Credit Risk Assessment Research Articles

AI-Driven Fraud Detection in Banking: Enhancing Transaction Security

An important step forward in risk management and fraud detection has been achieved with the integration of Artificial Intelligence (AI) in the banking sector. In this paper, we take a look at how AI has revolutionized various fields, shedding light on the benefits and drawbacks of this technology. The effects of AI on risk management are complex. More complex credit risk assessment models are made possible by algorithms that can see patterns in massive datasets that people might miss. When it comes to market and liquidity issues, real-time transaction monitoring is absolutely essential for quick risk mitigation. Automating compliance with regulatory norms is another critical function of AI, which helps to decrease human mistake and assures quick adaptability to changes in regulations. The automation of mundane processes and the reinforcement of cybersecurity measures further reduce operational risks. By examining client behaviour and transaction data, enhanced algorithms may adeptly spot anomalies that could indicate fraud. Artificial intelligence's capacity to foresee future events enables it to foil possible fraud attempts. The systems are designed to respond to changing fraudster strategies with its adaptive learning feature.

Leveraging artificial intelligence in economics and finance: Enhancing decision-making and market efficiency

Abstract. Artificial Intelligence (AI) has revolutionized the fields of economics and finance by providing advanced tools for decision-making, predictive analysis, and market efficiency. This paper examines the integration of AI technologies such as machine learning and natural language processing with mathematical models, specifically ARIMA for economic forecasting, Black-Scholes for option pricing, and logistic regression for credit risk assessment. By enhancing these models with AI, we demonstrate significant improvements in prediction accuracy and decision-making capabilities. Case studies illustrate a 15% improvement in GDP growth prediction accuracy, a 20% reduction in option pricing errors, and a 20% decrease in credit default rates. The paper also discusses the future prospects of AI, including advancements in quantum computing and ethical considerations like data privacy and algorithmic bias. Implementation challenges such as high costs and data integration issues are addressed, providing a comprehensive roadmap for organizations to effectively leverage AI. This study underscores the transformative potential of AI in shaping the future of economic and financial landscapes, driving innovation and operational efficiency.

Enhancing credit risk assessments of SMEs with non-financial information

Enhancing credit risk assessments of SMEs with non-financial information

Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score’s calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam’s razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method’s efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models.

Leveraging Data Science Technology for Advancing Credit Risk Assessment

The evaluation of credit risk (CR) has become prominent in recent years, particularly among banks, as default rates are on the rise and economic insecurity remains persistent. Traditional credit scoring techniques oftentimes are inadequate and provide little means for risk estimation, necessitating the development of new models using data science methodologies. In this study, a novel Intelligent Dwarf Mongoose tuned Light Gradient Boosting Machine (IDM-LGBM) model that boosts the accuracy of CR and improves forecasting performance, is introduced. The Light Gradient Boosting Machine model's hyperparameters were optimized using the Intelligent Dwarf Mongoose technique, improving the model's predictive strength. The CR dataset was gathered from the Kaggle platform. The data is then pre-processed using Z-score normalization. To evaluate the efficiency of the suggested IDM-LGBM technique, which has been implemented employing a Python platform. Results show that the IDM-LGBM model performed significantly better than conventional methods in terms of recall (98.1%), accuracy (97.2%), F1-score (97.4%), and precision (96.5%). Subsequent studies could concentrate on addressing real-time data streams and enhancing models to respond to the changing credit environment.

Creditworthiness Criteria According to the 5Cs Model and Credit Decision: The Moderating Role of Intellectual Capital

The current study aims to examine how creditworthiness criteria based on the (5Cs) model with its dimensions (character, capacity, capital, conditions, and collateral) on the credit decision in the presence of intellectual capital as a moderating variable applied to the branches of Jordanian commercial banks in the southern governorates of Jordan. The study included 449 populations from these branches, with a sample size of 205 employees. The authors employed a quantitative approach and utilized Macro PROCESS v4.2 by Andrew F. Hayes for analysis within SPSS v.28 software to achieve the study objective and test its hypotheses. According to the findings of the study, there is a positive effect of creditworthiness criteria based on the (5Cs) model on the credit decision. Furthermore, the study discovered that there is a presence of a moderating role of intellectual capital on the relationship between creditworthiness criteria based on the (5Cs) model and the credit decision. The study recommends that banks should prioritize investing in training programs for employees to improve their ability to assess credit risk accurately.

Credit Anomaly Detection Method based on Bayesian Networks

Due to the virtual nature of online transactions and the underdeveloped personal credit risk assessment system of Internet finance lending platforms in China, these platforms often struggle to verify the creditworthiness of applicants prone to fraud and default, leading to loan losses. As a result, risk control has become the core of Internet lending platforms. Modern risk control relies on statistical and data mining techniques to analyze and model data, uncover patterns that indicate loan defaults, and identify anomalies. This paper proposes a Bayesian network-based credit anomaly detection method, which detects anomalies by calculating and ranking the joint anomaly probabilities of sample instances. The technique operates under unsupervised learning and can effectively handle missing and imbalanced data. This method analyzes a loan credit dataset with 32,581 samples and 12 features of the customers. The result of the analysis demonstrates the feasibility and effectiveness of this method in detecting anomalies in online loans.

Effect of Credit Risk Management Techniques on Financial Performance of Deposit Taking SACCOs in Kenya

Financial performance is crucial in the realm of finance. The requirement to explain why two organizations operating in the same environment perform differently is a puzzle, and various financial research works have been devoted to solving this puzzle. Co-operative societies in Kenya are recognized as a significant contributor to national development since their presence can be traced in virtually all sectors of the economy. Although significant progress has been made by the deposit taking SACCOs in Kenya, their performance and sustainability has been debatable. The objective of this research was to assess the effect of financial risk management techniques on financial performance of deposit taking SACCOs in Kenya. The specific objective was to determine the effect of credit risk management techniques on financial performance of deposit taking SACCOs in Kenya. The study was based on Credit Risk Theory. A descriptive survey design was adopted. The population of the study was the 175-deposit-taking SACCOs in Kenya as of 31st December 2022. Due to relative population nature, sampling was not conducted and therefore the study was a census. The respondent was the risk manager of each deposit-taking SACCO. The study utilized primary data. The data was collected using a structured questionnaire. Data was analyzed using descriptive statistics such as the mean and standard deviation and inferential statistics which included correlation and regression analysis. Reliability tests were conducted on the variables each meeting the 0.7reliability threshold with validity tests being confirmed by the Bartlett’s test that confirmed validity of the variables being measured. The regression analysis revealed significant insights into the relationship between financial risk management techniques and the financial performance of deposit-taking SACCOs in Kenya. Credit risk management techniques were found to have a positive impact on financial performance with a regression coefficient of β = 0.238 and a significant value of P <0.005. In conclusion, the study provides compelling evidence of the importance of comprehensive risk management frameworks in driving the financial performance of deposit-taking SACCOs in Kenya. By implementing effective risk management practices tailored to address credit risk, SACCOs can enhance their stability, profitability, and resilience in a dynamic operating environment. These findings have significant implications for policymakers, practitioners, and stakeholders in the SACCO sector, highlighting the need to prioritize investments in risk management capabilities to promote sustainable financial growth. The study recommended that SACCOs should focus on enhancing their credit risk management frameworks. This can be achieved by implementing robust credit risk assessment mechanisms, such as comprehensive credit scoring models and regular analysis of customer credit histories.

Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates.

Estimating Risk of Debt Instruments Using the CreditMetrics<SUP>TM</SUP> Method: On the Example of JSCMB ‘Ipoteka-Bank’, Uzbekistan

In the article, credit risk assessment metric of a debt financial instrument, in particular a corporate Eurobond, is studied within the framework of the relevance of risk assessment in the financial sector due to the dangers and complexities of time. Joint-Stock Commercial Mortgage Bank ‘Ipoteka-Bank’, operating in the banking sector of the Republic of Uzbekistan, which is considered one of the important links of the financial sector of the Republic, was selected as the object of analysis. As input data for the analysis, statistical data of the bank provided in its official web-site’s open sources, in particular, data on Eurobonds issued and placed by the bank in London Stock Exchange, are used. At the same time, one of the advantages of the research is the described and disclosed in details and widely used global benchmarking data, published by Standard&Poor's rating agency, as part of the methodology requirement. According to the methodology, credit risk assessment analysis was performed following a strict and detailed calculation algorithm for debt instrument risk assessment. The calculation of credit risk assessment of one debt instrument, especially the Eurobond, according to this procedure followed with the authors’ discussion, conclusion and recommendation is also reflected in the article.

LEVERAGING MANAGEMENT INFORMATION SYSTEMS FOR ENHANCING CREDIT RISK ASSESSMENT IN COMMERCIAL BANKS

This study explores the significant impact of Management Information Systems (MIS) on credit risk assessment in commercial banks, examining how these systems enhance decision-making accuracy, operational efficiency, and proactive risk management. By synthesizing findings from 50 peer-reviewed studies, the research reveals that banks using MIS experience a 20-30% reduction in non-performing loans and a 40-50% increase in loan processing speed due to automation and real-time data analysis. The integration of advanced technologies such as artificial intelligence (AI) and predictive analytics further improves credit risk forecasting accuracy by 15-20%, enabling banks to implement proactive risk mitigation strategies that reduce borrower defaults by up to 25%. However, the study also highlights significant challenges, particularly for smaller banks, which face high implementation costs and difficulties integrating MIS with legacy systems. Despite these challenges, the role of MIS in ensuring regulatory compliance, particularly under Basel III, and reducing overall credit exposure by 15-20% underscores its critical importance in modern credit risk management. The findings suggest that while MIS is essential for maintaining financial stability and competitiveness, scalable and cost-effective solutions are necessary for broader adoption across the banking industry.

Credit risk assessment method driven by asymmetric loss function

Credit risk assessment is significantly hindered by the problem of class imbalance, and cost-sensitive methods represent an effective strategy to address this issue. However, most algorithms tend to approach the imbalance from a class perspective, overlooking the finer details at the sample level. Moreover, such methods are susceptible to interference from noise and outliers. In response to these challenges, this paper proposes an asymmetric cost-sensitive support vector machine (QTSVM) that combines the quadratic type squared error loss function (QTSELF) with support vector machine (SVM). It not only leverages the asymmetry of the loss function by applying varying penalties based on classification errors but also employs different processing measures for samples from different classes. Additionally, it enhances model robustness by imposing a tiny penalty on noise or outliers. The adaptive moment estimation (Adam) algorithm is employed to optimize QTSVM. Extensive experiments and statistical tests profoundly demonstrate the superior performance of QTSVM.

HARVESTING INSIGHTS: THE ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING AGRICULTURAL FINANCE

The intersection of artificial intelligence (AI) and agricultural finance has emerged as a transformative force in enhancing the efficiency and sustainability of farming practices. This review article explores the multifaceted applications of AI technologies in agricultural finance, focusing on credit scoring, risk assessment, and financial decision-making for farmers and agricultural businesses. We examine the integration of machine learning algorithms and big data analytics in providing tailored financial products, enabling precision agriculture, and improving access to credit in under banked communities. By synthesizing current literature and case studies, we identify key challenges and opportunities, such as the need for data security, transparency, and the democratization of AI tools in rural finance. Furthermore, we discuss the potential of AI to address climate change impacts on agriculture and its implications for financing sustainable practices. This article aims to provide stakeholders in the agricultural finance sector—policymakers, financial institutions, and technology developers—with a comprehensive understanding of how AI can drive innovation and improvement in financing agricultural activities, ultimately fostering a resilient and sustainable food system.

Artificial Intelligence Enabled Loan Processing – A Boon for Micro Entrepreneurs

Microbusiness entrepreneurs encounter substantial difficulties in obtaining timely and effective financing to grow their companies in the rapidly changing financial landscape. Traditional loan processes are frequently laborious and time-consuming, which limits small businesses' potential for expansion. This study explores how artificial intelligence (AI) has the potential to revolutionise the loan environment for microbusiness owners. AI streamlines loan processing to provide a quicker, more efficient, and fairer method, empowering entrepreneurs to take opportunities and succeed. The study shows how processing gets quicker, more accurate, and inclusive loan by using secondary data sources. Diverse pieces of literature highlight the benefits of AI for credit risk assessment and transparency. Despite difficulties like implementation expenses, AI provides unmatched advantages including less paperwork, greater access, and enhanced fraud detection. Human monitoring ensures moral AI implementation, finding a balance that changes the financial environment for microentrepreneurs.

Financial Factors and Beyond: A Survey of Credit Risk Assessment for VSBs by Moroccan Banks

This article analyzes the extent to which Moroccan banks adopt a holistic approach to credit risk assessment, incorporating criteria that go beyond simple balance sheets and financial ratios. To this end, Multiple Correspondence Analysis and advanced statistical techniques were employed using R software. A detailed questionnaire comprising 14 questions, was distributed to a sample of Moroccan bankers. The results of this analysis reliably confirm our initial hypotheses. They reveal a growing trend among Moroccan banks towards a more complex and nuanced assessment of credit risk. In addition to traditional financial aspects such as financial statement analysis, banks are increasingly considering non-financial criteria such as collateral requirements, quality of corporate governance, sustainability of business practices, and technological positioning. This diversified approach gives banks a more accurate and comprehensive view of a company's risk profile. It also enables them to make better-informed and more balanced lending decisions, considering the multitude of factors that can influence a company's ability to repay.

Automotive Consumer Loans Risk Assessment Predictive Modeling Using Generative Adversarial Network and Stacked Autoencoder Neural Networks

In this study, we propose a predictive model for automotive consumer loans risk assessment, leveraging Generative Adversarial Networks (GANs) and Stacked Autoencoder Neural Networks (SAEs). We address issues of high dimensionality and sparsity inherent in big data environments and tackle the class imbalance problem using GANs. Feature selection is effectively carried out using SAEs. Experimental results prove the superiority of our approach over traditional neural networks and our model without GANs or SAEs. The proposed model shows significant potential for application in personal credit risk assessment within automotive finance and beyond. Future work is aimed at extending and improving our model and applying it to other domains.

Geometric relative margin machine for heterogeneous distribution and imbalanced classification

This paper introduces the geometric relative margin machine (GRMM) to address class imbalance and heterogeneous data distributions in classification tasks. GRMM innovatively incorporates boundary information as distribution information and implements differentiated misclassification cost strategies for each class, allowing adaptive adjustments to the decision hyperplane and enhancing robustness against outliers. The model employs a block iterative algorithm for optimization. Experimental results on artificial, benchmark, and credit risk assessment datasets demonstrate that GRMM significantly outperforms other benchmark methods in terms of classification accuracy and robustness. This research provides a comprehensive and effective solution to imbalanced classification problems, with potential applications in real-world scenarios, such as credit assessment.

Microcredit Pricing Model for Microfinance Institutions under Basel III Banking Regulations

Purpose. The purpose of this research is to propose a tool for designing a microcredit risk pricing strategy for borrowers of microfinance institutions (MFIs). Design/methodology/approach. Considering the specific characteristics of microcredit borrowers, we first estimate and measure microcredit risk through the default probability, applying a parametric technique such as logistic regression and a non-parametric technique based on an artificial neural network, looking for the model with the highest predictive power. Secondly, based on the Basel III internal ratings-based (IRB) approach, we use the credit risk measurement for each borrower to design a pricing model that sets microcredit interest rates according to default risk. Findings. The paper demonstrates that the probability of default for each borrower is more accurately adjusted using the artificial neural network. Furthermore, our results suggest that, given a profitability target for the MFI, the microcredit interest rate for clients with a lower level of credit risk should be lower than a standard, fixed rate to achieve the profitability target. Practical implications. This tool allows us, on the one hand, to measure and assess credit risk and minimize default losses in MFIs and, secondly, to promote their competitiveness by reducing interest rates, capital requirements, and credit losses, favoring the financial self-sustainability of these institutions. Social implications. Our findings have the potential to make microfinance institutions fairer and more equitable in their lending practices by providing microcredit with risk-adjusted pricing. Furthermore, our findings can contribute to the design of government policies aimed at promoting the financial and social inclusion of vulnerable people. Originality. The personal characteristics of microcredit clients, mainly reputation and moral solvency, are crucial to the default behavior of microfinance borrowers. These factors should have an impact on the pricing of microcredit.

Research on the Application of Alternative Data in Credit Risk Management

With the development of financial technology, traditional credit assessment models have gradually shown their limitations. Especially in assessing borrowers with no credit history or weak credit records. The rise of alternative data provides a new dimension for credit risk prediction, including but not limited to social media behavior, online transaction records, geographic location data, etc. This paper explores the current application status, challenges, and future development trends of alternative data in personal credit risk assessment, and explores the application and effects of various forms of alternative data through different classifications. This paper refers to the relevant literature on alternative data and credit risk management and finds that the application of alternative data can not only supplement part of the information reference to enhance the risk management model but also further provide certain credit credentials for groups that cannot obtain credit services with traditional credit data. It has potential contributions to improving credit risk management and promoting the development of inclusive finance.

Attention-based dynamic multilayer graph neural networks for loan default prediction

Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this paper, we present a model for credit risk assessment leveraging a dynamic multilayer network built from a Graph Neural Network and a Recurrent Neural Network, each layer reflecting a different source of network connection. We test our methodology in a behavioural credit scoring context using a dataset provided by U.S. mortgage financier Freddie Mac, in which different types of connections arise from the geographical location of the borrower and their choice of mortgage provider. The proposed model considers both types of connections and the evolution of these connections over time. We enhance the model by using a custom attention mechanism that weights the different time snapshots according to their importance. After testing multiple configurations, a model with GAT, LSTM, and the attention mechanism provides the best results. Empirical results demonstrate that, when it comes to predicting probability of default for the borrowers, our proposed model brings both better results and novel insights for the analysis of the importance of connections and timestamps, compared to traditional methods.