy& / PWC case interview guide (by IGotAnOffer)

10. L.E.K. Consulting case interview examples

• Case interview example video walkthrough   (L.E.K. website)
• Market sizing case example video walkthrough  (L.E.K. website)

11. Roland Berger case interview examples

• Transit oriented development case webinar part 1  (Roland Berger website)
• Transit oriented development case webinar part 2   (Roland Berger website)
• 3D printed hip implants case webinar part 1   (Roland Berger website)
• 3D printed hip implants case webinar part 2   (Roland Berger website)
• Roland Berger case interview guide   (by IGotAnOffer)

12. Capital One case interview examples

• Case interview example video walkthrough  (Capital One website)
• Capital One case interview guide (by IGotAnOffer)

12. EY Parthenon case interview examples

• Candidate-led case example with feedback (by IGotAnOffer)

14. Consulting clubs case interview examples

• Berkeley case book (2006)
• Columbia case book (2006)
• Darden case book (2012)
• Darden case book (2018)
• Duke case book (2010)
• Duke case book (2014)
• ESADE case book (2011)
• Goizueta case book (2006)
• Illinois case book (2015)
• LBS case book (2006)
• MIT case book (2001)
• Notre Dame case book (2017)
• Ross case book (2010)
• Wharton case book (2010)

Practice with experts

Using case interview examples is a key part of your interview preparation, but it isn’t enough.

At some point you’ll want to practise with friends or family who can give some useful feedback. However, if you really want the best possible preparation for your case interview, you'll also want to work with ex-consultants who have experience running interviews at McKinsey, Bain, BCG, etc.

If you know anyone who fits that description, fantastic! But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

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In the race to stay ahead, provide best-in-class customer service, and meet ever increasing market demands, banks have bolted on digital capabilities in an ad hoc manner introducing considerable complexity into the underlying IT architecture. Today, most traditional banks operate with a complex, unmanageable IT architecture with duplicate systems and data impeding speed-to-market for new products and services. Increasingly inflexible legacy systems have resulted in business silos and monolithic applications that hinder agility and adversely impact the pace of key transformation initiatives.

To stay relevant, incumbent banks must foray into areas beyond traditional banking spaces by stepping into their customers’ lives at the right time with the right product. This will require banks to embrace purpose-driven business models through new partnerships with larger ecosystem partners, which in turn will require architectural readiness and plug-and-play integrations to enable ecosystem play. While banking channels are increasingly adopting digitalization to deliver beyond banking services, pain points around legacy architecture remain. In our view, to address these pain points, transition to purpose-driven ecosystem models and become future-ready, banks must adopt the Banking Industry Architecture Network (BIAN) standard. This article focuses on BIAN adoption trends and journeys.

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Case Studies

ArchiMate’ Modeling Notation for the Financial Industry Reference Model: Banking Industry Architecture Network (BIAN)

This document provides guidance on how the ArchiMate Specification, a standard of The Open Group, can be used to exploit the value of the Banking Industry Architecture Network (BIAN) Financial Industry Reference Model.

It is designed to provide a guide to anyone involved or interested in how to manage the transition to a digital financial institution. It guides an Enterprise Architecture organization to develop an agile, lean, and stable banking architecture using the ArchiMate language and BIAN.

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Archi Banking Group: Combining the BIAN Reference Model, ArchiMate’ Modeling Notation, and the TOGAF’ Framework

This Case Study is a fictitious example developed to illustrate the combined use of the Banking Industry Architecture Network (BIAN) Reference Model with the ArchiMate’ modeling notation and the TOGAF’ framework (both standards of The Open Group). The ArchiMate and TOGAF concepts used in this Case Study can be applied to different situations. The use of the BIAN Reference Model supports addressing typical financial industry concerns.

PNC Financial Services Group

The BIAN model fits perfectly in line with how we view enterprise architecture (EA) at PNC. One of the first steps we took as an organisation was to bring a business perspective to enterprise architecture. To us, technology is not just a collection of servers and software, but rather a set of technical solutions that are aligned to specific business capabilities and functions. Steven Van Wyk, Executive Vice President, Head of Technology and Operations, PNC Financial Services Group Read case study

Cognizant Technology Solutions, led by Sanghosh Bhalla, Niloy Sengupta and Akshaya Bhargava from the firm’s Banking and Financial Services Consulting practice, recently helped a top three North American bank, adopt BIAN and optimize their enterprise portfolio of applications that support business functions across all of its business units. Sanghosh Bhalla, Niloy Sengupta and Akshaya Bhargava, Cognizant Read Case Study

As a vendor that grew through acquisitions, we inherited a rich collection of applications that have their application specific interfaces. Applications that had similar scope ended up having their specific interfaces for essentially the same responsibilities. One of our strategic goals was to cut integration time and cost and over time achieve plug-and-play interoperability between different applications in our portfolio. Aleksandar Milosevic, Chief Software Architect at banking software provider Asseco SEE Read case study

TymeBank Case Study: The Customer Impact of Inclusive Digital Banking

Full report.

This publication is also available in French  and Spanish .

Executive Summary

This case study presents insights from customer research with TymeBank clients that bolsters CGAP’s hypotheses around how digital banks can support the mission of financial inclusion. As a fully digital South African bank that disproportionately serves low-income rural customers, TymeBank has created a suite of basic products that cater to the essential financial needs of those customers, namely a low-cost transactional account and a high-yield savings account. Judging from product uptake and client testimonials, these products add to a compelling value proposition that not only resonates with customers but improves their lives.

TymeBank’s distribution network, which is based on its partnerships with the nationwide Boxer and Pick n Pay (PnP) grocery store chains, helps to keep operational costs low and passes cost savings onto customers in the form of more affordable services. A clear majority of the bank’s customers cite affordability as a key source of value and the reason they opened a TymeBank account. The distribution network also extends the bank’s reach to areas that are underserved by traditional players. The affordability and accessibility likely explain why underserved segments, such as low-income women and rural customers, are over-represented in TymeBank’s (active) customer base as compared to the overall banked population in South Africa.

Despite having access to other banking options, TymeBank customers overwhelmingly see no compelling alternatives in the market. Crucially, the value customers see in the bank appears to be inversely related to income, with poorer customers reporting higher levels of satisfaction.  

In today’s high-tech financial services landscape, which is often dominated by headlines about fintech startups and tech giants, it is easy to overlook the role banks can play in advancing financial inclusion. The high cost of running brick-and-mortar branch networks has traditionally inhibited banks from serving less profitable client segments, including the low-income groups that are the focus of financial inclusion. Banks have also been slow to adapt the digital innovations that have helped some newcomers reach these segments at lower cost. It is no surprise that some observers have questioned whether banks are even relevant to financial inclusion.

However, there are reasons to believe that banks can play an important role in financial inclusion if they overcome the challenges of their legacy systems and processes and digitize operations. In fact, banks have advantages over other types of financial services providers (FSPs) that may allow them to have an outsized impact on financial inclusion – if they are willing to expand down- market. Most importantly, banks do not face the same regulatory constraints as other providers. Whereas mobile money providers and fintechs generally cannot provide a wide array of financial products (ranging from savings to credit), banks can. License to intermediate retail deposits further plays to a bank’s advantage in the arena of digital credit. Banks can fund their lending portfolios with retail deposits that are typically cheaper than the other funding sources pure lenders use, which further reduces the cost of reaching low-income customers with credit.

CGAP previously presented three emerging business models in banking that we consider to be particularly promising for financial inclusion (Jeník and Zetterli 2020). These models are fully digital retail banks, marketplace banks, and Banking-as-a-Service (BaaS) (see Box 1). We conclude that they have the potential to deepen financial inclusion by:

• Lowering the cost of financial services; 
• Improving access to a greater variety of services;
• Creating services that better meet the needs of various customer segments; and 
• Improving the customer experience. 1

We analyzed several fully digital retail banks in a series of detailed case studies (Jeník, Flaming, and Salman 2020). One of these cases focused on TymeBank in South Africa. TymeBank is a fully digital retail bank founded with financial inclusion as a core business objective. Since its 2018 launch, the bank has onboarded over 4 million customers.

TymeBank offers low-income customers simple products at low prices, such as checking accounts, savings accounts, and debit cards – all through a distribution network that combines online and offline customer interaction based on partnerships with grocery store chains Boxer and PnP. In the area of credit products, TymeBank only offers a “buy now, pay later” option called MoreTyme. This case study provides a compelling example of how challenger banks can leverage digital technology to reach excluded customer segments with more affordable and useful products.

This paper builds on the TymeBank case study by examining the impact the bank’s services have had on low-income customers. By combining a quantitative analysis of TymeBank customer data with a phone-based survey of a randomly selected sample of low-income customers, the paper addresses the following questions:

• Does TymeBank serve low-income customers?
• Are its products relevant to low-income customers?
• What impact do the bank’s products have on low-income customers’ lives, in their own words?

The aim of this research is to shed light on the potential of digital banks to deepen financial inclusion in a way that improves the lives of low-income customers. CGAP is conducting additional research with other providers to better understand the impact of new financial services business models on customers. 2

TymeBank’s main value proposition consists of (i) simple, affordable, and accessible products; (ii) fast and automated onboarding; and (iii) incentive programs that appeal to target segments (e.g., the SmartShopper loyalty program). These are the qualities we would expect customers to point out when talking about the benefits of using TymeBank.

They are also important features that respond to three frequently cited barriers to financial inclusion: (i) expensive services, (ii) limited access points, and (iii) prohibitive know-your-customer (KYC) requirements. 3

Product affordability relies on TymeBank’s ability to maintain low operational costs and proportionally reduce them further as the bank grows. Current cost efficiency is due to the bank’s technology and microservice architecture (Flaming and Jeník 2020), its branchless model, and digitally facilitated onboarding. TymeBank onboards approximately 110,000 customers per month: about 93,500 through kiosks at an estimated cost of US$3 per customer, and about 16,500 via web at approximately US$0.60 per customer. 4

SOUTH AFRICA 5

South Africa enjoys relatively high levels of financial inclusion, including a banked adult population of approximately 85 percent in a market dominated by the country’s well-established commercial banks. However, many customers only use their bank account to receive government benefits; other use cases lag. There is little to no use of non-bank mobile money wallets.

Across demographic, socioeconomic, and geographic factors, financial inclusion levels positively corelate with higher age (people aged 18–29 are among those least included), urban areas, income level and regularity. Only 38 percent of individuals who reported having no income are banked, while 31 percent are entirely excluded.

METHODOLOGY

For the qualitative analysis based on customer interviews, 1,162 customers were screened from an overall sample of 10,000. The aim was to reach those TymeBank customers living in poverty (i.e., 70 percent or more likely to be living on less than US$5.50). Ultimately, 278 customers were identified for in-depth interviews. The screener surveys were conducted partly through interactive voice response (IVR) surveys and partly through live phone calls.

The quantitative analysis used customer data from TymeBank to assess the potential impact of the bank’s offering on its customer base, particularly individuals from groups that generally exhibit lower levels of financial inclusion. The data examined spanned a nine-month period from July 2020 to March 2021. The analyzed data correlated to active EveryDay account customers, defined as those who had performed a transaction within the past 30 days. Various sets of proxies were applied to estimate income level (e.g., onboarding location, outstanding balance, frequency of transactions, average size of transactions).

The analysis considered several important caveats:

a) We recognize that TymeBank is not representative of all fully digital retail banks in South Africa or elsewhere. The findings presented in this paper should not be interpreted as automatically applicable to other digital banks without careful consideration.

b) The research was conducted during the COVID-19 pandemic; some findings were or could be affected (e.g., as customer behavior changes in response to the pandemic).

c) Despite our best efforts to exclusively focus the analysis on low-income segments, we were unable to identify customers based on their stated income levels since TymeBank does not collect that information. Customer segmentation was performed through the previously mentioned set of proxies for the customer data analysis and through the screening questionnaire for the customer interviews. 6

d) The quantitative analysis focused on active customers with at least one transaction performed over the past 30 days, unless otherwise noted.

e) Where customers stated they had been financially excluded before opening a TymeBank account, we did not identify the underlying cause(s) of financial exclusion.

Key Findings

Does tymebank serve low-income customers.

Our research showed that TymeBank serves a higher proportion of low-income customers than the typical bank in South Africa, and a significantly higher portion of the most financially excluded segment.

Low-income customers in South Africa are relatively highly banked, although they are under-represented. South Africans earning US$200 per month or less constitute 47 percent of the population but only 41 percent of the banked population. 7 However, we estimate that this segment represents 48 percent of TymeBank’s active user base. 8

Among the three-quarters of TymeBank customers for whom data are available, 58 percent live in metropolitan areas and 42 percent in rural areas. This compares to South Africa’s rural population of 35 percent (as of 2016); we estimated this share to be even lower in 2021 (approximately 30 percent). 9 Hence, rural customers appeared to be noticeably overrepresented in the TymeBank user base.

Young, rural, low-income women comprise the most financially excluded and underserved segment in South Africa. This group forms 2.3 percent of South Africa’s banked population but 7 percent of TymeBank’s active base – nearly three times as much. 10 Finally, 13 percent of TymeBank’s active customers are first-time bank customers. 11

From a more general perspective, women in the low-income segment represent a higher-than- average share of the bank’s overall customer base sample (65 percent versus 57 percent),12 which suggests that low-income women particularly benefit from TymeBank’s services.

These findings lead us to conclude that TymeBank customers disproportionately seem to come from traditionally unbanked and underserved segments. In fact, the evidence suggests that the bank’s customer base may particularly skew toward the most underserved segments.

DOES TYMEBANK OFFER PRODUCTS THAT ARE RELEVANT TO LOW-INCOME CUSTOMERS?

Customers find TymeBank’s products useful and act upon features designed to promote certain behaviors.

The bank’s customers particularly value the low cost of its services and the convenience of access and usage. The lower their income, the more value customers seem to derive from its services. While the vast majority of TymeBank customers have previously held bank accounts, 67 percent say they see no good alternative to TymeBank (Figure 4). This response is despite the fact that, as of the time the research was conducted, the bank still only had a relatively modest payments and savings offering and had yet to launch credit products. (TymeBank has since launched MoreTyme, a “buy now, pay later” consumer credit product.) Customer endorsement seems driven by the strength of the bank’s value proposition and the low cost of its services. When asked, customers specifically appreciate the low fees (48 percent) and the high-yield savings account (38 percent).

Importantly, women make up a larger share of the total number of GoalSave (savings account) users compared to their representation in the overall customer base (3 percentage points higher). This finding suggests that female customers find value in the product, although they had slightly lower savings per user than men (US$58 versus US$59). The number of their deposits exceeds the number of withdrawals.

We did not find any significant differences in usage and product lifecycle patterns across income groups (aside from the frequency and size of transactions that correlate with income level), which suggests that TymeBank covers its customers’ essential needs across segments. The similarities in lifecycle (behavior patterns across products, such as most frequently performed type of transaction and their change over time) indicate that customers across income levels increase their engagement as they grow confident with the products.

However, important nuances do exist. For instance, the most excluded segment uses till machines for cash-in and cash-out transactions that are free-of-charge (and perhaps more accessible in certain areas), compared to the ATMs other segments prefer. This may be explained by price sensitivity that drives the preference for free till point withdrawals compared to ATM withdrawals, which are charged at US$0.61 per part of US$70.

The value generated for low(er) income customers will hopefully further expand as TymeBank expands its product offering (e.g., insurance and diverse credit products).

WHAT IMPACT DOES TYMEBANK HAVE ON CUSTOMERS’ LIVES?

Most customers report positive life changes due to their use of TymeBank. Importantly, levels of customer satisfaction increase as customer income decreases. This suggests that the TymeBank value proposition tailored to lower-income customers resonates well.

We relied on the actual voices of customers from the demand survey to gauge the impact the TymeBank offering had on its users. When asked, 73 percent of customers reported a positive change in quality of life attributable to TymeBank. The change could be associated with multiple factors. For instance, 80 percent of interviewed customers reported a decrease in the amount spent on bank fees, which is crucial for low-income segments that have historically experienced cost as one of the biggest barriers to financial inclusion. Nearly a third (31 percent) of customers who reported life improvement said that their access to financial services had expanded thanks to TymeBank. Customers also reported an improved ability to digitally transact and receive money (51 percent and 55 percent of all interviewees, respectively).

One of the most important findings concerned the ability to save. Seventy-three percent of interviewed customers reported an increase in their savings balance due to TymeBank. Savings likely drove customers’ ability to achieve their financial goals (68 percent) and improve financial resilience (32 percent).

These findings support our overall hypothesis that digital banks are well placed to deepen financial inclusion with cheaper, better products that reach beyond payments and are relevant to improving the lives of low-income customers.

It is critical to note that the high-interest yield on the GoalSave savings account was among the reasons most prominently cited by customers as driving them toward TymeBank. Our finding that female and young TymeBank customers were more likely to save using the bank service compared to what nationwide averages suggest was also important. While the national numbers show a 9 percentage point gap in formal savings between men and women (35 percent versus 26 percent), the gap among TymeBank customers favored women by 10 percentage points (45 percent versus 55 percent).

Our findings also revealed areas for improvement. Perhaps not surprisingly, TymeBank customers have not been spared the surge of fraud in South Africa. Ten percent of customers reported challenges concerning security and protection of funds. Six percent of respondents mentioned delays in service delivery and nearly the same share complained of issues related to digital access. Complaints were related to system downtime, clearing time (TymeBank is planning to offer real-time clearing), and the general concerns first-time users may have about their funds.

When asked about potential improvements, the presence of physical branches scored the highest (11 percent), followed by improved security (9 percent) related to the challenges mentioned in the previous paragraph and improved digital services (5 percent).

While these findings are encouraging, more research is needed before conclusive statements can be made about the broader role of digital banks in advancing financial inclusion. We encourage other experts to undertake similar research and add to the emerging evidence on the impact of digital banks on financial inclusion.

Acknowledgments

This case study features insights from research commissioned by CGAP and conducted by 60 Decibels and Genesis Analytics under the leadership of Ivo Jeník.

The author thanks CGAP colleagues Gayatri Vikram Murthy and Mehmet Kerse for reviewing this paper, and Gcinisizwe Andrew Mdluli for contributions and insights. Peter Zetterli and Xavier Faz oversaw the effort. Andrew Johnson led the editorial work.

This paper would not have been possible without the time and dedication of the team from TymeBank and TymeGlobal.

Flaming, Mark, and Ivo Jeník. 2020. “ How Does Tech Make a Difference in Digital Banking ?” CGAP blog post, 11 November.

Jeník, Ivo, Mark Flaming, and Arisha Salman. 2020. “ Inclusive Digital Banking: Emerging Markets Case Studies .” Working Paper. Washington, D.C.: CGAP.

Jeník, Ivo, and Peter Zetterli. 2020. “ Digital Banks: How Can They Deepen Financial Inclusion? ” Slide deck. Washington, D.C.: CGAP.

Download a PDF of this Case Study >>

1 To assess bank inclusivity, we developed and implemented a four-dimensional framework focused on cost, access, fit, and experience (CAFE). See Jeník and Zetterli (2020), page 42. In a business-to business (B2B) model, BaaS providers have other FSPs as their customers. Thus, their impact on end users is indirect.

2 see collection of cgap research on fintech and new financial services business models: www.cgap.org/fintech, 3 world bank global findex database (2017)., 4 atm-like machines placed in partner grocery stores – mainly pnp and boxer – allow for automated customer onboarding in less than five minutes., 5 this section is based on data from the finmark trust finscope (south africa) 2018 database., 6 the quantitative analysis used the average monthly inflows of customers originated at pnp value stores (us$271) and boxer stores (us$224) to estimate income level. the qualitative analysis estimated that 35 percent of tymebank’s customers live on less than us$5.50 per day, based on the screener survey findings., 7 the finmark trust finscope (south africa) 2018 database., 8 using place of origination (pnp value and boxer stores) as a proxy for low income., 9 south africa gateway .  , 10 the finmark trust finscope (south africa) 2018 database., 11 n = 1,162., 12 comparing screened customers (n = 1,162) and interviewed customers (n = 278)., related resources, inclusive digital banking: emerging markets case studies, digital banks: how can they deepen financial inclusion, related research, 8 billion reasons: inclusive finance as a catalyst for climate action, open finance self-assessment tool and development roadmap, global landscape: data trails of digitally included poor (dip) people.

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Open banking. The art of the possible in 10 use cases.

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10 open banking uses cases 

How can financial services companies drive real value and service clients better through open banking ? A new report by Altitude Consulting and Axway details a number of use cases to help you visualize the art of the possible in open banking and imagine new ways to grow your organization’s capabilities, gain a competitive edge, and deliver superior customer experiences. Here are 10 of them:

No. 1: Account aggregation 

Open banking offers secure APIs for accessing financial account data and benefits all parties: banks, account owners, and fintechs. With open banking, banks have greater control and visibility into third parties that are accessing their clients’ financial data and the purpose for which the data is being used.

No. 2: Consumer spending insights 

Financial documents and data from external credit and bank accounts can bridge the gap between banking and buying, uncovering opportunities for banks to proactively engage customers. Customer life stages, psychographics, brand loyalties and other triggers help to create a personalized experience.

No. 3: Buy now, pay later 

BNPL (By Now Pay Later) fintechs use the banking systems to verify consumers’ identity, retrieve financial information, verify accounts, conduct KYC checks and make lending decisions. Consent management allows consumers to grant access to data for loan applications and credit approvals which improves conversion rates.

No. 4: Wealth management 

Wealth management with open banking capabilities makes serving customers more efficient. Customer permission to retrieve data from external accounts, and conducting a digital KYC, smooths onboarding for new and existing customers looking to take advantage of the bank’s financial planning services.

No. 5: Insurance Sales 

Banks can analyze customer data for insurance triggers to provide personalized insurance offers through online and mobile channels. Such triggers are found in personal information updates, purchasing data from external cards aggregated through open banking, and location data from a bank’s mobile app.

No. 6: Personalizing products and experiences 

Open banking allows banks to combine data from external sources, including other banks, brokerages, credit issuers and investment managers, and combine it with first party data such as buying history, credit score, income, age and location, to build a deep understanding of each client’s unique needs.

No. 7: Bill payment and management 

Improved billing and payment capabilities offer customers more payment options and increase customer engagement. Open banking APIs let billers send bills directly through the bank’s channels and use detailed consumer behavior data to deepen loyalty and offer the right product at the right time.

No. 8: Tax preparation 

Open banking provides a number of tax preparation benefits. With a direct connection from bank systems to popular accounting platforms to retrieve tax data, banks will realize operational efficiencies, be able to serve consumer and business clients better, and reduce call volumes during tax season.

No. 9: Payment initiation 

Open banking lets consumers and corporations initiate payments directly from their online banking accounts without entering routing data, simplifying the payment experience. Multiple payment options are available within the same payment interface and payments are often processed in minutes or hours.

No. 10: Streamlining account opening 

With open banking, digital ID verification and cross- referencing with external accounts reduces manual input by the user, speeding account opening. Funding is done directly from another bank account via ACH transfers. Virtual cards allow new customers to make purchases as soon as the account is funded. * Download this open banking checklist as a pdf

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Case scenarios about holding funds

Publication date: August 21, 2024

The following fictional case scenarios provide examples to help providers identify whether they perform the payment function of holding funds.

These examples are intended to demonstrate when a payment service provider (PSP ) is performing the payment function of “holding funds on behalf of an end user until they are withdrawn by the end user or transferred to another individual or entity” (“holding funds”). Certain requirements under the Retail Payment Activities Act (RPAA ) relate only to PSPs that hold end-user funds, such as the requirement to safeguard those funds in accordance with section 20 of the RPAA . For information on the requirement to safeguard end-user funds, please consult the Bank’s guideline: Safeguarding end-user funds .

Note that even if an entity is not holding funds, it may still perform other payment functions as defined in the RPAA and need to register with the Bank of Canada, assuming it meets the other registration criteria.

Case scenario: Electronic money wallet and holding funds

Company A is a payment service provider (PSP ) offering an electronic wallet (“e-wallet”) that its customers can use to load and store funds using an online application. Once this is done, the customer can use the e-wallet to make purchases at online retailers that accept the e-wallet as a means of payment. Customers can also transfer funds to other individuals that use Company A’s e-wallet.

Customers can load funds into their Company A e-wallet, for example, by using a debit card or by linking a bank account with the e-wallet application. In these instances, customers load their e-wallets by transferring funds from their bank accounts into Company A’s bank account at its financial institution, and Company A then credits the funds balance on the e-wallet application.

In this example, Company A is considered to be holding funds as defined in the RPAA , because it offers e-wallets that enable customers to keep funds at rest in Company A’s possession and control. The funds remain available to the customer until they request a future transfer or withdrawal.

Company A begins holding funds as soon as it receives them from an end user. It stops holding funds on behalf of that end user when it receives an instruction to transfer all of these held funds to one or more end users. Even though the funds may be held in a bank account at Company A’s financial institution, the account is in Company A’s name and it is Company A that is indebted to its customers for those funds. As a result, it is Company A, not its financial institution, that performs the payment function of holding funds.

Accordingly, Company A must comply with the end-user funds safeguarding requirements in the RPAA , as described in the Bank’s Safeguarding end-user funds guideline.

Case scenario: Money transfer business and holding funds

Company B is a PSP that specializes in making domestic and cross-border funds transfers for individuals and businesses. Company B permits its customers to make only immediate transfers and does not allow customers to maintain a balance to fund future transfers.

When carrying out a cross-border transfer, Company B must complete several steps, including regulatory and compliance checks (e.g., for fraud or anti-money laundering purposes), and coordinate with its partners in the country where the funds are being transferred. These steps are necessary for payees to receive funds in their local currency deposited directly into their bank accounts. Given differing time zones and the number of parties involved in this process, once Company B receives funds from a customer, it typically takes 48 to 72 hours before those funds become available in the payee’s account abroad.

In this example, Company B is not holding funds as defined in the RPAA . To be holding funds, the funds must be at rest with the PSP and available for future transfer or withdrawal. In this case, Company B may be in possession of end-user funds for multiple days while it processes a transfer; however, the funds are not available for a separate transfer or withdrawal. Rather, the funds are subject to an instruction for immediate transfer for the entire time they are in Company B’s possession.

Case scenario: Money transfer business and holding funds before a transfer

Company B has launched a new product, which introduces the ability for customers to pre-fund their planned money transfers. In effect, Company B customers can opt to have an account that maintains a balance and allows them to schedule transfers at a future date. As such, instead of funds transfers taking place immediately, a customer can now, for example, instruct Company B to transfer the funds 10 days in the future. These transfers can be modified until the date the transfer is scheduled to take place, and customers have the option to retrieve their funds if they decide the transfer should not proceed. To make the transfer, the customer credits Company B with the funds using a debit card, along with the transfer instruction.

In this example, Company B is holding funds as defined in the RPAA . When transfers are future-dated the funds are at rest and can be transferred or withdrawn by the customer until the time of the future-dated transfer. Company B begins holding funds as soon as it receives the funds. The holding of funds continues until the date of the transfer, as the funds are not in transit until Company B begins processing the transfer.

Accordingly, Company B must comply with the end-user funds safeguarding requirements in the RPAA , as described in the Bank’s Safeguarding end-user funds guideline.

Case scenario: Intermediary PSP and holding funds

A Company B customer wishes to send money to a friend in Country X. The customer goes on Company B’s website and requests an immediate transfer of funds to be sent to their friend. However, Company B does not itself have the presence nor the payment infrastructure to send funds to Country X. To fill this gap, Company B has a business relationship with PSP C to be able to offer services to users that need to send funds to Country X.

Upon receipt of the transfer request and the corresponding funds from their customer, Company B instructs PSP C to take the next steps required to move forward with the international money transfer. In particular, PSP C must ensure it has sufficient funds available in the local currency of the foreign jurisdiction. In addition, it needs to exchange payment instructions with PSP D, a PSP located in Country X with which the recipient has an existing account, so the funds can be credited to the recipient. While those steps are automated, they involve some processing time, and the funds ultimately take three days after the date of the transfer request to reach the recipient account.

In this example, Company B is not holding funds on behalf of its customer by virtue of the immediate transfer. The funds are not held at rest with Company B since they are immediately processed for transfer by instructing PSP C to carry out the international transfer.

Similarly, the funds are not at rest and available for future transfer or withdrawal while in PSP C’s possession. Additionally, PSP C in this example is an intermediary party whose only role in the transaction is to support the flow of funds from one PSP to another. Consequently, PSP C could not be considered to be holding funds for the end user, such as Company A’s client, as it does not have any contractual relationship with them to hold their funds on their behalf. In this example, PSP C has a contractual relationship with only Company B and does not have a direct relationship with the payer or payee to hold funds on their behalf.

As a result, neither Company B nor PSP C hold funds in this example.

The case scenarios are illustrative examples reflecting the Bank of Canada’s interpretation of certain requirements set out in the Retail Payment Activities Act (RPAA) . All names, facts and descriptions in these scenarios are entirely fictitious and do not reflect any real or actual individuals or entities.

Additionally, they do not represent legal advice and should not be used as a replacement for seeking such advice if an individual or entity is unsure about whether they are required to register with the Bank of Canada as a payment service provider. The nature of the products and services offered by each individual or entity will vary, as will the circumstances around offering these products and services. Therefore, any individual or entity that may be subject to the RPAA should assess their own situation on a case-by-case basis according to their own facts and circumstances. Any entity or individual that may be subject to the RPAA is ultimately responsible for determining whether they are required to register with the Bank.

The examples provided are not a replacement for the Criteria for registering payment service providers supervisory policy, but rather they are meant to complement the policy. They should be read in conjunction with the policy.

On this page Table of contents

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• Open access
• Published: 20 August 2024

Assessment of multi-professional primary healthcare center quality by patients with multimorbidity

• Antoine Dany 1 , 2 ,
• Paul Aujoulat 1 , 3 ,
• Floriane Colin 1 , 3 ,
• Jean-Yves Le Reste 1 , 3 &
• Delphine Le Goff 1 , 3  

BMC Health Services Research volume  24 , Article number:  954 ( 2024 ) Cite this article

73 Accesses

Metrics details

The main aim of this study was to build an item bank for assessing the care quality of multi-professional healthcare centers (MPHCC) from the perspective of patients with multimorbidity. This study was part of the QUALSOPRIM (QUALité des SOins PRIMaires; primary healthcare quality) research project to create a psychometrically robust self-administered questionnaire to assess healthcare quality.

First, twelve experts built an item bank using data from a previous qualitative work and a systematic literature review. Second, the validity of each item was assessed in a sample of patients. Adult patients with multimorbidity were recruited from six French MPHCC. Items were assessed based on ceiling effects, the level of missing or neutral responses and patient feedback. Patient feedback was recorded after the item bank completion. Based on results, items were validated, improved, or removed during expert meetings. In case of disagreement the Delphi method was used to reach consensus.

The study sample included 209 outpatients. The most frequent medical conditions were cardiovascular risk factors, cardiovascular diseases and rheumatological conditions. In total, a bank of 109 items classified in nine domains was built. The validity assessment led to the removal of 34 items. Retained items explored a variety of topics related to care quality: availability, accessibility, premises’ layout and building, technical care, expertise, organization, relationships with caregivers and communication, involvement and personal relationships.

Conclusions

This study allowed cross-validation of a bank of 75 items, leading to a complete picture of the patient perception of care quality items. Overall, patients were generally satisfied with their care at the MPHCC. Nonetheless, there were still numerous items on subjects for which patients’ satisfaction could be improved.

Peer Review reports

Introduction

Patients with multimorbidity have one chronic disease and at least another (acute or chronic) disease, a biopsychosocial risk factor and/or a somatic risk factor. They often experience complex healthcare interactions [ 1 ]. To meet their increasing care needs, healthcare systems are shifting to a more patient-centered and comprehensive approach with increasing numbers of multi-professional healthcare centers (MPHCC) [ 2 , 3 , 4 , 5 ]. Patient-centered care, including user experience, quality of care and outcomes, can help to produce high-quality healthcare systems [ 6 ]. Healthcare quality can be measured to improve patient-centered care and healthcare quality [ 7 , 8 ].

Primary healthcare provides integrated, accessible healthcare services by clinicians who address most healthcare needs by developing a partnership with patients, and by practicing in the context of family and community [ 9 ]. Healthcare quality can be evaluated from the patients and healthcare professionals’ (HCP) perspective. Independent medical evaluation is the gold standard approach to assess healthcare quality from the HCP perspective. To assess healthcare quality from the patients’ perspective, two approaches can be used: (i) patient experience that reflects the patients’ perception of the received care, and (ii) patient satisfaction that reflects the gap between the quality of the received care and the personal expectations [ 10 , 11 ]. The patient perspective allows assessing the patient centeredness of care, which is a feature of high-quality care, like safety and efficacy [ 12 ].

A recent systematic review revealed that many self-assessment instruments are available to measure care quality at MPHCC from the perspective of patients with multimorbidity. These instruments capture many patient experiences, but few have strong psychometric properties. This review highlighted the need of a valid, responsive, reliable and robust instrument to assess and improve the quality of primary care [ 13 ]. Therefore, the aim of the QUALSOPRIM (QUALité des SOins PRIMaires; primary healthcare quality) project was to build a new evaluation tool with robust psychometric properties. First, a qualitative study was performed using in-depth, face-to-face interviews with 26 patients, 23 informal caregivers and 57 HCPs from five MPHCC in France. This study showed that patients, informal caregivers, and HCPs shared a common vision to improve primary care quality. Nine core domains for care quality were identified [ 14 ].

The main aim of the present study was to build an item bank for MPHCC care quality assessment by patients with multimorbidity.

Item bank construction

Twelve experts [two general practitioners (GP), a psychometrician, and seven GP trainees] developed the bank of items that were formulated according to the Question Appraisal System-1999 guideline [ 15 ]. Response options were phrased during expert meetings. To formulate the response options experts were instructed to assign an objective quantity to each response option (e.g. numbers or common situations that patients could easily relate to).Item response options were designed to ensure the possible range of responses would be captured. The maximum number of response options was set at five.

The item bank included questions on patient experience and satisfaction (available as supplementary file). As healthcare quality is a multi-dimensional concept, items were classified into several domains on the basis of previous phases of the QUALSOPRIM project, clinicians’ experience at a MPHCC and psychometric model requirements.

Validity assessment of the item bank

Validity assessment was conducted in Brittany, a region in Northwest France. It started in the summer 2019 and was planned to last 1 year. The assessment goal was to validate, improve or remove items. Face validity and psychometric dysfunction were assessed to reduce the item bank size. Indeed, reducing the item bank size will facilitate all subsequent work.

Inclusion criteria

Patients (≥ 18 years of age) with at least two chronic conditions, and followed by at least two HCPs were recruited. Purposive sampling was used to ensure that enough patients had home care and an informal caregiver so that related items could be assessed. At least 25% of recruited patients needed to have an informal caregiver and 50% were to have home care to assess corresponding items. An investigating physician identified the eligible patients at the MPHCC. Patients were included if they were able to express their informed consent and signed a written consent. Patients were anonymized and received an ID number. The experimental protocol was approved by the ethical research committee (Comités de Protection des Personnes SUD-EST IV) and was categorized as observational.

GP trainees underwent training on the item bank construction methodology and the required administrative tasks. A GP trainee met the included patients at their MPHCC or their home to explain how to assess the item bank. Patient data were collected: age, sex, and care details (duration, frequency, HCP type, place of care), current medical conditions. Each patient completed a paper version of the item bank. The GP trainee could provide help for the first ten items, if needed. Patients with an informal caregiver could complete the related domain items with their help. Each answer was scored from 1 (total disagreement) to 4 (total agreement); neutral answers (unconcerned or did not mind) were scored zero. Following the item bank assessment completion, patients participated in an open discussion with the GP trainee who included them in the study. The aim was to evaluate how well they understood the item bank and to detect problematic items (e.g. unknown words or irrelevant answers). Patients were encouraged to be uncompromisingly honest and to highlight all difficulties, inconsistencies, or misunderstandings. Patients could suggest improvements and judge the overall item bank acceptability.

Patient feedback was recorded in an Excel spreadsheet and was used to propose adaptations or improvements for each item in a meeting with the same expert group that constructed the item bank. In case of disagreement during these meetings, the Delphi method was later used to reach a consensus (> 80% agreement), one to four rounds were planned [ 16 ].

The scores for each item were recorded manually and independently in two Excel spreadsheets by two GP trainees. The two spreadsheets were automatically compared to identify mismatches. For each mismatch, the two GP trainees entered the definitive value in a third spreadsheet, after discussion. After the last validation by all investigators, the final data file was frozen.

Items were considered to have a poor face validity if they had either a low response rate (missing answer rate > 10%) or relevant negative patient feedback. Items were considered to have psychometric dysfunction if they had either a high percentage (> 75%) of neutral/unconcerned responses or a floor/ceiling effect (> 90% of most negative or most positive answers).

Items with negative feedback, poor face validity and/or psychometric dysfunction were improved if possible or removed from the item bank.

The main results and their integration in the whole research project are presented in Fig.  1 .

The main results of the present paper are within the dashed rectangle. Results from previous QUALSOPRIM studies used in the present paper are above it. The next phase of the QUALSOPRIM project is below. QUALSOPRIM: QUALité des SOins PRIMaires; primary healthcare quality

Item drafting

In total, 109 items were included in the item bank in the form of a self-report questionnaire. Each item was given 3 to 5 ordered response categories (Likert scale).

Classification in domains

The initial version of the item bank had ten domains: nine core domains and a general satisfaction domain. The nine core domains were: (i) “HCP availability”, (ii) “Care accessibility”, (iii) “MPHCC layout”, (iv) “Medical-technical care”, (v) “GP’s expertise”, (vi) “Care organization within the MPHCC”, (vii) “Patient-HCP relationship and communication”, (viii) “Patient’s role in their care”, and (ix) “Main informal caregiver’s role in the care pathway”. Each domain included 6 to 18 items. The general satisfaction domain included three items and was at the end of the item bank. It will serve as a point of comparison for later-stage psychometric assessments.

The item bank is available as supplementary material.

In total, 209 patients (60% of women) were included at six MPHCC from July 26, 2019 to October 29, 2020. Their characteristics are presented in Table  1 . Their age ranged from 29 to 99 years (median = 74 years) and was between 67 and 83 years in 53% of patients. Almost half of patients had been followed by their current GP for > 10 years, exclusively at the MPHCC or at home and at the MPHCC. Most patients were seen every 3 months, mainly by their GP and nurses. The most frequent medical conditions were cardiovascular risk factors, cardiovascular diseases and rheumatological conditions.

The time needed to complete the item bank assessment ranged between 45 min and 1 h. Both patients and GP trainees repeatedly stressed that as the item bank was very long, it was difficult to maintain concentration and questions were missed. Therefore, several items and answers were simplified, and terminology was changed to improve clarity. The important terms in each item were underlined. Neutral answers were added for some items to avoid missing answers when patients felt unconcerned/indifferent. The layout was improved to facilitate reading. Overall, 69% of items were retained, resulting in an adapted 75-item bank. Domain-specific results are summarized in Table  2 . Thirty-four items had psychometric dysfunction and/or poor face validity. They were removed from the item bank. For detailed results see the additional table provided as supplementary material.

Overall, 75 items of the item bank had suitable validity for future development. Despite our purposive sampling technique, the study sample adequately represented the rural and semi-rural French population demographics. Indeed, most patients were women (60%) and in the 60–80 years age group (56%), in line with French national epidemiological data [ 17 ]. The patient feedback and quantitative results on validity allowed cross-validation, leading to a complete picture of the patient perception of care quality items. A satisfactory outcome from this study was that overall, patients were generally satisfied with care at the MPHCC. This is supported by the fact that low rated items were infrequent, the presence of many ceiling effects and the absence of any floor effect. Although many items on subjects considered important by patients had to be removed because of ceiling effects, there were still numerous items for which patients’ satisfactions could be improved which is essential for the future questionnaire.

Almost all items assessing HCP availability had proper validity. This probably come from the fact that they raised important issues for many patients in the context of growing primary care needs (e.g. choosing or changing GP). Indeed, the region has experienced a decline in HCP numbers. For instance, the number of GP in Brittany decreased by 5.6% from 2012 to 2021 [ 18 ]. The situation was probably more significantly affected in the rural and semi-urban areas that participated to this study. Removed items were on subsidiary matters which may depend very much on the situation. For example, most patients were not concerned about the possibility of grouping appointments with different HCPs, possibly because the chosen MPHCC were small with a limited number of HCPs. However, this item may be relevant in larger MPHCC.

In the Care accessibility domain, the four items that showed acceptable validity were about two subtopics: accessibility for people with reduced mobility and ease to reach HCP. Although building compliance with accessibility for people with reduced mobility is mandatory in France, there were still few cases where exemptions or delay had been granted. Ease to reach GP was a frequent expectation for patients with multimorbidity which was emphasized by the increasing number of modalities available through mobile phone, and internet software. This was however often perceived as overburdening by HCP who wanted to be seldom contacted by patients outside consultation. The items on consultation costs and receiving care without upfront payment were both removed because of ceiling effects. These items were not relevant for the French healthcare system where patients with multimorbidity receive care without upfront costs and 100% of their medical expenses are covered by the national health insurance system. However, these items can be important in other countries where patients can face high costs [ 19 ]. Moreover, as economic pressure on the French healthcare system increases, they might become relevant also in France.

Few items on the MPHCC layout and building were considered important by patients. They were related to overall comfort, building sound insulation, things to pass time in the waiting room and information displayed in the waiting room. Conversely, there were strong ceiling effects for several items (e.g., building temperature, ventilation) which probably reflected the little interest by patients in these factors. Indeed, although some MPHCC included in this study were not built recently and their layout was not always optimal and ergonomic, these were not a priority for patients.

Concerning the medical-technical and GP’s expertise domains, although most patients felt they had little ability or legitimacy to judge these aspects, most items had proper validity. The ceiling effect on the GP’s capacity to focus on the health condition that led to the consultation may be related to the fact that patient-centered care is one of the main GP’s skills [ 20 , 21 ]. Most patients rated highly their GP’s skills. This is consistent with data from the National French Directorate of Research, Studies, Evaluation and Statistics (DREES; Direction de la Recherche, des Études, de l’Évaluation et des Statistiques) showing that 88% of French people are satisfied with the quality of care and information provided by the GP on their health status [ 22 ].

Most items on care organization within the MPHCC had proper validity. For instance, the item on consultations with specialists had the lowest score, possibly due to the scarcity of some specialists in MPHCC in France. Recently, this issue has slightly improved because young specialists are more attracted by interprofessional cooperation in primary care centers, besides their role in hospitals. For instance, in the Finistère department, three MPHCC offer consultations with a cardiologist, neurosurgeon, orthopedic surgeon, psychiatrist, gastroenterologist, urologist, and dermatologist. This item is thus particularly valuable for the item bank because it contained a large number of high rated items and lacked low-rated items.

Items on the Patient-HCP relationship and communication showed adequate face validity. Only over-specific items such as medical record sharing, communications among professionals, and openness on complementary medicine were removed because of poor validity.

The validity of most items on the patient’s role in their care domain are promising because patient involvement in their care (e.g. therapeutic patient education) will be developed at these MPHCC in the coming years [ 23 ]. Again, only over-specific items assessing formal intervention such as focus group or dedicated therapeutic education session were removed because of poor validity. Indeed, patients were not familiar with these expert terms or participated in therapeutic education in a non-formal way. Interestingly, although they were introduced in the French public health code in 2005 most patients (62.21%) said that they had never discussed about advanced care directives with their HCPs. This indicator needs to be measured to facilitate approaching this topic because HCPs must support patients in drafting their advanced care directives [ 24 , 25 ].

Most items on the main informal caregiver’s role were hard to evaluate due to the smaller amount of available data (i) by design (only 25% of recruited patients had an informal caregiver), and (ii) because this domain was at the questionnaire end and thus patients might have lost interest. Thus, for conservative reasons, very few items were removed from this domain compared with the other domains. The results for the item on informal caregiver resources at the MPHCC showed that 68% of patients reported that few or no resources were available for the informal caregiver. This confirms the poor informal caregivers’ recognition in France as well as in other developed countries [ 26 , 27 ].

The present study had two important limitations. First, the length of the item bank negatively affected the study feasibility. Indeed, almost all patients lost motivation and became disinterested towards the questionnaire end. The adapted version with fewer items should address this major limitation. Second, as this was a pilot study, the sample size was small. Therefore, only important item psychometric dysfunctions were detected and items with poor psychometric properties might still be present.

The next study phase will analyze the item bank psychometric properties using the item response theory [ 28 , 29 ] in a larger patient sample. This will allow further reducing the item number and assessing the domain structure and psychometric properties.

A bank of 109 items was built and then reduced to 75 items on the basis of their validity assessed in a sample of 209 patients. Retained items explored a large variety of topics related to care quality: availability, accessibility, premises’ layout and building, technical care, expertise, organization, relationships with caregivers and communication, involvement and personal relationships. The analysis of item validity provided a valuable insight on how patients with multimorbidity evaluated their MPHCC care. Most removed items were about topics that were either subsidiary (e.g., possibility to refuse the presence of a student during care), fully satisfactory (e.g., consultation cost), over specific (e.g., possibility to group several appointments together) or which patients felt unable to answer (e.g., communication effectiveness between professionals for home care).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

National French Directorate of Research, Studies, Evaluation and Statistics

General practitioners

Healthcare professionals

Multi-professional healthcare centers

QUALité des SOins PRIMaires (primary healthcare quality)

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Acknowledgements

This paper is dedicated to the memory of Dr. Jérémy Derriennic. We thank all the GP trainees who contributed greatly to this project: Quentin Beauvillard, Vincent Abiliou, Igor-Nicolas Oilleau, Aude Konkuyt, Floriane Colin, Élise Clédic, Lucie Daniel, Léa Robin, and Marion Lastennet.

This study was funded by a grant by the French national health ministry (PREPS15-0472). No conflicts of interest.

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Antoine Dany, Paul Aujoulat, Floriane Colin, Jean-Yves Le Reste & Delphine Le Goff

Department of Human Sciences, University of Western Brittany, Brest, France

Antoine Dany

Department of General Practice, University of Western Brittany, Brest, France

Paul Aujoulat, Floriane Colin, Jean-Yves Le Reste & Delphine Le Goff

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The authors declare that all authors meet criteria for authorship as stated in the Uniform Requirements for Manuscripts Submitted to Biomedical Journals. Author contributions: AD, JYLR DLG designed the study; JYLR coordinated the ethics approval process; PA, FC, JYLR, DLG participated in patient recruitment; AD analyzed the data and drafted the manuscript. All authors reviewed the manuscript and approved the decision to submit for publication.

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The experimental protocol was approved by the ethical research committee (Comité de Protection des Personnes SUD-EST IV, n°19.03.19.67407) and was categorized as observational (n°ID RCB 2019-A00199-48).

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Dany, A., Aujoulat, P., Colin, F. et al. Assessment of multi-professional primary healthcare center quality by patients with multimorbidity. BMC Health Serv Res 24 , 954 (2024). https://doi.org/10.1186/s12913-024-11315-2

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Received : 22 March 2024

Accepted : 15 July 2024

Published : 20 August 2024

DOI : https://doi.org/10.1186/s12913-024-11315-2

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