data mining case study in banking

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Home > Books > Data Mining - Methods, Applications and Systems

Data Mining in Banking Sector Using Weighted Decision Jungle Method

Submitted: 25 November 2019 Reviewed: 20 February 2020 Published: 20 April 2020

DOI: 10.5772/intechopen.91836

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Classification, as one of the most popular data mining techniques, has been used in the banking sector for different purposes, for example, for bank customer churn prediction, credit approval, fraud detection, bank failure estimation, and bank telemarketing prediction. However, traditional classification algorithms do not take into account the class distribution, which results into undesirable performance on imbalanced banking data. To solve this problem, this paper proposes an approach which improves the decision jungle (DJ) method with a class-based weighting mechanism. The experiments conducted on 17 real-world bank datasets show that the proposed approach outperforms the decision jungle method when handling imbalanced banking data.

• data mining
• classification
• banking sector
• decision jungle
• imbalanced data

Author Information

Derya birant *.

• Department of Computer Engineering, Dokuz Eylul University, Izmir, Turkey

*Address all correspondence to: [email protected]

1. Introduction

Data mining is the process of analyzing large data stored in data warehouses in order to automatically extract hidden, previously unknown, valid, interesting, and actionable knowledge such as patterns, anomalies, associations, and changes. It has been commonly used in a wide range of different areas that include marketing, health care, military, environment, and education. Data mining is becoming increasingly important and essential for banking sector as well, since the amount of data collected by banks has grown remarkably and the need to discover hidden and useful patterns from banking data becomes widely recognized.

Banking systems collect huge amounts of data more rapidly as the number of channels (i.e., Internet banking, telebanking, retail banking, mobile banking, ATM) has increased. Banking data has been currently generated from various sources, including but not limited to bank account transactions, credit card details, loan applications, and telex messages. Hence, data mining can be used to extract meaningful information from these collected banking data, to enable banking institutions to make better decision-making process. For example, classification , which is one of the most popular data mining techniques, can be used to predict bank failures [ 1 , 2 , 3 ], to estimate bank customer churns [ 4 ], to detect frauds [ 5 ], and to evaluate loan approvals [ 6 ].

In many real-world banking applications, the distribution of the classes in the dataset is highly skewed. A bank data is imbalanced , when its target variable is categorical and if the number of samples in one class is significantly different from those of the other class(es). For example, in credit card fraud detection, most of the instances in the dataset are labeled as “non-fraud” (majority class), while very few are labeled as “fraud” (minority class). Similarly, in bank customer churn prediction, many instances are represented as negative class, whereas the minorities are marked as positive class. However, the performance of classification models is significantly affected by a skewed distribution of the classes; hence, this imbalance problem in the dataset may lead to bad estimates and misclassifications. Dealing with imbalanced data has been considered as one of the 10 most difficult problems in the field of data mining [ 7 ]. With this motivation, this paper proposes a class-based weighting strategy.

The main contribution of this paper is that it improves the decision jungle (DJ) method by a class-based weighting mechanism to make it effective in handling imbalanced data. In the proposed approach, a weight is assigned to each class based on its distribution, and this weight value is combined with class probabilities. The experimental studies conducted on 17 real-world banking datasets confirm that our approach generally performs better than the traditional decision jungle algorithm when the data is imbalanced.

The rest of this paper is organized as follows. Section 2 briefly presents the recent and related research in the literature. Section 3 describes the proposed approach, class-based weighted decision jungle method, in detail. Section 4 is devoted to the presentation and discussion of the experimental results, including the dataset descriptions. Finally, Section 5 gives the concluding remarks and provides some future research directions.

2. Related work

As a data-intensive sector, banking has been a popular application area for data mining researchers since the information technology revolution. The continuous developments in banking systems and the rapidly increasing availability of big banking data make data mining one of the most essential tasks for the banking industry.

Banking industries have used data mining techniques in various applications, especially on bank failure prediction [ 1 , 2 , 3 ], possible bank customer churns identification [ 4 ], fraudulent transaction detection [ 5 ], customer segmentation [ 8 , 9 , 10 ], predictions on bank telemarketing [ 11 , 12 , 13 , 14 ], and sentiment analysis for bank customers [ 15 ]. Some of the classification studies in the banking sector have been compared in Table 1 . The objectives of the studies, years they were conducted, algorithms and ensemble learning techniques they used, the country of the bank, and obtained results are shown in this table.

Classification applications in the banking sector.

The main data mining tasks are classification (or categorical prediction), regression (or numeric prediction), clustering, association rule mining, and anomaly detection. Among these data mining tasks, classification is the most frequently used one in the banking sector [ 16 ], which is followed by clustering. Some banking applications [ 8 , 10 ] have used more than one data mining techniques, among which clustering before classification has shown sufficient evidence of both popularity and applicability.

Apart from novel task-specific algorithms proposed by the authors, the most commonly used classification algorithms in the banking sector are decision tree (DT), neural network (NN), support vector machine (SVM), k-nearest neighbor (KNN), Naive Bayes (NB), and logistic regression (LR), as shown in Table 1 . Some data mining studies in the banking sector [ 1 , 2 , 6 , 11 , 15 ] have used ensemble learning methods to increase the classification performance. Bagging and boosting are the most popular ensemble learning methods due to their theoretical performance advantages. Random forest (RF) [ 2 , 6 , 11 , 15 ], AdaBoost (AB) [ 6 ], and extreme gradient boosting (XGB) [ 2 , 15 ] have also been used in the banking sector as the most well-known bagging and boosting algorithms, respectively. As shown in Table 1 , accuracy (ACC) and area under ROC curve (AUC) are the commonly used performance measures for classification.

Dealing with class imbalance problem, various solutions have been proposed in the literature. Such methods can be mainly grouped under two different approaches: (i) application of a data preprocessing step and (ii) modifying existing methods. The first approach focuses on balancing the dataset, which may be done either by increasing the number of minority class examples (over-sampling) or reducing the number of majority class examples (under-sampling). In the literature, synthetic minority over-sampling technique (SMOTE) [ 17 ] is commonly used as an over-sampling technique. As an alternative approach, some studies (i.e., [ 18 ]) focus on modifying the existing classification algorithms to make them more effective when dealing with imbalanced data. Unlike these studies, this paper proposes a novel approach (class-based weighting approach) to solve imbalanced data problem.

3.1 Decision jungle

A decision jungle is an ensemble of rooted decision directed acyclic graphs (DAGs), which are powerful and compact distinct models for classification. While a traditional decision tree only allows one path to every node, a DAG in a DJ allows multiple paths from the root to each leaf [ 19 ]. During the training phase, node splitting and merging operations are done by the minimization of an objective function (the weighted sum of entropies at the leaves).

Unlike a decision forest that consists of several evolutionary induced decision trees, decision jungle consists of an ensemble of decision directed acyclic graphs. Experiments presented in [ 19 ] show that decision jungles require significantly less memory while significantly improving generalization, compared to decision forests and their variants.

3.2 Class-based weighted decision jungle method

In this study, we improve the decision jungle method by a class-based weighting mechanism to make it effective in dealing with imbalanced data.

Giving a training dataset D  = {( x 1 , y 1 ), ( x 2 , y 2 ), ..., ( x n , y N )} that contains N instances, each instance is represented by a pair ( x , y ), where x is a d -dimensional vector such that x i  = [ x i 1 , x i 2 , ..., x id ] and y is its corresponding class label. While x is defined as input variable, y is referred as output variable in the categorical domain Y  = { y 1 , y 2 , ..., y k }, where k is the number of class labels. The goal is to learn a classifier function f : X  →  Y that optimizes some specific evaluation metric(s) and can predict the class label for unseen instances.

Training dataset is usually considered as a set of samples from a probability distribution F on X  ×  Y . An instance component x is associated with a label class y j of Y such that:

where P ( y j | x ) is the predicted conditional probability of x belonging to y j and threshold is typically set to 1.

In this paper, we focus on imbalanced data problem, where the number of instances in one class ( y i ) is much larger or less than instances in the other class ( y j ). Like many other classification algorithms, the decision jungle method is also affected by a skewed distribution of the classes, because the traditional classifiers tend to be overwhelmed by the majority class and ignore the rare samples in the minority class. In order to overcome this problem, we locally adapted a class-based weighted mechanism, where weights are determined depending on the distribution of the class labels in the dataset. The main idea is that the minority class receives a higher weight, while the majority class is assigned with a lower weight during the combination class probabilities. According to this approach, the weight over a class is calculated as follows:

where W c is the weight assigned to the class c , N is the total number of instances in the dataset, N c is the number of instances present in the class c , and k is the number of class labels. In the proposed approach, Eq. (1) is updated as follows:

Figure 1 shows the general structure of the proposed approach. In the first step, various types of raw banking data are obtained from different sources such as account transactions, credit card details, loan applications, and social media texts. Next, raw banking data is preprocessed by applying several different techniques to provide data integration, data selection, and data transformation. The prepared data is then passed to the training step, where weighted decision jungle algorithm is used to build an effective model which accurately maps inputs to desired outputs. The classification validation step provides feedback to the learning phase for adjustment to improve model performance. The training phase is repeated until a desired classification performance is achieved. Once a model is build, after that it can be used to predict unseen data.

General structure of proposed approach.

4. Experimental studies

Ensemble approach: Bagging

Number of decision DAGs: 8

Maximum width of the decision DAGs: 128

Maximum depth of the decision DAGs: 32

Number of optimization steps per decision DAG layer: 2048

Conventionally, accuracy is the most commonly used measure for evaluating a classifier performance. However, in the case of imbalanced data, accuracy is not sufficient alone since the minority class has very little impact on accuracy than the majority class. Using only accuracy measure is meaningless when the data is imbalanced and where the main learning target is the identification of the rare samples. In addition, accuracy does not distinguish between the numbers of correct class labels or misclassifications of different classes. Therefore, in this study, we also used several more metrics: macro-averaged precision , recall , and F-measure .

4.1 Dataset description

In this study, we conducted a series of experiments on 17 publically available real-world banking datasets which are described in Table 2 . We obtained eight from the UCI Machine Learning Repository [ 20 ] and nine datasets from Kaggle data repository.

The main characteristics of the banking datasets.

4.2 Experimental results

Table 3 shows the comparison of the classification performances of DJ and weighted DJ methods. According to the experimental results, on average, the weighted DJ method shows better classification outcome than its traditional version on the imbalanced banking datasets in terms of both accuracy and recall metrics. For example, the imbalanced dataset “bank additional” has an accuracy of 94.54% with the DJ method and 94.61% with the weighted DJ method. The accuracy is slightly higher with the weighted version because the classifier was able to classify the minority class samples better (0.8385, instead of 0.7914). The proposed method only disappointed in its accuracy and recall values for 4 of 17 datasets (with IDs 5, 9, 12, and 13).

Comparison of unweighted and class-based weighted decision jungle methods in terms of accuracy, macro-averaged precision, and macro-averaged recall.

It is observed from the experiments that the weighted DJ method failed in classifying only one dataset among 17 datasets in terms of macro-averaged recall values. This means that the proposed method generally can be able to build a good model to predict minority class samples.

It can be deduced from the average precision and recall values that higher classification rates can be achieved with the weighted DJ method for minority classes, while more misclassified points in majority classes may also be detectable in the case of imbalanced data.

Figure 2 shows the comparison of the classification performances of two methods in terms of F-measure: decision jungle and class-based weighted decision jungle (weighted DJ). In principle, F-measure is defined as F  = (2 × Recall × Precision)/(Recall + Precision), which is a harmonic mean between recall and precision. According to the results, for all banking datasets, the proposed method showed some increase or the same performance in the F-measure value.

Comparison of unweighted and class-based weighted decision jungle methods in terms of F-measure.

It can be possible to conclude from the experiments that the minority and majority ratios are not the only issues in constructing a good prediction model. For example, the minority and majority ratios of the first and last datasets are very close, but the classification outcomes related to these datasets are not similar. Although the minority and majority class ratios are almost the same for these two datasets, there is a significant difference between the classification accuracy, precision, and recall values of the datasets, as can be seen in Table 3 . There is also a need for appropriate training examples that have data characteristics consistent with the class label assigned to them.

5. Conclusion and future work

As a well-known data mining task, classification in real-world banking applications usually involves imbalanced datasets. In such cases, the performance of classification models is significantly affected by a skewed distribution of the classes. The data imbalance problem in the banking dataset may lead to bad estimates and misclassifications. To solve this problem, this paper proposes an approach which improves the decision jungle method with a class-based weighting mechanism. In the proposed approach, a weight is assigned to each class based on its distribution, and this weight value is combined with class probabilities. The empirical experiments conducted on 17 real-world bank datasets demonstrated that it is possible to improve the overall accuracy and recall values with the proposed approach.

As a future study, the proposed approach can be adapted for multi-label classification task. In addition, it can be enhanced for the ordinal classification problem.

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S. H. Chong , J. F. Chin , W. P. Loh; Data mining in retail banking: A case study of branch classification. AIP Conf. Proc. 24 June 2022; 2465 (1): 030006. https://doi.org/10.1063/5.0079140

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As customers increasingly shift to online banking services, retail banks constantly review their retail banking network strategies to keep abreast with the trend. The paper studies data mining (DM) classification to determine the branch status of a case study involving 242 retail bank branches in Malaysia and 74 attributes of these branches. The likelihood of a branch being open or close soon was rated in an expert survey. The value is converted into a variable termed as aggregate closure possibility and discretized differently into the target classes of two balanced and unbalanced datasets. In the unbalanced dataset, attribute selection was applied only on instances randomly extracted from the majority classes. Then, two experiments were carried out to identify the best performing classifiers into these datasets. The results show that the decision table classifier produces superior performance over other classifiers. Attribute selection and instance reduction entailed more efficient datasets for data mining. Despite the mildly affected overall classification accuracy, the results are acceptable as there was no impact on the prediction of the critical class (To_Close). The study demonstrates the DM technology to emulate expert decision-making in the retail banking sector.

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• DOI: 10.1109/BESC48373.2019.8963436
• Corpus ID: 210888247

A Case Study of Predicting Banking Customers Behaviour by Using Data Mining

• Xujuan Zhou , Ghazal Bargshady , +3 authors K. C. Chan
• Published in International Conference on… 1 October 2019
• Business, Computer Science

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Harnessing Data Mining in Banking for Enhanced Decision-Making

• June 26, 2024
• Artificial Intelligence in Banking

Data mining in banking has emerged as a pivotal aspect of modern financial systems, enabling institutions to extract valuable insights from vast datasets. With the integration of artificial intelligence, data mining techniques have become essential for optimizing operations and enhancing customer experiences.

As banks navigate an increasingly complex landscape, the significance of data mining cannot be overstated. From detecting fraudulent activities to segmenting customers effectively, the applications of data mining in banking showcase its transformative potential in driving efficiency and profitability.

Table of Contents

Significance of Data Mining in Banking

Data mining in banking refers to the process of analyzing large datasets to uncover patterns, correlations, and insights that can enhance business decisions and customer experience. This practice has become increasingly essential as financial institutions strive to navigate the complexities of modern banking.

The significance of data mining lies in its ability to optimize operations, reduce risks, and drive profitability. By leveraging historical transaction data, banks can predict customer behavior, streamline services, and tailor product offerings that resonate with various customer segments. Ultimately, this enhances customer satisfaction and loyalty.

Additionally, data mining aids in detection and prevention of fraudulent activities, a major concern for the banking sector. Through sophisticated algorithms, banks can identify unusual transaction patterns in real-time, thus mitigating potential threats and safeguarding customer assets.

In a landscape where data is a potent asset, data mining in banking not only supports compliance with regulations but also allows institutions to innovate and remain competitive. As banks continue to transform with emerging technologies, harnessing the power of data mining will prove vital for sustainable growth.

Applications of Data Mining in Banking

Data mining in banking facilitates various impactful applications that enhance operational efficiency and customer service. It employs advanced analytical methods to extract valuable insights from large datasets, enabling banks to make informed decisions.

Fraud detection is among the most significant applications. By analyzing transaction patterns, banks can identify unusual activities, minimizing losses and protecting customer information. Customer segmentation further optimizes marketing strategies, allowing for targeted offers tailored to specific demographics and behaviors.

Another critical application is credit scoring. By examining historical data, banks can accurately assess the creditworthiness of applicants, significantly reducing financial risk. Data mining techniques also support risk management, helping institutions to forecast potential loan defaulters more effectively.

Understanding these applications underscores the vital role of data mining in banking. Businesses that leverage these techniques benefit from enhanced competitiveness and improved customer relations, ultimately driving growth and innovation in the financial sector.

Fraud Detection

Fraud detection in banking utilizes data mining techniques to identify and prevent fraudulent activities effectively. By analyzing vast amounts of transactional data, banks can detect patterns and anomalies that indicate potentially fraudulent behavior.

Common methods employed include:

• Anomaly detection, which identifies transactions that deviate from established behavioral norms.
• Pattern recognition, which analyzes historical data to uncover trends indicative of fraudulent activities.
• Predictive analytics, which forecasts future occurrences of fraud based on past data.

These techniques have revolutionized how banks safeguard their operations and assets. Leveraging machine learning and artificial intelligence, banks can continuously adapt their fraud detection systems to counter new threats, significantly enhancing their vigilance against financial crime. Data mining in banking not only optimizes these processes but also fosters a proactive approach to fraud prevention.

Customer Segmentation

Customer segmentation refers to the process of dividing a customer base into distinct groups based on shared characteristics. This technique allows banks to tailor their services and marketing strategies to meet the specific needs of various customer segments, ultimately enhancing customer satisfaction and loyalty.

Data mining techniques enable financial institutions to conduct thorough analyses of customer demographics, behavior, and preferences. By leveraging these insights, banks can identify high-value customers, optimize marketing efforts, and design personalized financial products that appeal to different segments, such as millennials, retirees, or small business owners.

For instance, a bank may use customer segmentation to develop targeted promotions for younger customers interested in digital banking solutions. Similarly, wealth management services can be tailored for affluent clients, ensuring that marketing efforts resonate with each group’s unique financial goals and expectations.

By employing effective customer segmentation strategies through data mining, banks can optimize resource allocation, increase retention rates, and foster long-term relationships with their clientele, ultimately leading to improved profitability in a competitive market.

Credit Scoring

Credit scoring is the process used by financial institutions to evaluate the creditworthiness of potential borrowers. It involves analyzing various factors, including an individual’s credit history, current debt obligations, and income levels, to predict the likelihood of timely repayments.

In the realm of data mining in banking, advanced algorithms analyze vast datasets to generate more accurate credit scores. Techniques such as regression analysis and neural networks discover patterns in historical data to assess risk more effectively. This helps banks make informed lending decisions and tailor products to individual needs.

Data mining enhances the granularity of credit assessments by incorporating alternative data sources. For instance, behavioral data from social media or utility payment history can provide additional insights into a borrower’s reliability. Consequently, customers with limited traditional credit histories can still access credit.

Using data mining for credit scoring not only improves risk assessment but also contributes to more equitable lending practices. By relying on diverse datasets, banks can offer fairer credit terms and expand financial access to underserved populations. This ultimately promotes financial inclusion and enhances overall market efficiency.

Data Mining Techniques Used in Banking

Data mining encompasses various techniques that analyze large datasets to extract valuable insights in the banking sector. These techniques are pivotal for enhancing decision-making and fostering innovation.

Cluster analysis is commonly employed to categorize customers and identify distinct segments based on their behavior and preferences. This method allows banks to tailor products and services to different demographic groups effectively.

Decision trees provide a straightforward approach to classification and regression tasks. They help in predicting customer behavior and managing risk by outlining paths to potential outcomes, making complex data more interpretable.

Neural networks simulate human brain operations, making them suitable for recognizing patterns in vast amounts of data. In banking, these advanced models are particularly useful for tasks such as fraud detection and risk assessment, leading to more accurate forecasts and strategic initiatives.

Cluster Analysis

Cluster analysis refers to a statistical method employed to group a set of objects or data points into clusters based on their similarities. In the banking sector, this technique is instrumental for categorizing customers, transactions, and other entities to uncover essential patterns and behaviors.

By applying cluster analysis, banks can identify distinct segments within their customer base, enabling targeted marketing strategies and personalized services. Common applications include:

• Marketing campaigns tailored to specific customer groups
• Enhanced customer experience through personalized product offerings
• Streamlined operations by identifying high-value clients

Furthermore, cluster analysis aids in detecting anomalies within groups, thus contributing to effective fraud detection. The insights gathered not only enhance decision-making but also foster deeper customer relationships, ultimately promoting customer loyalty within the competitive landscape of banking.

Decision Trees

Decision trees are a popular data mining technique used in banking for their ability to simplify complex decision-making processes. They visually represent decisions and their possible consequences, allowing for straightforward interpretation and analysis. In the banking sector, these trees aid in evaluating risk and making informed choices.

Each node in a decision tree signifies a decision point, where data is split based on specific criteria. By analyzing customer data, banks can classify clients into different categories, predicting behaviors like loan default or fraud likelihood. This clear depiction of branches helps banks understand which factors most influence outcomes.

The use of decision trees in credit scoring exemplifies their effectiveness. Banks can assess applicants by considering variables such as income, employment history, and credit utilization. This systematic approach ensures a fair assessment while also identifying statistically significant predictors of creditworthiness.

Furthermore, the intuitive nature of decision trees enables banking professionals to communicate findings easily across various teams. This transparency enhances collaborative efforts within organizations, ultimately leading to better strategies in customer relationship management and risk mitigation.

Neural Networks

Neural networks are computational models inspired by the human brain, designed to recognize patterns and solve complex problems through interconnected nodes (neurons). In the context of data mining in banking, they are extensively utilized for predictive analytics and decision-making processes.

These models excel at processing vast amounts of data, making them particularly effective for tasks such as fraud detection and customer behavior analysis. By analyzing historical transaction data, neural networks can identify anomalies that indicate potential fraudulent activities with remarkable accuracy.

In customer segmentation, neural networks help banks tailor their services to diverse clientele by segmenting customers based on behaviors and preferences. This enables financial institutions to implement targeted marketing strategies and improve customer satisfaction.

The adaptability of neural networks allows them to learn continuously from new data, enhancing their accuracy over time. Their effectiveness in data mining positions them as invaluable tools for banking institutions seeking to leverage artificial intelligence for optimized operations and enhanced customer experiences.

Benefits of Implementing Data Mining in Banking

Implementing data mining in banking provides multiple advantages that enhance operational efficiency and customer satisfaction. By leveraging data mining techniques, banks can uncover valuable insights from large datasets, allowing for informed decision-making. This capability is particularly beneficial for identifying emerging trends and customer behavior patterns.

Improved fraud detection is one significant benefit. Data mining algorithms analyze transaction histories and identify anomalies, drastically reducing financial losses and protecting customers from fraudulent activities. This proactive approach not only safeguards assets but also fosters trust between banks and their clientele.

Another benefit is enhanced customer segmentation. By grouping customers according to their financial behavior and preferences, banks can tailor products and services to meet specific needs. This personalized marketing strategy improves customer retention and satisfaction, driving revenue growth.

Additionally, the accuracy of credit scoring models increases through advanced data mining techniques, enabling banks to assess creditworthiness more effectively. This leads to better lending decisions, reduced default rates, and a healthier financial ecosystem. Overall, the implementation of data mining in banking yields significant operational and strategic benefits, solidifying its critical role in the industry.

Data Privacy and Security in Data Mining

Data privacy and security are crucial considerations in data mining within the banking sector. As financial institutions collect and analyze vast amounts of sensitive customer information, the risk of data breaches and unauthorized access increases significantly. Protecting this data not only safeguards customer confidentiality but also upholds the bank’s reputation.

Banks must employ robust security measures, such as encryption and secure access controls, to protect the integrity of data. Regulatory frameworks like the General Data Protection Regulation (GDPR) further emphasize the importance of data protection, mandating banks to establish strict guidelines for data processing and user consent.

The implementation of effective data mining techniques must balance the need for insights with the imperative to maintain customer trust through responsible data usage. Compliance with industry regulations ensures that financial institutions manage data ethically while benefiting from the insights provided through data mining.

As banks embrace data mining to drive innovation, prioritizing data privacy and security becomes indispensable. This commitment to safeguarding customer information not only mitigates risks but also supports the long-term success of data-driven initiatives in banking.

Challenges in Data Mining in Banking

Data mining in banking faces several notable challenges that can impede its effectiveness. One primary issue is related to data quality. Inconsistent, incomplete, or outdated data can lead to inaccurate insights, which significantly detracts from the value of data-driven decision-making processes. Therefore, ensuring data integrity is paramount for successful mining.

Regulatory compliance also poses a significant challenge. The banking industry is subject to stringent regulations concerning data privacy and security. Organizations must navigate complex legal frameworks to ensure that their data mining practices adhere to local and international laws, which can be time-consuming and resource-intensive.

Furthermore, the dynamic nature of financial markets necessitates continuous adaptation of data mining techniques. Models and algorithms that work effectively today may become obsolete due to changing consumer behaviors or emerging fraud strategies. Keeping pace with these shifts is essential for maintaining relevant and actionable insights in data mining in banking.

Lastly, the integration of advanced technology and infrastructure to support data mining processes can be daunting. The costs associated with implementing sophisticated systems may deter smaller institutions from taking full advantage of data mining capabilities in their operations.

Data Quality Issues

Data quality issues represent a significant challenge in the realm of data mining in banking. High-quality data is essential for making informed decisions, as inaccurate or incomplete data can lead to misinterpretations and suboptimal strategies.

Banks often collect vast amounts of customer data, but this data can suffer from inconsistencies, duplication, or errors. For instance, customer records may be entered incorrectly, leading to misalignment in fraud detection systems and customer segmentation strategies.

Furthermore, data quality varies across different sources within a banking institution. Merging data from various branches, online platforms, and third-party providers can introduce discrepancies, complicating analytical processes. Improved data governance and validation mechanisms are necessary to ensure accuracy, reliability, and completeness.

Ultimately, addressing data quality issues is critical for maximizing the effectiveness of data mining in banking. Robust data management practices can significantly enhance the decision-making capabilities within the banking sector, ensuring that insights derived from data mining are both valid and actionable.

Regulatory Compliance

Regulatory compliance in banking encompasses adherence to laws, regulations, and guidelines governing financial institutions. This framework ensures that banks operate fairly, transparently, and responsibly while protecting customer data. Compliance is particularly critical in the context of data mining in banking.

The growing reliance on data mining for insights necessitates rigorous compliance with data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These regulations mandate banks to handle consumer information responsibly, ensuring transparency in data usage.

Non-compliance can result in significant penalties, impacting the bank’s reputation. Furthermore, maintaining customer trust is imperative in banking; regulatory adherence plays a crucial role in building and sustaining that trust. As banks innovate with data mining technologies, they must balance data utilization with stringent compliance measures.

Navigating these regulations requires robust frameworks and a deep understanding of both legal stipulations and operational practices. By doing so, banks can not only minimize risks but also leverage data mining capabilities to enhance their services responsibly.

Artificial Intelligence and Data Mining Synergy

The integration of artificial intelligence with data mining in banking enhances the analytical capabilities employed to extract insights from vast datasets. This synergy empowers financial institutions to process data more efficiently, identifying patterns and trends that were previously difficult to discern.

AI algorithms complement data mining techniques by automating the analysis of customer data and transaction histories. This leads to improved fraud detection, as AI can recognize anomalies in real-time, allowing banks to take swift action against suspicious activities.

Customer segmentation also benefits from the combination of AI and data mining, enabling banks to create tailored marketing strategies. By analyzing customer behavior and preferences through advanced algorithms, institutions can offer personalized services that enhance customer satisfaction and loyalty.

The role of artificial intelligence in data mining further extends to predictive analytics, assisting banks in forecasting future trends. This proactive approach enables institutions to make informed decisions, improve risk management, and drive overall performance, reinforcing the importance of data mining in banking.

Future Trends in Data Mining for Banking

The future of data mining in banking is poised for significant advancements driven by emerging technologies and the increasing demand for data-driven decision-making. As banks seek to enhance operational efficiency and customer engagement, the integration of artificial intelligence will play a pivotal role in refining data mining processes.

Predictive analytics is expected to gain traction, allowing banks to forecast market trends and customer behaviors more accurately. This capability will enable financial institutions to tailor products and services to meet the specific needs of their clientele, thereby enhancing customer satisfaction and loyalty.

Furthermore, the incorporation of blockchain technology will enhance data integrity and security in data mining for banking. This advance has the potential to revolutionize the way institutions handle sensitive information, ensuring that data is both secure and easily accessible.

Lastly, the implementation of advanced machine learning algorithms will improve the accuracy of fraud detection and risk assessment. As these technologies evolve, they will empower banks to respond proactively to emerging threats, thereby safeguarding their assets and maintaining consumer trust in an increasingly digital finance landscape.

Case Studies of Data Mining in Banking

Several banks have successfully demonstrated the power of data mining in banking through real-world applications. Notable case studies highlight the effectiveness of these techniques in improving operational efficiency and customer satisfaction.

One prominent example is JPMorgan Chase’s use of data mining to enhance customer segmentation. By analyzing transaction data, the bank identified distinct customer profiles, allowing for tailored marketing strategies. This targeted approach resulted in increased engagement and revenue.

Another case is Wells Fargo, which implemented fraud detection algorithms. The bank utilized historical transaction data to identify patterns indicative of fraudulent behavior. This proactive stance significantly reduced financial losses and bolstered customer confidence in the bank’s security measures.

Lastly, Bank of America adopted credit scoring models that utilize data mining techniques. By integrating various datasets, including credit history and transaction behavior, the bank improved the accuracy of its lending decisions. This not only mitigated risk but also expanded access to credit for reliable borrowers.

The Road Ahead for Data Mining in Banking

The future of data mining in banking is poised for significant transformation as advanced technologies and evolving consumer behaviors shape the landscape. Financial institutions are likely to enhance their analytical capabilities through the integration of machine learning and artificial intelligence, enabling more accurate predictions and decision-making processes.

As competition intensifies, the adoption of sophisticated data mining techniques will empower banks to better understand customer needs and preferences. This approach facilitates tailored product offerings, ultimately fostering customer loyalty and satisfaction. Additionally, advancements in big data technologies will streamline the management and analysis of vast data sets, revealing valuable insights and trends.

Data privacy and regulatory compliance will continue to be paramount in future developments. Banks must prioritize ethical data mining practices, ensuring the protection of customer information while harnessing its potential. Striking a balance between innovation and privacy will be critical to maintaining trust and compliance in this evolving sector.

With the ever-increasing capabilities of data mining in banking, we can anticipate a more agile and responsive financial environment. This evolution will empower banks to mitigate risks, enhance operational efficiency, and ultimately deliver exceptional service to their customers.

The integration of data mining in banking represents a transformative approach to understanding customer behavior, enhancing security, and optimizing decision-making processes. As financial institutions increasingly harness these technologies, they pave the way for more efficient and effective operations.

Adopting robust data mining techniques not only bolsters fraud detection and customer segmentation but also elevates credit scoring methodologies. As the banking sector evolves, maintaining a focus on data privacy and regulatory compliance will be essential for sustainable growth and trust.

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Journal of Enterprise Information Management

ISSN : 1741-0398

Article publication date: 23 November 2022

Issue publication date: 7 March 2023

The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.

Design/methodology/approach

Guided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.

The study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.

Originality/value

For theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

• Transaction cost theory
• Big data analytics
• Enterprise information management
• Banking industry
• Precise marketing

He, W. , Hung, J.-L. and Liu, L. (2023), "Impact of big data analytics on banking: a case study", Journal of Enterprise Information Management , Vol. 36 No. 2, pp. 459-479. https://doi.org/10.1108/JEIM-05-2020-0176

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Case study of data mining application in banking industry.

Yongping Liu , Applied Mathematics Department, South China University of Technology Follow

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In this paper, we study the usages of data mining in banking industry and its related impacts. Great changes in banking services emerged from the application of data mining especially in retailing banking. We present China Merchant Bank (CMB) as an example to do case analysis, in which we explore data environment evaluation analysis model, operational efficiency model and profitability model to analysis the application performance for CMB. Finally we provide some advices of future development to CMB.

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Liu, Yongping, "Case Study of Data Mining Application in Banking Industry" (2003). ICEB 2003 Proceedings (Singapore) . 77. https://aisel.aisnet.org/iceb2003/77

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How Data Mining Can Positively Impact the Banking Sector

How Data Mining is Positively Impacting the Banking Business

Banking fraud detection with data mining, target marketing, capital market forecast using data mining, better management for money laundering, data mining in a nutshell.

Data mining or Data extraction is the process of extracting information from vast volumes of data. This data could be multimedia, time series, or online or on the web. Data mining is extracting meaningful, significant, implicit, largely undiscovered, potentially beneficial patterns or knowledge from massive amounts of data. It is the series of steps to discover new, hidden, or unexpected ways in data.

Hire Data Mining Services Experts

It’s inevitable for banks to lose millions of dollars annually via fraud. Besides, a single fraud can cause significant loss and devalue the brand’s name. Moreover, it also decreases the customers’ trust and may see a substantial shift to different banks that can be profitable for them. Naturally, every bank wants to retain its customers; hence, data mining services aid the solution.

Identifying fraudulent transactions allows banks to act quickly and prevent losses. Fraud detection is the method of identifying potential fraud risks and fraudulent transactions that may occur. It can assist banks in detecting fraud quickly for numerous aspects, including credit card products, financial statement fraud, and money laundering operations. Clustering methods can be used to categorize transactions and investigate the outliers. Nowadays, most international banks choose outsourcing data mining services as it saves them a lot of time, effort, and money. Moreover, it also helps to focus on the core banking services and enhance customer experience.

Uniquesdata is among the top Data Mining Service providers that guarantee excellence, efficiency, and accuracy. Financial statement fraud detection is another area where data mining services can be applied. Banks make credit choices based on customer financial statements, which may overestimate assets, sales, and profits or understate losses and obligations. Since these types of fraud are difficult to identify using conventional auditing procedures, classification techniques based on neural networks, regression, and decision trees are used that can distinguish between fraudulent and non-fraudulent ratios.

Reducing the risk with default detection

Data mining can assist banks in reducing the risk of potential financial losses. Analyzing clients’ risk can also help to determine which clients are likely to miss or default on loan repayment, allowing the bank to take corrective measures on time and avoid losses. Data mining services techniques can also be used to improve the quality of credit scores and predict credit risk. Data mining techniques improve credit score accuracy and estimate default rates. Turnover trends, balance sheet data, limit consumption, behavioral patterns, and cheque return patterns are all factors to consider. Cognitive scores are derived using probability models of customer behavior to forecast future behavior. By evaluating accessible credit history, data mining can calculate this score based on the borrower’s historical debt payback behavior.

Data mining services can evaluate consumer data and identify consumer behavior to assist the bank in targeted marketing. It can also categorize clients based on their attributes, behavior, needs, preferences, values, etc. With this information, banks can target their potential customers. By evaluating historical data associations, outsourcing data mining services can also indicate cross-selling possibilities, such as advertising home loans to credit card consumers.

Investment is putting money into an asset to make a profit from it. Banks’ Customers are frequently offered investing services that may interest them and be profitable from both ends. There are numerous financial products available in the market. Data mining techniques such as K-means clustering can be used to select the best investments depending on the customer’s profile. The ability to predict asset values based on previous data can significantly boost investment returns. Data mining services prediction techniques such as neural networks and linear regression can be used to forecast stock prices. According to research, the expansion rates of essential fundamental features such as capital investment, revenues, earnings per share, market share, and debt can be utilized to forecast the future returns of various companies. Network modeling has been the most widely used data mining method in stock market prediction.

The process of hiding the “black” money to legalize it is known as money laundering. Banks are widely used as channels to launder money. Hence, governments and financial regulators require banks to adopt processes, systems, and procedures to detect and prevent money laundering transactions. Data mining services techniques can be used to uncover transaction trends that could lead to money laundering. Statistical false reduction approaches based on decision tree classification are used to reduce the number of faulty patterns found.

In the age of digitization, the use of data mining in finance has widely grown. Banks utilize data mining in various applications, such as marketing, fraud detection, risk management, money laundering detection, and investment banking. They are investing in data mining services technology to remain competitive. Nonetheless, many concerns and challenges remain to be addressed to achieve successful financial management for companies and individuals. Whether you want to cut costs, increase profits, or obtain insights into your banking business, choose uniquesdata to Outsource Data Mining Services.

Aniruddh Chauhan

Contributing my knowledge and skills to the company's financial initiatives, I look forward to educating audiences on financial matters integrated with data and how financial institutes can leverage the power of data with its trends.

Smarter analytics for banks

Advanced analytics is enabling superior performance in organizations willing to make the proper commitment: across all industries, companies that are more analytically driven realize financial growth three times higher than their less analytical competitors, according to McKinsey’s Analytics Quotient (see sidebar).

Industries ranking highest on analytics maturity include pharmaceuticals and medical products; insurance; and energy, materials, and agriculture. But banking, with a long history in leveraging data, starts from the strongest position.

McKinsey’s Analytics Quotient

Analytics Quotient (AQ) offers an objective and comprehensive assessment of a company’s analytics maturity along key dimensions that drive financial performance. It distills insights from over 1,000 conversations with chief experience officers on advanced analytics, combined with McKinsey’s expertise across functional areas (such as organization, talent, and culture) and cutting-edge data science (for example, infrastructure, modeling, techniques, and tools).

The AQ is organized along six dimensions that define a state-of-the-art analytics capability. These include analytics strategy, data and technology, models and tools, value assurance, organization and talent, and culture.

The AQ is designed to identify companies’ strengths and gaps relative to best practice along those six dimensions and delivers a single AQ score for benchmarking against peers. The underlying criteria of the 40 questions it poses are highly descriptive, allowing companies to develop a road map to improving in each dimension. The resulting insights become core to an analytics transformation program—informing priorities, sequencing opportunities for growth, and tracking progress.

However, progress within banking is relative—many firms have yet to realize the full potential from embedding analytics deep into their culture, decision processes, and business operations. We recently conducted an in-depth analysis of more than 20 banks in Europe, Middle East, and Africa (EMEA), assessing their analytics maturity across six dimensions. While the banks in this group have generally constructed strong initial analytics foundations, there is still room for them to improve performance. The study revealed five areas where banks can improve returns:

• Align analytics priorities to strategic vision. For more than half of the banks surveyed analytics is a strategic theme, but the majority struggle to connect the high-level analytics strategy to a targeted selection and prioritization of use cases , and to implement them in an orchestrated way. Banks are starting to leverage advanced-analytics techniques in several areas—commercial, risk, innovation, and technology—but for many, top-down views limit the potential of analytics in their core strategic activities.
• Embed analytics into decision making and workflows. Senior managers acknowledge the potential value of analytics, but they often do not know how to expand from a few pilots to a full-scale impact. Some firms are challenged by shortages of technical production and engineering capabilities. For others, the problem lies in the absence of a change-management program to drive adoption or a culture that fails to support data-driven decision making and rapid, agile iterations.
• Develop advanced-analytics assets and teams to scale. Most banks have successfully launched single, stand-alone initiatives in advanced analytics, but few have developed them into efficient, large-scale operations. More successful organizations gain traction through advanced-analytics centers of excellence (COEs) and expand their analytics scale to accelerate impact. Broader analytics reveal transformative opportunities and enable interfaces with third-party vendors, allowing development of external capabilities, know-how, and assets.
• Invest in critical analytics roles. Banks are expanding their analytics teams to meet the growing need for specific technical profiles: data engineers, data scientists, visualization specialists, and machine-learning engineers. But, in addition to adding pure technical analytics talent, banks are facing challenges in building “translator” capabilities and fostering effective collaboration among different roles. Translators are a crucial link between business and analytics and typically come from within business units. They help data scientists understand business problems and priorities and ensure that analytics insights are communicated back to business units, and therefore they must be fluent in the languages of both.
• Enable the user revolution. Banks possess great sources of data suitable for many use cases, but data practices tend to be narrow in scope and focused on regulation. Moreover, data often is not made readily available to the broader organization. Companies typically impose formal constraints on data security, privacy, and compliance, but few define clear data ownership and maintain high-quality data that is ready for application to develop analytics use cases.

Banks display a range of approaches to analytics and varying success in embedding analytics into their “cultural DNA.” This article details our findings among the sample of EMEA banks and provides more detail on the five imperatives that will enable banks to reach their full analytics potential.

Align analytics priorities to strategic vision

Advanced analytics in banking has evolved considerably in the last few years. Most banks can articulate an analytics strategy and have implemented—or are in the process of implementing—a set of use cases. However, in many cases there is a disconnect among the use cases defined by business units, the broader goals of the organization, and the aspiration to use advanced analytics to help realize these goals in the next three years.

Among the banks we surveyed, only 30 percent have effectively matched their analytics efforts with their business goals. However, among the subset of firms that scored high on analytics maturity overall, 60 percent have aligned their analytics use cases with their strategic priorities.

Banks currently concentrate most of their analytics use cases in sales management (for example, next product to buy, digital marketing, and transactional analytics), financial risk management (collections), and nonfinancial risks (cybersecurity and fraud detection). These are logical first choices, but banks also need an analytics road map for the entire organization to ensure transparency and clarity on their aspiration for advanced analytics.

Before launching efforts on specific use cases, banks should identify those areas where analytics will do the most to enhance their value propositions, in line with their business strategies. Over time, banks should extend analytics to other functions and set their ambitions for how analytics will help the organization in the years ahead.

Across industries, analytics leaders integrate analytics not only into a few crucial business units but also across all operations. This is true for analytics leaders among banks as well: more than half have introduced use cases to three or more functional areas.

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Embed analytics into decision making and workflows.

Firms also face a significant challenge in turning their analytics insights into business outcomes and realizing the full value of analytics—what we term “attention to the last mile.” In addition to significant technical and production engineering challenges, getting the most from advanced analytics requires business adoption and change management . By their own reckoning, only 7 percent of surveyed banks had achieved full integration of key analytics use cases.

Moreover, many banking leaders have not yet adopted a data-driven mind-set for decision making: just 15 percent of respondents believe that their bank leadership makes decisions from a heavy reliance on analytics, and only 20 percent of firms believe that their leaders will be persuaded by analytics insights that run counter to their initial beliefs.

While many banks surveyed are convinced of the potential impact of analytics, in many instances this message is not clearly transmitted from senior management to the front line. Without this emphasis, business teams will fail to understand the power of analytics, and in turn, relationship managers, marketing teams, and credit underwriters will not be motivated to make the necessary changes in mind-set. As a result, while half of management teams at EMEA banks surveyed claim to be sold on the value of analytics, only 25 percent effectively communicate a change story on how analytics will improve operations, and only 20 percent of banks succeed in expanding investments in analytics to scale.

Even data-savvy banks have a hard time gaining high-visibility, firm-wide engagement from middle management, and building front-line and management capabilities to scale advanced analytics efforts across the organization.

At almost two-thirds of banks applying analytics, C-suite sponsors evangelize their programs and give progress reports on strategies to the broader organization. These communications should emphasize how analytics can be a complement—or counterpoint—to established practices.

Once strategically relevant use cases are defined, management should identify end-to-end business owners who can move them from pilot stage to full scale and create accountability and incentives to capture maximum impact.

Early on in analytics planning, banks should consider how the insights will be delivered and contribute to decision making . Data-visualization specialists, designers of user experiences and interfaces, and behavioral economists all can play a role and reshape the bank’s workflow design, digital tooling, and decision processes.

Banks also should invest time and effort into completing the last mile. Analytically mature firms typically allocate more than half of their investments to embedding decision making in line organizations—process and workflow definition, team capabilities, and an effective rollout. Banks can address the key challenges to adopting analytics by providing front-line staff with actionable real-time insights, establishing intuitive key performance indicators, and ensuring that business owners move from idea to implementation.

Operationally, managers should leave analytics complexity behind the curtain. For example, whether banks follow rule-based logic for mapping product offers to basic customer segments or employ more sophisticated machine-learning methods for targeting product offers to customer microsegments, front-line representatives and call-center agents need not be aware of the underlying technology when prompting best next actions.

Develop advanced-analytics assets and teams to scale

Banks follow disparate approaches to positioning their analytics teams. Forty percent of banks follow a hybrid approach that concentrates analytics talent in COEs, providing solutions to the entire bank and balancing analytics efforts within business units. About one-fourth take a completely decentralized approach, while the remainder implement highly centralized solutions.

Decentralized approaches are effective when there is a need to stay close to the business units to infuse domain expertise and drive adoption (and when a lower priority is assigned to scaling quickly and consistently across business units). Centralization of the analytics organization is typically better suited to a company that is starting its analytics journey and seeking to establish groupwide capabilities and consistent policies and language. A hybrid state with a COE defines the direction of the strategy, stays abreast of the latest methodological advancements, provides shared analytics services, and moves the organization towards an agile culture.

Banks should focus on building core advanced-analytics capabilities to capture transformative opportunities and to interface with third-party vendors, which will enable banks to leverage existing know-how, solutions, and assets as well as infrastructure. These efforts start with a COE engineered to quickly deliver successful use cases. Establishing such a foundation and leveraging quick wins are prerequisites to building a data-driven organization.

There is a proven approach to designing the core structural elements of an advanced-analytics COE: identifying impact opportunities, establishing reporting lines and ownership, hiring and training, developing the analytics delivery model, and building organization-wide analytics capabilities.

Invest in critical analytics roles

Banks are short on analytics talent. Few managers know the exact number of dedicated specialists—data scientists, engineers, and architects, as well as visualization specialists, workflow integrators, delivery managers, and product owners—within their organizations or can fully define their roles. Little wonder, then, that 80 percent of surveyed banks find it difficult to recruit the right analytics talent.

Organizations require a variety of skills and well-defined roles and modus operandi to bring together business, analytics, and technology. Our findings indicate that stakeholders at only 20 percent of banks believe that analytics has been given an adequate role, and just 14 percent report that their teams of data scientists, architects, and engineers have been assigned clear responsibilities.

Irrespective of their organizational structures, banks have underemphasized the crucial role of translators —the ambassadors who ensure that analytics solves critical business problems. Translators typically guide and collaborate with data engineers and data scientists to translate business problems into a language they can clearly understand, and then ensure actionable insights are integrated into the business units’ workflows and decision making. Translators typically come from within business units, allowing them to build an analytics awareness in the broader organization. They are especially important in organizations and business units that have not previously emphasized analytics literacy.

Banks must step up their investments in analytics, expanding their recruiting strategies and competing with leading tech firms for the talents of data scientists, data engineers, and translators. High achievers centralize their analytics talent, providing analytical support across bank business units (for example, marketing, risk, human resources, IT, and operations).

Banks need to create or expand training programs to broaden analytics understanding at all levels—senior management, business-team leaders, and non-analytics employees. Several major European banks have made fluency in analytics a requirement for advancement not only to the C-suite but also for the top echelon of all management. Typically, such efforts to upgrade skill levels span two years or more.

Banks should also expand the number and scope of use cases they undertake, engaging additional stakeholders and adopting a “fail fast, win fast” philosophy. Most banks surveyed are already planning an average 20 percent increase in their analytics investment over the coming three years and an expansion of analytics teams and translator teams by more than three times.

A bank branch for the digital age

Enable the user revolution.

Data collection and security have long been core priorities for banks: more than half of those surveyed report having formal systems for data security, privacy, and compliance. However, just one-third possess data resources that are clean and ready for advanced analysis.

Moreover, banks’ analytics strategies and efforts to date have focused primarily on fulfilling regulatory requirements, not on strategic value creation. Few banks have data strategies that support delivery of broad-based analytics efforts, and a similarly small number have provided employees with ready access to information.

However, because they are data conscious, banks have a head start on their analytics solutions. Two-thirds already offer their data analysts and scientists a state-of-the-art workbench with an extensive set of tools. But there is a wide variance in their deployment and sophistication. Most of the banks we studied rely heavily on simple descriptive and predictive models; more sophisticated techniques, such as real-time predictive analytics enabled by machine learning, remain in the early stages.

Banks should strengthen their data-management processes to ensure that adequate amounts of relevant data are gathered, available, and actionable. Central data repositories (for example, data lakes) stocked with exhaustive primary information on customer transactions are a foundation, but management should also be open to data that is less rigorously structured, such as logs from call centers. Moreover, valuable insights can be drawn from information that originates outside the organization—from governments, trade organizations, industry utilities, and cross-industry coalitions sharing information. Data should also be “democratized,” giving open but secure access to all those entitled to draw on it.

Analytics leaders should have a range of tools to call upon; more importantly, they need an approach for integrating them across their systems and business operations. Banks leading the way in analytics have a methodology for the development of models, interpretation of data, and deployment of new capabilities—heavily involving the front line—to generate relevant real-time insights for the business. These leaders also continually score the results of a range of models and implement the most effective.

For banks, advanced analytics should be a broad capability rather than a stand-alone function. Success in this regard requires that banks rethink tools, platforms, and methodologies. To support this shift in mind-set, leading banks are creating pull from users with prominent in-house academies. These programs address analytics literacy for all employees and inspire tangible and practical use cases to mobilize the organization.

McKinsey estimates that sharpening analytics efforts could lead to an increase in earnings of as much as $1 trillion annually for the global banking industry. The benefits would be widespread, but about one-third of the gains would come in reduced fraud losses and about 20 percent from better informed pricing and promotion. To realize these benefits, banks need to build on their long history of using data to become true analytics leaders.

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Carlos Fernandez Naveira is an associate partner in McKinsey’s Madrid office; Imke Jacob is a consultant in the Frankfurt office, where Eckart Windhagen is a senior partner; and Khaled Rifai is a partner in the New York office. Pamela Simon is a solution delivery manager in the North America Knowledge Center.

The authors wish to thank Ignacio Crespo and Bryce Hall for their contributions to this article.

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News & Insights

What are Prefeasibility and Feasibility Studies? (Updated 2024)

September 18, 2024 — 04:55 pm EDT

Written by Melissa Pistilli for Investing News Network  ->

Resource investors new to the market might see quite a few unfamiliar phrases in news releases. Prefeasibility and feasibility studies are definitely two key mining terms to know.

Prefeasibility and feasibility studies are inherently linked to each other — understanding their differences creates a clearer idea of what they are and how they’re used. Their key similarity is that they represent milestones for mining and exploration companies.

On that note, let’s take a closer look at what exactly prefeasibility and feasibility studies are, as well as how they fit into company plans and the lifecycle of a mining project .

What is a prefeasibility study?

A prefeasibility study (PFS) is an early stage analysis of a potential mining project. These studies are conducted by a small team and are designed to give company stakeholders the basic information they need to greenlight a project or choose between potential investments. They typically give an overview of a mining project’s logistics, capital requirements, key challenges and other information deemed important to the decision-making process, such as whether the operation will be open pit or underground.

​What comes before a prefeasibility study?

Prefeasibility studies are usually preceded by sufficient mineral exploration work, including drilling, to inform a mineral resource report, a potential model of the orebody and a scoping study.

​What is a scoping study?

A scoping study, also known as a preliminary economic assessment or a PEA, is a study that includes initial technical and economic analysis of the potential viability of a project's mineral resources.

PEAs should include base-case data on the capital costs associated with bringing a project into production, an estimate of how the mine will operate once it is built, how much metal and money it will produce and what operating costs it will incur. PEAs help mining companies understand risks and uncertainties associated with a project by providing information on pre-production capital costs, life-of-mine sustaining capital, mine life and cash flow, as well as details on processing and production methods and rates.

​When and why do companies undertake prefeasibility studies?

Following a preliminary mineral resource report and the creation of an orebody model, a PFS acts as one of the first explorations of a potential investment. Companies use these studies to collect information before investing millions of dollars into tasks like acquiring permits or research equipment.

What information does a prefeasibility study include?

In addition to information relating to geological models and mine design, prefeasibility studies take into account factors that may impact or interfere with the final project. That can involve community issues, geographic obstacles, permit challenges and more.

A comprehensive PFS should include detailed designs and descriptions for the mining operation, as well as cost estimates, project risks, safety issues and other important information. There should also be multiple options included in the study for tackling different issues, as that will provide organizations with more ways to overcome potential challenges.

What happens if prefeasibility study results are positive? Negative?

If a PFS shows a positive base-case scenario, the company will likely move on to the next stage, a feasibility study. If the study is negative, an organization may head back to the drawing board or abandon the potential project altogether.

What is a feasibility study?

A feasibility study is an in-depth evaluation of a mining project with and established mineral resource. These studies are intended to evaluate if a mineral reserve can be mined effectively and if it will be profitable. Detailed mining feasibility studies are also used as the basis for a project’s capital estimates, operating costs and overall economic viability.

What is the difference between prefeasibility and feasibility studies?​

While the cover many of the same topics, the main difference between prefeasibility and feasibility studies is that the latter are meant to be much more accurate and require more resources to complete. Feasibility studies should offer estimates that are within 10 to 20 percent accuracy, whereas prefeasibility studies are allowed to run between 20 and 30 percent.

​When and why do companies undertake feasibility studies?

At this point in the process, organizations already have large sums of money at stake and a drive to see their project through to completion. Feasibility studies are all about reducing risks and addressing potential issues that may complicate a mining project. The studies also include information that is helpful for stakeholders such as local governments or environmental analysts.

In a guide to feasibility studies , Don Hofstrand and Mary Holz-Clause of Iowa State University note, “A feasibility study is usually conducted after producers have discussed a series of business ideas or scenarios.” The number of business alternatives being considered can be reduced from here.

​What information do feasibility studies include?

Feasibility studies cover many important points, including economic, legal, operational and scheduling issues. Feasibility studies should be able to address questions across these topics and feature information about the technical feasibility of a project, as well as how much it will cost, whether it’s in accordance with the law, how operations will work and when it can be completed.

Market analysis research can also be a vital part of the feasibility study phase. This type of research is intended to ensure that there is demand for the metal or commodity a project may produce. Market research also helps to zero in on competition in the marketplace. This type of information on markets and demand is especially valuable for investors.

​What happens if the feasibility study results are positive? Negative?

Feasibility studies act as tools that provide CEOs and mining engineers with as much detailed information as possible to make intelligent and strategic decisions regarding the development of a project. Positive results will likely move the project forward as is. Decisions will vary, but reactions to negative results can include choices like canceling projects, bringing in partners, increasing investment or changing schedules.

Why should investors care?

Both prefeasibility and feasibility studies can provide investors with useful updates on the progress of a company’s project. These studies help create a more concrete picture about a company’s milestones and challenges moving forward.

This is an updated version of an article originally published by the Investing News Network in 2015.

Don’t forget to follow us @INN_Resource for real-time news updates!

Securities Disclosure: I, Melissa Pistilli, hold no direct investment interest in any company mentioned in this article.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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An evaluation of financial statement quality in pre-versus post-IFRS-7 implementation: the case of Iraqi banking industry

• Open access
• Published: 18 September 2024
• Volume 5 , article number  277 , ( 2024 )

Cite this article

You have full access to this open access article

• Esraa Esam Alharasis 1 ,
• Ahmad Marei 2 ,
• Ahmad Abdul-Rahman Almakhadmeh 1 ,
• Sarah Abdullah 3 &
• Abdalwali Lutfi 4 , 5 , 6 , 7 , 8  

This study compares Iraqi banking financial statements before and after IFRS-7 financial instruments disclosure. “Nijmegen Centre for Economics (NiCE)” qualitative content analysis was used to assess financial statement quality, which was measured using NiCE disclosure index. This study analysed data using a paired sample test to meet its goals. After IFRS-7, financial reporting quality is represented by 2016–2018. Before IFRS-7, it was represented by 2013–2015. In addition, the OLS regression analysis was implemented to assess hypotheses, incorporating data from 24 Iraqi institutions. The univariate analysis (t-test) and OLS regression showed that IFRS-7 improves financial statement “relevance, faithful representation, comparability, and timeliness”. No correlation was found for “understandability”. This study is the first to use Iraqi data and the most recent disclosure index to test the relationship between financial figure quality before and after applying the IFRS-7 financial instruments disclosure standard in developing countries. The results help regulators and policymakers regulate IFRS in Iraq and suggest policy changes to ensure compliance. The findings have major implications for business and policy that executives, lawmakers, and stockholders should consider. This study is applicable to ME countries that share comparable institutional, cultural, and accounting framework characteristics.

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1 Introduction

The global economy's huge interaction between the stock market and rising financial activity have made stock markets more interdependent. IASB developed IFRS in 2001 to harmonise accounting information due to the increased need for a unified accounting language. International organisations and multinational firms have become more influential due to globalisation. Globalisation boosted international capital markets. Due to globalisation, which increased international trade, IAS/IFRS is now essential [ 1 ]. By 2005, tens of thousands of companies used IAS/IFRS reporting [ 2 ]. Despite uncertain "cost–benefit trade-offs," several nations quickly adopted IFRS. IFRS cost–benefit trade-offs vary by country due to authorised, social, and organisational factors [ 3 ].

Reclassifying financial instruments was required by “IFRS 7—Financial Instruments: Recognition and Measurement (IFRS-7)” on October 13, 2008 [ 4 ]. In order to fairly present financial position and performance, IFRS-7 mandates companies to report financial instrument importance. Disclosures for “held for trading” (HFT), “available-for-sale (AFS)” and “held-to-maturity investments (HTM)” financial instruments are included. Market participants may expect IFRSs to provide more “transparent, comparable, and accountable” financial information [ 5 ]. IFRS-produced financial data is essential for global markets that make uniform economic decisions. Transparent and comparable financial data can help users identify global investment opportunities and risks [ 6 ]. IFRS standardises financial statement preparation and reporting. International reporting is cheaper with trusted accounting language [ 3 ]. Market players will easily understand and use this information in appraisals. IFRS-7 addressed accounting figures measurement quality, but financial instrument estimation complexity increases managerial bias and agency conflict [ 7 ]. The complexity of measuring and estimating financial instruments may affect financial report quality.

Professional accountants, regulators, and financial report users care most about financial reporting quality. Such reports are crucial to conveying the results of financial events and transactions within the organisation. This helps users assess a business's financial performance and condition and make economic decisions as directors. Financial reporting also provides information about management's stewardship, useful decisions, and stewardship evaluation [ 8 ]. Financial reporting currently aims to "provide financial information that is useful to users in making decisions relating to providing resources to the entity." The IFRS conceptual framework (2018) lists relevance and faithful representation as the fundamental qualitative characteristics of useful financial information. After the 2008 and 2010 exposure drafts, IASB published a conceptual framework for financial reporting to improve it. The frameworks discuss financial reporting goals and useful financial information's qualitative qualities. Qualitative conceptual framework characteristics are the main assets of financial information for decision-making. Thus, committing to financial reporting information's objective and qualitative characteristics ensures high quality [ 9 ]. Useful financial information includes financial statements and other financial information with qualitative characteristics. For financial data to be useful, it has to be “relevant” and "faithfully represent" what it claims to be, according to the recently updated IFRS conceptual framework from March 2018. Financial data is enhanced when it is “comparable, verifiable, timely, and understandable” [ 10 ].

In this globalised world, traditional accounting regulation is inadequate for foreign players. Developing countries are integrating their economies due to trade and commercial interests, political collaboration, and integrated economic systems [ 11 ]. This harmonises financial data. These changes are significant because developing nations' accounting standards limit their ability to attract foreign investors [ 3 ]. The most suitable accounting framework for economic growth is IFRS [ 12 ]. Arab-global trade benefits from IFRS standards' financial information transparency and comparability in growing markets [ 13 ]. Environment affects accounting practices, policies, and goals worldwide. Countries implement IFRSs because they improve financial report quality [ 14 ]. For global investors, IFRS improves financial reporting consistency and comparability [ 12 ]. By providing superior financial information to decision-makers, IFRS helps attract domestic and foreign investors [ 3 ]. Rich and poor countries can now compare financial reports as a result of IFRS convergence [ 15 ]. After IFRS, the company's financial statement quality is still questioned. Implementing the IFRS may improve financial statements because it uses advanced measurements based on current prices. Such measurements will better reflect the company's economic situation. IFRS limits management opportunism [ 16 ]. However, IFRS may hinder management's ability to present the real economy [ 16 ]. However, refs. [ 17 ] and [ 18 ] found that IFRS implementation did not improve financial information quality. The research results were inconsistent because financial information quality was not directly measured.

This study addresses the concerns raised by Khlif and Achek [ 19 ] regarding the dearth of research on IFRS implementation and the quality of financial reports, particularly in the Middle East, thereby filling the gap in the literature. The 1990 introduction of IFRS and the 2008 disclosure requirements of complex accounting figures in IFRS-7 encouraged additional research in this field. Upon conducting a thorough examination of the literature, we discovered that the majority of the evidence utilised quantitative methodologies to evaluate the quality of financial reporting. At the same time, only a few studies examined the impact of IFRS. Particularly, there has been a dearth of research that concentrates on IFRS-7 in the limited number of attempts that have been made to evaluate the quality of financial reporting during the implementation of IFRS for financial instruments [i.e., 20 – 24 ]. Prior studies on the quality of financial statements have employed only three characteristics (namely, comparability, relevance, and earnings management) to assess the accuracy of accounting statements, without including any reference to the adoption of IFRS. The company's performance and the credibility of its financial statements would both improve if management were more accountable, as per Petreski [ 22 ]. According to a study conducted by Ewert and Wagenhofer [ 24 ], the implementation of more stringent accounting standards has the potential to enhance the integrity of financial statements and reduce earnings management. Both [ 25 ] and [ 16 ] arrived at the same conclusion. However, Tendeloo and Vanstraelen [ 26 ] did not observe any modifications in the integrity of financial statements either before or after the implementation of IFRS [ 27 ].

Consequently, the objective of this investigation is to address the knowledge gap in different ways. First, it investigates the anticipated impact of the implementation of IFRS-7 on the integrity of financial reports. Second, the objective of this study is to evaluate the current body of literature by comparing financial reports that were prepared before and after the implementation of IFRS-7 to ascertain whether the latter has led to an enhancement in the quality of reporting. The authors are unaware of any published evidence regarding the impact of IFRS on the integrity of financial statements subsequent to the implementation of financial instruments. Third, this investigation employs both quantitative and qualitative methodologies to evaluate the integrity of financial reporting. The qualitative measurement approach employed is predicated on the “Nijmegen Centre for Economics (NiCE).” To evaluate the quality of financial reporting, the IASB and FASB have established qualitative characteristics, including “relevance, faithfully represent, comparability, verifiability, timeliness, and understandability.” NiCE has established an “index quality measurement” system that is predicated on these attributes [ 28 , 29 ]. Fourth, this analysis employs data from Iraqi banks. Consequently, the primary objective of this investigation is to examine the banking sector in Iraq, a developing nation. It is feasible that the reporting methodologies implemented in developed economies are inadequate due to empirical disparities among nations [ 30 , 31 ]. This analysis examines the capital markets in Iraq and the initial challenges associated with IFRS-7.

This study builds on previous work on financial statement/reporting quality by evaluating the impact of IFRS-7 using content analysis, a qualitative approach based on the IFRS conceptual framework and the NiCE theoretical foundation. The research is based on the construction of a disclosure index. To accomplish the research objectives, data was collected from 24 publicly traded Iraqi banking institutions between 2013 and 2018 and analysed. Financial statement quality in terms of “Relevance, faithful representation, comparability, and timing” has been improved as a result of applying IFRS-7 financial instruments disclosure, according to the results of the OLS regression. However, we did not discover a change in the mean of the “understandability” factor between the periods before and after IFRS-7. The implications of the findings for business and policy are substantial, and executives, legislators, and stockholders should duly consider them. This study should help the Iraqi government draft more detailed accounting laws and regulations. This has the potential to simplify the disclosure and measurement process while ensuring its quality and safeguarding investors. Investors contemplating investing capital into Iraqi businesses may find clarity and interpretation to be crucial, according to the results of this study. In a broader sense, this study's results apply to countries in the Middle East (ME) that share similar cultural, accounting, and institutional features.

The paper is structured as follows. Section  2 outlines literature evaluation and hypothesis development. Section  3 contains study data and methodology. The findings and discussion are in Sect.  4 . Sections  5 concludes the paper.

2 Literature review

Accountants, regulators, and other financial report users care most about financial reporting quality. Such reports are crucial for communicating results of financial events and transactions within the organisation. Users can use this information to evaluate a business's financial performance and make economic decisions as directors. Financial reporting also provides management stewardship information and useful decision-making information [ 8 ]. As of now, financial reporting aims to “provide financial information that is useful to users in making decisions relating to providing resources to the entity.” Relevance and faithful representation are the fundamental qualitative characteristics of useful financial information, according to the IFRS conceptual framework (2018). Financial statements and other financial information meet the qualitative criteria for usefulness. According to the March 2018 IFRS conceptual framework update, financial information must be “relevant” and “faithfully represent” what it purports to represent. Comparable, verifiable, timely, and understandable financial information is more useful [ 10 ].

2.1 Fundamental characterstics

Financial statements need fundamental qualitative characteristics to be useful. Relevance and faithful representation (which replaced reliability) are the fundamental qualitative characteristics of financial reporting, according to the revised conceptual framework in 2014. First, relevance: The IASB's fundamental qualitative characteristic, relevance, requires entities to report timely, predictive, and confirmatory accounting information. The IASB's Basis of Conclusion (2016) states that relevance is only useful for decision-making if it can make a difference. Lennard [ 32 ] states that financial reporting allows management to account for the entity's affairs to financial statement users. He also claimed that financial reporting is about informing financial statement users of the company's results and performance to help them make investment decisions. Financial statements must contain relevant information for users to make decisions [ 32 ]. If users can use reported data to predict output, it has predictive value. Confirmatory financial statement information provides feedback or confirms previous valuations. Barker et al. [ 33 ] believe researchers focus on earnings quality rather than financial reporting quality for relevance. They argue that focusing on earnings quality ignores non-financial information and excludes future financial information from reporting entities' annual reports, such as future transactions and contingent contracts. Predictive values are most relevant to financial information usefulness. Predictive value indicates relevance, according to [ 34 ]. They measure predictive values using three items, starting with financial statements that predict company futures. Annual reports' forward-looking statements are measured here. Second, they disclose business risks and opportunities. Annual reports disclose non-financial information like this. Thirdly, it reports company financials using fair-value accounting rather than historical cost. Fair value accounting provides more current and relevant data than cost accounting [ 35 ].

Second, faithful representation: Accounting information must be reliable or faithfully presented. The conceptual framework defines reliability as user-reliable information that improves decision-making. Financial statement users usually base their decisions on annual reports or financial statements. Financial statement users will make poor choices with unreliable information. In 2010, the conceptual framework changed ‘reliability’ to ‘faithful representation’ [ 36 ]. The IASB's conceptual framework requires financial statements to faithfully present relevant phenomena and represent them. Complete, neutral, error-free financial information is required for accurate presentation. Faithful financial statement presentation does not guarantee completeness, accuracy, or error-freeness. The conceptual framework (2018) recognises that perfection is rare. This makes accurate and error-free financial statements unlikely. When financial statements have no material misstatements, they are reliable, complete, and faithful. Even though the company can provide mitigating factors, financial statements are prepared by humans and may contain errors. Financial statements that are complete, neutral, and useful are trustworthy. For faithful presentation, financial statements should not contain material misstatements. Completeness and verifiability are also part of financial statement faithful presentation. Complete financial information includes all descriptions and explanations needed to understand the data. This is achieved by reporting relevant, complete information about the reporting entity [ 10 ].

2.2 Enhanced characterstics

Financial statements need enhanced qualitative characteristics to be useful. Enhancing qualitative characteristics of financial reporting must be met and treated as equally important as fundamental qualitative characteristics to achieve the objective. Ernst and Young [ 37 ] state that qualitative financial reporting characteristics must be maximised individually and in combination to improve relevance and faithfulness. First, understandability. The IASB's conceptual framework defines understandability as when the quality of reported financial information allows users to understand the financial statements' asserted meanings. The Basis of Conclusion (2016) states that understandability is a qualitative trait that helps users understand reported information, making it useful for decision-making. Financial information must be clear for users to understand. Classifying, describing, and presenting information clearly is understandability. Ernst and Young [ 37 ] argued that financial information meets conceptual framework understandability criteria when classified, characterised, and presented clearly and concisely. The conceptual framework recognises that financial statements must be understandable, but some phenomena are inherently complex (based on its standards) and cannot be simplified. Unfortunately, excluding such information from financial reports would make them incomplete and potentially misleading to users. Thus, annual financial statements include notes and disclosure to simplify some items. Financial information is useless unless users can understand and value it [ 10 ]. Users of financial statements are more likely to base their economic decisions on reported financial statements if they are understandable and unlikely to negatively impact their decisions. Understandable information helps financial statement users make decisions.

Second, financial information comparability was important in 1989. Comparability is a quality of the relationship between two or more pieces of information and is secondary to relevance and faithful presentation, according to the Conceptual Framework 2010 [ 37 ]. The ref. [ 38 ] argued that comparability is equally important. Comparability is a qualitative trait that lets users compare two economic phenomena. In ref. [ 10 ] argued that comparability lets users see similarities and differences between the reporting entity's current and prior financial statements or those of similar industry entities. Financial information about an enterprise is very useful if it can be compared to accounting information from previous reports or industrial reports, according to the IASB's Conceptual Framework (2013). Comparability can occur when companies in the same industry treat similar transactions the same way and that accounting standards improve comparability. Chatterjee et al. [ 39 ] believe comparability has two components: comparability over time and comparability between entities. Jonas and Blanchet [ 40 ] suggested asking if the information is prepared for informed comparison with other companies. Third, timeliness: means that financial information is available to financial statement users in time to improve decision-making, so users need the latest information to make decisions. The IASB's Conceptual Framework states that financial statement users may use old information to make decisions, such as analysing firm trends. Generally, financial statement users need timely information. Without a comparability or trend analysis, historical data could mislead decision-making [ 10 ].

2.3 Hypotheses developments

Due to globalisation, which increased international trade, IAS/IFRS is now essential [ 1 , 41 ]. This increases the need for global investment agreements. Market demands for current, decision-making financial information have increased dramatically [ 42 , 43 ]. Thus, IASB created IFRS, a single set of high-quality financial standards for all listed firms worldwide. High-quality financial standards would improve international financial statement soundness and comparability [ 16 , 44 ]. After seven years of IAS/IFRS adoption in Iraqi firms since 2016, the effects on their reported financial statements are clear. After firms worldwide implement IFRSs, researchers are interested in the debate over shifting to IFRS and its economic effects [ 45 ]. Most IFRS research shows that it reduces earnings management, but its effects on financial information value relevance vary by country. Firms with strong enforcement regimes benefit more from IFRS adoption. Armstrong et al. [ 46 ] also said harmonised accounting standards reduce information asymmetry. Thus, information comparability and transparency increased, benefiting capital markets. International accounting standards improve capital market efficiency by increasing firm financial reporting consistency and transparency across countries [ 47 ]. IFRS financial information is more comparable, allowing investors to trade and invest internationally [ 48 ]. Houqe [ 48 ] claims that IFRS reduced information asymmetry and improved financial information for decision-making. IFRS increases the value relevance of financial information, the main indicator of its usefulness. Higher value relevance of financial information increases book value and earnings, lowering investment risk because investors rely on less information [ 49 ]. In a sample of German and Italian firms, Cascino et al. [ 50 ] examined how IFRS implementation affects financial information comparability. The impact was minor, and regional and country-level incentives helped compliance, according to the research. The researcher also found that these incentives improved financial information comparability.

Many IFRS proponents believe that IFRS reporting improves financial information transparency and comparability, reduces information asymmetry, and lowers capital market costs [ 51 ]. Hung and Subramanyam [ 52 ] found that IFRS implementation in German industrial firms does not improve financial information value relevance or timeliness. Al‐Htaybat [ 3 ] say IFRS improves financial market quality and stability. Financial information is also more comparable after IFRS implementation [ 53 ]. Barth and Israeli [ 9 ] found that countries that require IFRS have accounting numbers more like US GAAP than those that use domestic standards. Yip and Young [ 54 ] examined financial information comparability in 17 EU countries after IFRS implementation in 2005. IFRS makes financial information more comparable for firms from similar instructional environments than those from different instructional environments. Brochet et al. [ 53 ] noted that UK domestic standards are similar to IFRS, which may reduce financial information comparability after IFRS implementation. Additionally, firms that follow domestic standards similar to IFRS can benefit from IFRS adoption. Barth and Israeli [ 9 ] found that voluntary IFRS adoption in 21 European countries reduced earnings management, time recognition, and financial information value relevance. Similarly, Leuz and Verrecchia [ 55 ] found that Germany firms' voluntary adoption of international accounting standards reduced information asymmetry in bid/ask spreads, share price volatility, and trading volume. Daske and Gebhardt [ 56 ] found that 26 countries' stock-market liquidity and cost of capital increased in the first year of IFRS adoption, especially in countries with strong enforcement mechanisms. Some scholars believe convergence and harmonisation to IFRS have improved financial reporting quality rapidly [ 57 ]. Ball [ 5 ] said IFRS helps investors make decisions by providing complete, accurate, and timely information. Street [ 58 ] says IFRS implementation is the best way for investors and stakeholders to compare harmonise statements.

Conversely, adopting IFRS is criticised. Opponents of IFRS implementation have highlighted accountants, auditors, and users' poor professional and technical skills [ 59 ]. Increased convergence and transition costs [ 60 , 61 ]. Hellman [ 62 ] noted that moving to IFRS raises concerns about training and education and weak government enforcement mechanisms that can boost compliance. Jones et al. [ 63 ] also noted that IFRS makes financial statements more ambiguous, requiring additional disclosures and clarifications to make them understandable. Also, Cascino et al. [ 50 ] claimed that post-IFRS implementation in one country has led to less comparable financial information than domestic GAAP. Liao et al. [ 64 ] found that post-IFRS earnings and book values were less comparable in France and Germany. Brüggemann et al. [ 65 ] did not provide empirical evidence on how IFRS improves financial information transparency and comparability. Post-IFRS accounting statements are not more comparable, especially in countries with weak enforcement. Lin et al. [ 23 ] said IFRS had lower earnings quality than US GAAP.

In developing countries, empirical studies on IFRS adoption's economic effects are scarce. Using a sample of 46 Kenyan listed firms from 2005 to 2007, [ 15 ] examined the relationship between IFRS and foreign ownership and share turnover. Foreign ownership and share turnover are positively correlated with IFRS compliance. Arab countries adopted IFRS to follow global firms' reporting standards, according to Fiechter et al. [ 66 ]. Arab countries must adopt IFRS to demonstrate transparency and comparability in accounting reporting to their trading partners [ 67 ]. IFRS adaptation by trading partners is partly due to their accounting systems. IFRS implementation in such regions is limited by cultural, legal, and taxation systems [ 68 ]. Thus, this region’s representation on the IASB would help develop and update accounting standards for the region. Turel [ 69 ] used price models to study the effects of IFRS implementation on book value and reported earnings in Turkey and Malaysia, two developing countries. After IFRS adoption, book values and earnings were more relevant in Turkey. Earnings value relevance has improved, but book value relevance has decreased since IFRS adoption in Malaysia. Elbannan [ 70 ] examines the impact of IFRS-encouraged updated EASs in Egypt since 2006. The findings show that such adoption has not reduced earnings management.

In some Arab, developing markets, adopting IAS/IFRS standards is essential to increase financial information transparency and comparability, which boosts Arab-global trade. IFRSs improve financial report quality, which is why those countries implement them [ 71 ]. In addition, IFRS improves financial reporting consistency and comparability for global investors. Professional accountants, regulators, and financial report users care most about financial reporting. Reports on financial transactions are essential. According to ref. [ 72 ], this information helps users make business decisions based on the financial performance and state of a business. IFRS objectives to improve firm financial statements emphasise financial reporting transparency, according to the IASB. The IFRS mission is to develop Standards that bring transparency, accountability, and efficiency to global financial markets. We promote global economic trust, growth, and financial stability for the public good [ 2 , 73 , 74 ]. Hypotheses were formatted as follows based on theoretical evidence:

Hypothesis: In Iraqi banking industry, the implementation of IFRS-7 has a significant impact on financial statement quality.

Hypothesis 1.: In Iraqi banking industry, the implementation of IFRS-7 has a significant impact on financial statement quality—in relation to value relevance.

Hypothesis 2: In Iraqi banking industry, the implementation of IFRS-7 has a significant impact on financial statement quality—in relation to faithful representation.

Hypothesis 3: In Iraqi banking industry, the implementation of IFRS-7 has a significant impact on financial statement quality—in relation to understandability.

Hypothesis 4: In Iraqi banking industry, the implementation of IFRS-7 has a significant impact on financial statement quality—in relation to comparability.

Hypothesis 5: In Iraqi banking industry, the implementation of IFRS-7 has a significant impact on financial statement quality—in relation to timeliness.

3 Data and the selection of the sample

3.1 collecting data and focus.

The data was sourced from annual reports of Iraqi banks that were published on the website of the Iraqi bourse between 2013 and 2018. This analysis was initiated in 2013 to reduce the effects of the "Global Financial Crisis (GFC)" and was terminated in 2018 to mitigate the impact of the Covid-19 pandemic on the results. The initial population of the study comprises 54 banks that operate in Iraqi capital markets. 24 banks were excluded due to their affiliation with the Islamic banking system, which has its unique characteristics, and 36 banks were excluded due to the absence of data (refer to Table  1 ). Consequently, the final selected sample for the research is comprised of the remaining 24 banks which is generally. The selected sample number is aligned with prior research [ 20 , 21 , 22 , 23 ]. 144 annual reports from 24 Iraqi banks were evaluated and examined to assess the quality of accounting information.

3.2 Research model

The objective of this study is to conduct an empirical assessment of the quality of financial statements subsequent to the adoption of IFRS-7. To accomplish this, we employ a qualitative methodology to assess the quality of financial reporting in this study. The employed qualitative measurement approach is predicated on the “Nijmegen Centre for Economics’s (NiCE)” measurement system. NiCE constructs a comprehensive metric for assessing the quality of financial reporting in the form of an index quality measurement utilising the qualitative attributes of the FASB and IASB [ 10 , 28 , 29 ]. This metric is divided into two primary categories: enhanced (comprising understandability, comparability, and timeliness) and fundamental (comprising faithful representation and relevance). An OLS regression model with time-varying fixed effects is employed in this. This analysis employs the proportions of each financial information category, which are proxies for "financial statement quality" as defined by IFRS. The focus is on the Iraqi banking industry, and the implementation of IFRS-7 is examined in relation to this indicator in developing nations. The following modified equations are used in the proposed research paradigm to test hypotheses:

Baseline equation : \(\begin{aligned} FSQ = & \, \delta 0 + \, \delta 1IFRS7 + \delta 2L\_ASSET + \delta 3ROI + \, \delta 4DEB + \, \delta 5GROWTH \\ & + \, \delta 6BIG4 + \delta 7CHANGE + \, \delta 8OPINION + \, FE + \varepsilon \\ \end{aligned}\)

Modified equations:

Eq. (1/H1): \(\begin{aligned} RELEVANCE\, = \, & \delta 0\, + \,\delta 1IFRS7\, + \,\delta 2L\_ASSET\, + \,\delta 3ROA\, + \,\delta 4DEB\, + \,\delta 5GROWTH\, \\ & + \,\delta 6BIG4\, + \,\delta 7CHANGE\, + \,\delta 8OPINION\, + \,FE\, + \,\varepsilon \\ \end{aligned}\) .

Eq. (1/H2): \(\begin{aligned} FAITHFUL\_REP\, = & \,\delta 0\, + \,\delta 1IFRS7\, + \,\delta 2L\_ASSET\, + \,\delta 3ROA\, + \,\delta 4DEB\, + \,\delta 5GROWTH\, \\ & + \,\delta 6BIG4\, + \,\delta 7CHANGE\, + \,\delta 8OPINION\, + \,FE\, + \varepsilon \\ \end{aligned}\) .

Eq. (1/H3): \(\begin{aligned} UNDER\_STAND\, = & \,\delta 0\, + \,\delta 1IFRS7\, + \,\delta 2L\_ASSET\, + \,\delta 3ROA\, + \,\delta 4DEB\, + \,\delta 5GROWTH \\ & \, + \,\delta 6BIG4\, + \,\delta 7CHANGE\, + \,\delta 8OPINION\, + \,FE\, + \varepsilon \\ \end{aligned}\) .

Eq. (1/H4): \(\begin{aligned} COMPARA\, = & \,\delta 0\, + \,\delta 1IFRS7\, + \,\delta 2L\_ASSET\, + \,\delta 3ROA\, + \,\delta 4DEB\, + \,\delta 5GROWTH\, \\ & + \,\delta 6BIG4\, + \,\delta 7CHANGE\, + \,\delta 8OPINION\, + \,FE\, + \,\varepsilon \\ \end{aligned}\) .

Eq. (1/H5): \(\begin{aligned} TIMELINESS\, = & \,\delta 0\, + \,\delta 1IFRS7\, + \,\delta 2L\_ASSET\, + \,\delta 3ROA\, + \,\delta 4DEB\, + \,\delta 5GROWTH\, \\ & + \,\delta 6BIG4\, + \,\delta 7CHANGE\, + \,\delta 8OPINION\, + \,FE\, + \varepsilon \\ \end{aligned}\) .

3.3 Variables measurements

3.3.1 measurement of financial reporting quality.

This study employs content analysis, a qualitative methodology grounded in the IFRS conceptual framework and NiCE theoretical foundation, to assess the effects of IFRS-7 on the aforementioned aspects. This is achieved through the construction of a disclosure index. Consequently, the present study assessed the qualitative attributes of financial statements following the methodologies proposed by refs. [ 29 , 34 ] and the NiCE measurement model [ 28 ]. Each category was evaluated through a series of inquiries, and the scores were derived from the financial report of the organisation. Qualitative content analysis was employed to gather and evaluate the data. We scrutinise the annual reports of all banks, devoting particular scrutiny to each of the five indicators that assess the qualitative attributes of financial information. We were thus capable of categorising 144 annual reports into five discrete groups via coding. Previous studies have underscored the significance of the subcategories as per the IFRS criteria for pertinent information, accurate depiction, comprehensibility, comparability, and promptness [ 29 , 36 , 75 ]. Data were compiled on an annual and bank-by-annual basis. 21 overarching themes and subcategories were derived from the classification of the five categories [ 29 ]. We utilise a checklist formulated by previous research to ascertain the number of instances of each category that were incorporated in the study reported by the Iraqi bank [ 29 ]. In particular, we determined the frequency of occurrence for each of 21-IFRS statements in the annual reports of Iraqi banks. We developed a checklist of qualitative characteristics mentioned in each report using an unweighted approach. The list of items is presented in Appendix A, Table 8 .

To ascertain the score for a specific category, which encompasses relevance, faithful representation, understandability, and comparability, the total actual score of its sub-categories is divided by the total number of sub-categories on the checklist. The natural logarithm of the number of days it took for the auditor to sign the auditors' report after the end of the financial year was employed to measure the timeliness category. The mean values for each category and sub-category are presented in Appendix A, Table 9 . This analysis enhances the credibility of the measurement instrument utilised in the research and raises the likelihood that it can be applied universally and locally to other nations. The existing body of research on the influence of qualitative attributes on the utility of financial reporting with respect to timeliness is relatively scarce. As the foundational qualitative attributes of financial reporting, relevance, dependability, and comprehensibility have occupied the majority of research on qualitative characteristics [ 75 ].

3.3.2 Measurement of IFRS-7 variable

Following prior research [ 12 , 66 , 72 ] IFRS-7 in the current study is measured as a dummy variable coded 1 for the years subsequent to the IFRS-7 implementation by Iraqi banks (2016–2018) while 0 otherwise of the years (2013–2015). The measurements of variables are provided in Table  2 .

4 Results and discussion

4.1 descriptive statistics and correlation statistics.

The descriptive statistics of the utilised variables have been provided in Table  3 . The mean value of timeliness is the highest of 2.086 followed by understandability and comparability of 0.825 and 0.824 while relevance and faithful representation are the lowest with mean of 0.727 and 0.61. It can be noticed that the current study’s period is divided into two different periods of pre vs. post-IFRS-7 implementation with an average 0.50 for each period.

Table 4 presents the Spearman correlation matrix values for the dependent and independent variables. In regression models, the multicollinearity test eliminates correlation between independent variables. Each model’s VIF test mean is less than 2, which eliminates any concerns regarding multicollinearity. The significant positive correlation between the implementation of IFRS-7 and several financial statement quality factors (namely, relevance, faithful representation, comparability, and timeliness) is confirmed by the Spearman correlation. Nevertheless, no such correlation is observed with regard to the understandability factor.

4.2 Mean difference and regression results

Table 5 contains the parametric (independent t-test) outcomes. Using data from 2013 to 2018, this table compares the mean of each category of financial statement quality—“relevance, faithful representation, understandability, comparability, and timeliness—as required by disclosure regulations prior to and subsequent to IFRS-7. The table illustrates that the average of each category has experienced a statistically significant increase subsequent to the implementation of IFRS-7 in the following areas: relevance, faithful representation, comparability, and timeliness, with the exception of the understandability factor. The respective means for faithful representation, timeliness, relevance, and comparability were 0.861, 0.661, 0.870, and 2.151, with corresponding significant coefficients (t-values) of − 6.7943, − 2.4995, − 2.7917, and − 2.5984. On the contrary, the mean of understandability was determined to be insignificant, with a t-value of − 1.3147 and a mean of 0.839.

Following the t-test results, regression analyses are presented in Tables 6 through 7 . Models 1–2 of Table  6 illustrate the extent to which the refinements of IFRS-7 impact the quality of financial statements with respect to value relevance and faithful representation. Significant P-values for the models begin at 0.01, and their explanatory power can reach 30%. According to the findings presented in Models 1–2, the implementation of IFRS-7 has a noteworthy and positive impact on the faithful representation of financial statement data (Coeff. = 0.074, and t = 1.740) and value relevance (Coeff. = 0.261, and t = 6.02). This result is consistent with the IFRS conceptual framework update published in March 2018, which affirmed that financial information must be "relevant" and "faithfully represent" its intended meaning in order to be deemed useful. The findings are in accordance with the agency theory and the theoretical foundation that have been previously discussed in research [ 2 , 77 ]. This confirms that IFRS is essential in improving the quality and utility of accounting information, as historical accounting is no longer pertinent to economic decision-making [ 78 , 79 ]. According to the agency theory, the implementation of IFRS resulted in a decrease in information asymmetry and an improvement in the quality of financial data used in decision-making [ 48 ]. In particular, the results of the analysis are consistent with the case of developing regions, specifically ME. For instance, [ 2 ] conducted an analysis that verified the efficacy of IFRS in enhancing audit quality, which in turn fosters the transparency and informativeness of financial statements in Jordan [ 80 ]. Consequently, this enables users to make business decisions that are informed by the financial performance and current state of a business. Thus, the analysis accepts H1 and H2.

The findings from Models 3–5 of Table  7 indicate that the implementation of IFRS-7 has resulted in enhanced comparability (Coeff. = 0.96, and t = 2.59) and timeliness (Coeff. = 0.125, and t = 2.23), respectively, of financial data. However, no correlation between financial data quality and understandability was identified through the analysis. This outcome supports the assertion that conventional accounting regulation mechanisms have become inadequate for foreign participants in this globalised economy. Developing countries are being compelled to integrate their economies by political collaboration, integrated economic systems, and closer trade and commercial interests [ 11 , 76 , 81 ]. In doing so, these components harmonise financial data. Traditional accounting norms and practices impede the capacity of developing nations to attract foreign investors; consequently, these developments are consequential [ 3 ]. The accounting system that is most effective in measuring economic growth is IFRS [ 12 ]. IFRS standards in emerging markets facilitate and enhance the promotion of Arab-international trade and financial information comparability [ 82 ]. The environment has an impact on accounting standards, procedures, policies, and objectives. These nations have adopted IFRSs due to their ability to improve the content of financial reports [ 14 ]. Additionally, IFRS improves the consistency and comparability of financial reporting for investors worldwide [ 12 , 80 , 83 ]. Consequently, the analysis admits H4 and H5, while rejecting H3.

The analysis confirmed that the utility of accounting information was improved in terms of its value relevance, faithful representation, comparability, and timeliness as a result of the adoption of IFRS-7. Additionally, the accountant's measurement and disclosure of the complex accounting figures required by IFRS-7 do not impact the level of comprehension among stakeholders. We have discovered evidence that the quality of financial reporting in Iraq experienced a transformation both before and after the implementation of IFRS. The quality level has improved since the implementation of IFRS, as evidenced by an analysis of the mean value of the financial reporting quality measurement, as opposed to the pre-adoption period. The results are consistent with the research conducted by ref. [ 2 ], which showed that the quality of accounting in Jordan improved as a result of the implementation of IFRS. Barth et al.'s research indicates that both value relevance and timely loss recognition have increased. In comparison to the utilisation of US GAAP, the significance of financial reporting is further enhanced by the application of IFRS, as per refs. [ 28 , 29 ].

The research also demonstrated that the information a company presents is more accurate in reflecting its actual state when IFRS is implemented, suggesting that faithful representation is enhanced. This study provides additional evidence that comparability has improved since the implementation of IFRS, building on the results of the previous investigation. This research suggests that the organisation provides a more comprehensive array of financial information that is more comparable to that of its competitors as a result of the adoption of IFRS. However, there has been no significant improvement in the area of comprehensibility. This is due to the organization's obligation to provide supplementary disclosures in accordance with the IFRS-7 criteria. The issue is hypothesised to have been exacerbated by the pervasive use of uncertain measures and estimations of complex financial assets and liabilities in the presentation of financial information in accordance with the IFRS-7 instructions [ 84 ]. We have discovered evidence that the quality of financial reporting in Iraqi institutions underwent a transformation both before and after the implementation of IFRS. In other words, our research indicates that the integrity of financial reporting has improved since the implementation of IFRS. Additionally, the implementation of the principle-based standard improves the quality of financial reporting by increasing the number of disclosures.

5 Conclusion, implications, limitations and future research

The purpose of this study is to investigate the quality of the financial statements in the Iraqi banking sector both before and after the implementation of the IFRS-7 financial instruments disclosure standard. The qualitative content analysis that was developed by the “Nijmegen Centre for Economics (NiCE)” has been utilised in order to investigate the quality of financial statements. As a result, the NiCE disclosure index has been utilised in order to measure the level of quality that is present in financial statements. An analysis of the data was performed using a paired sample test in this research project so that the objectives of the study could be determined. The years 2013–2015 are used to illustrate the quality of financial statements prior to the adoption of IFRS-7 (also known as “pre-IFRS-7”), while the years 2016–2018 are used to illustrate the quality of financial reporting that occurred after the implementation of IFRS-7 (also known as “post-IFRS-7”). The findings of the univariate analysis, which consisted of a parametric independent t-test, confirmed that the use of IFRS-7 for the disclosure of financial instruments tends to result in the conclusion that the implementation of IFRS-7 for the disclosure of financial instruments has led to an improvement in the quality of financial statements in terms of relevance, faithful representation, comparability, and timing despite the fact that we were unable to discover any correlation with regard to the term “understandability.”

This study aims to evaluate the correlation between the quality of financial figures in developing settings before and after the implementation of the IFRS-7 financial instruments disclosure standard. Furthermore, this investigation is the first to employ Iraqi data to implement the most recent disclosure index. The findings are beneficial to policymakers and regulators in Iraq, as they can be employed to regulate the application of IFRS and recommend policy enhancements to ensure compliance with IFRSs. The findings are of significant importance to executives, legislators, and stockholders, as they have significant implications for both business and policy. Academics, the preparers community, and government institutions in Iraq that are responsible for the implementation of IFRS benefit from this study. This research is advantageous for regulatory agencies that supervise and regulate the accounting industry in Iraq. This addition broadens the applicability and viability of the analysis's conclusions to a broader range of contexts, including Middle Eastern nations with comparable institutional and cultural characteristics, as well as auditing and accounting practices.

The sample size and time range may limit the usefulness of the results; therefore, future research should broaden this approach to include other contexts and sectors, and this analysis can be expanded to include data from 2019 to 2023. Furthermore, the findings of the study present new opportunities for research in the future. For example, it is possible to investigate the impact of Covid-19 on a variety of aspects of financial reporting, such as earnings management practices and the role that business governance legislation plays.

Data availability

The data used to support the findings of this study are available upon request. However, please note that the data for this article were generated as part of research project (Masters dissertation) prepared by Sarah Abdullah at Mutah University. To protect intellectual property rights, the data cannot be shared without prior permission from Mutah University.

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Alharasis, E.E., Marei, A., Almakhadmeh, A.AR. et al. An evaluation of financial statement quality in pre-versus post-IFRS-7 implementation: the case of Iraqi banking industry. Discov Sustain 5 , 277 (2024). https://doi.org/10.1007/s43621-024-00487-w

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