Bibliography

Credit risk management has become a must in this era due to the increase in the number of businesses defaulting. Building upon the legacy of Kealhofer, McQuown, and Vasicek (KMV), a mathematical model is introduced based on Merton model called KMV-Merton model to predict the credit risk of firms. The KMV-Merton model is commonly used in previous default studies but is said to be lacking in necessary detail. Hence, this study aims to combine the KMV-Merton model with the financial ratios to determine the firms’ credit scores and ratings. Based on the sample data of four firms, the KMV-Merton model is used to estimate the default probabilities. The data is also used to estimate the firms’ liquidity, solvency, indebtedness, return on asset (ROA), and interest coverage. According to the weightages established in this analysis, scores were assigned based on those estimates to calculate the total credit score. The firms were then given a rating based on their respective credit score. The credit ratings are compared to the real credit ratings rated by Malaysian Rating Corporation Berhad (MARC). According to the comparison, three of the four companies have credit scores that are comparable to MARC’s. Two A-rated firms and one D-rated firm have the same ratings. The other receives a C instead of a B. This shows that the credit scoring technique used can grade the low and the high credit risk firms, but not strictly for a firm with a medium level of credit risk. Although research on credit scoring have been done previously, the combination of KMV-Merton model and financial ratios in one credit scoring model based on the calculated weightages gives new branch to the current studies. In practice, this study aids risk managers, bankers, and investors in making wise decisions through a smooth and persuasive process of monitoring firms’ credit risk.

This current research aims to assess the performance of Al Etihad Credit Bureau (AECB) operating in the United Arab Emirates (U.A.E.) in reducing credit risk in the Islamic banking model. The research aims to clarify the effects of credit scores on credit risk management in Islamic banks and the extent of adopting Islamic banks of these ratings when evaluating the borrowers. The study was done based on a primary qualitative research method where six top managers from AECB and nine managers from UAE’s Islamic banks who are involved with credits within the bank were interviewed using a structured interviews approach. It was found that Islamic banks perceive AECB services and products as useful for credit scoring and risk management as a supplement to their internal subjective rules and guides. AECB applies the same rating across banks and financial institutions in the UAE. The study has implications for Islamic banks, AECB, and financial policymakers in the UAE.

Abstract: The assessment of credit risk in the real estate supply chain is an essential part of financial risk management that influences investment decisions, financial stability, and the health of the overall real estate segment. Traditional financial credit risk models have long been used for the assessment of borrower credibility and potential default prediction with historical financial data, credit score, and some various financial ratios, while other methods could complement this approach. Although these conventional approaches have some merit, they frequently fail in capturing real-time market fluctuations, new emerging risks, and complex interdependencies that build creditworthiness. The introduction of artificial intelligence (AI) and machine-learning technologies has planted the seeds of change in the credit risk analysis horizon. AI-based models have given way to advanced analytical techniques that use big data, predictive analytics, and real-time insights to assess risk dynamically and more accurately. This particular paper gives a thorough comparison between the AI-driven and the traditional financial credit risk models alongside their methodologies and performance on prediction, adaptability, and limitation. Credit risk assessment is AI-driven because it utilizes machine learning algorithms to process both structured and unstructured data of large sizes to identify so-called hidden behaviours that conventional models are not able to detect. Real-time market conditions as well as transaction behaviours and macroeconomic indicators are incorporated in AI risk models to improve accuracy and timeliness of risk evaluation. Such models also help financial institutions, lenders, and investors of the real estate sector in decision-making, thus reducing possible financial losses and improving total risk management strategies. On the contrary, traditional models remain relevant since they are regulatory-compliant, transparent, and rely on well-documented financial indicators. They might be slower in reacting to changing market conditions, yet they maintain an aspect of interpretability that is usually absent in AI models. The regulatory authorities and financial institutions are sceptical of the black box of AI models within which lies the accountability, ethical considerations, and potential biases woven into machine-learning algorithms. Data privacy issues and regulatory frameworks concerning AI adoption in financial risk assessment remain reverse challenges that require immediate attention. By systematically comparing AI techniques with the classic credit risk models, the study delineates some of the parameters of distinction, including accuracy, scalability, cost-effectiveness, and applicability in the real world for the real estate sector. Two comparison tables depict the efficiency and application of the two approaches, along with usefulness in contrasting their efficacy. The results, though, suggest that AI-based credit risk models possess superior predictive accuracy, adaptability, and risk mitigation when weighed against traditional methods; yet, those features need to be balanced against regulatory oversight and ethical viewpoints to allow for successful implementation. Ultimately, the aforementioned study shows that innovation and regulatory compliance should be seen as two sides of the same coin for credit risk evaluation. The application of AI for the financial risk evaluation process reconstructively resembles giving an identity to the rehabilitation of the entire real estate supply chain by making decision-making more proactive and also helping in mitigating defaults. However, the transition phase from conventional models to AI-driven models needs a holistic understanding of both these approaches, along with their relative pros and cons. With an active evolution of AI technologies, future works may focus on developing transparent, non-biased, and interpretable AI systems that comply with available industry regulations and ethical principles, so that their adoption in real estate credit risk management can be considered responsible. Keywords: Risk of Credit, Supply Chain in the Real Estate sector, Financial Stability, Conventional Templates of Credit, Models for Credit Powered by AI, Machine Learning, Big Data Analytics, Predictive Analytics, Risk Evaluation Recurrently, Default Risk Mitigation, Decision making in Investments, Credit Scoring, Financial Ratios, Risk Management Strategies, Efficiency of Models, Ethics in AI, Regulatory Compliance.

For the emerging peer-to-peer (P2P) lending markets to survive, they need to employ credit-risk management practices such that an investor base is profitable in the long run. Traditionally, credit-risk management relies on credit scoring that predicts loans’ probability of default. In this paper, we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of loans. To validate our profit scoring models with traditional credit scoring models, we use data from a European P2P lending market, Bondora, and also a random sample of loans from the Lending Club P2P lending market. We compare the out-of-sample accuracy and profitability of the credit and profit scoring models within several classes of statistical and machine learning models including the following: logistic and linear regression, lasso, ridge, elastic net, random forest, and neural networks. We found that our approach outperforms standard credit scoring models for Lending Club and Bondora loans. More specifically, as opposed to credit scoring models, returns across all loans are 24.0% (Bondora) and 15.5% (Lending Club) higher, whereas accuracy is 6.7% (Bondora) and 3.1% (Lending Club) higher for the proposed profit scoring models. Moreover, our results are not driven by manual selection as profit scoring models suggest investing in more loans. Finally, even if we consider data sampling bias, we found that the set of superior models consists almost exclusively of profit scoring models. Thus, our results contribute to the literature by suggesting a paradigm shift in modeling credit-risk in the P2P market to prefer profit as opposed to credit-risk scoring models.

Credit risk is a fundamental aspect of banking and entails the possibility of late or nonexistent payments, that negatively influence the cash flow and liquidity position of a bank. The major reason of failure of financial institutions is credit risk management even after various developments in the financial services sector. Regulations that prohibit connected-party lending and substantial exposure to linked parties can reduce credit risk. Both the underlying worth of a financial institution’s capital and the value of the loan portfolio are impacted by asset classification and the subsequent provisioning against potential losses. The customer profile needs to be clear and the risks related to the main banking products need to be recognized and controlled. Liquidity risk management and loan product maturity profiles are closely related. The target of the paper is understanding the strategic credit risk management with the help of credit scoring considering the mechanism of Credit Information Bureau of India (CIBIL). This study will help in understanding the relevance of credit scoring done by CIBIL to measure the credibility position of the borrower. The credit scoring is done by CIBIL that is India’s first CIC(Credit Information Company). Credit scoring is done by CIBIL based on the customer’s past performances related to repayment, duration of loan, owned accounts, new credit and types of credit taken. Based on the scores giving by governing authorities to the borrowers, their credibility can be assessed thus makes easier loan allocation and risk assessment. The study is an exploratory research thus the data is collected from secondary sources which includes publications of Reserve bank of India, Bank’s annual report, risk management association portal. Keywords: CIBIL, Credit Risk Management, Credit Risk, Credit score

The Surprising Use of Credit Scoring in Small Business Lending by Community Banks and the Attendant Effects on Credit Availability and Risk

Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.

Credit scores determine a person’s life chances. The credit scores we’re all used to, calculated by Equifax, Experian, or TransUnion, take as inputs a person’s payment history, loans, current debt, and similar financial information. But that world is changing. Modern alternative data models for credit scoring can go so far as to include an individual’s educational record, criminal history, shopping behavior, or telephone patterns. Activists, regulators, and scholars have expressed serious concerns about these new credit systems. Do they classify applicants on unfair or arbitrary grounds? Do they perpetuate, or even amplify, bias and pre-existing inequality? Participants in this conversation tend to assume that the new credit scoring models are a departure from a stable historical norm in which lenders made credit decisions solely based on individuals’ loan repayment history and similar financial inputs. But that’s not right. The new models recapitulate a story from the mid-twentieth century, when a new credit scoring industry, relying on newly developed statistical modeling techniques, looked to a broad range of information: How many years had the person been at the same address? Did he have a telephone? What zip code did he live in? For the new method’s proponents, all data – including the applicant’s race and religion -- was fair game. The new credit scoring crystallized a growing sense that computers, and the new computer age, had no room for fully fleshed human beings. Opponents charged that the new technology enabled and replicated bias, seized on spurious correlations, and generated arbitrary results. They saw it as stripping away agency from credit applicants, based on apparently arbitrary criteria, and as reinforcing social and economic hierarchy. More fundamentally, they argued that it was inconsistent with basic human dignity. The technology was short-lived and has largely been forgotten. By the early 1990s, lenders––for economic rather than public-policy reasons––had moved to the model we’re familiar with today, in which credit scores are based solely on applicants’ credit history and related financial information. However, the story of 1970s-era credit scoring is still relevant today, and it provides lessons as we confront today’s use of machine-learning algorithms to categorize people and predict their future behavior.

Credit scoring models play a fundamental role in the risk management practice at most banks. They are used to quantify credit risk at counterparty or transaction level in the different phases of the credit cycle (e.g., application, behavioral, collection models). The credit score empowers users to make quick decisions or even to automate decisions and this is extremely desirable when banks are dealing with large volumes of clients and relatively small margin of profits at individual transaction level (i.e., consumer lending, but increasingly also small business lending). In this article, we analyze the history and new developments related to credit scoring models. We find that with the new Basel Capital Accord, credit scoring models have been remotivated and given unprecedented significance. Banks, in particular, and most financial institutions worldwide, have either recently developed or modified existing internal credit risk models to conform with the new rules and best practices recently updated in the market. Moreover, we analyze the key steps of the credit scoring model's lifecycle (i.e., assessment, implementation, validation), highlighting the main requirement imposed by Basel II. We conclude that banks that are going to implement the most advanced approach to calculate their capital requirements under Basel II will need to increase their attention and consideration of credit scoring models in the near future.

This paper investigates how to improve statistical-based credit scoring of SMEs involved in P2P lending. The methodology discussed in the paper is a factor network-based segmentation for credit score modeling. The approach first constructs a network of SMEs where links emerge from comovement of latent factors, which allows us to segment the heterogeneous population into clusters. We then build a credit score model for each cluster via lasso-type regularization logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 1,5000 SMEs engaged in P2P lending services across Europe. The result reveals that credit risk modeling using our network-based segmentation achieves higher predictive performance than the conventional model.

In financial risk, credit risk management is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. Deep learning is a powerful classification tool which is currently an active research area and successfully solves classification problems in many domains. Deep Learning provides training stability, generalization, and scalability with big data. Deep Learning is quickly becoming the algorithm of choice for the highest predictive accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and improve the accuracy of classifiers. In this study, we constructed a credit scoring model based on deep learning and feature selection to evaluate the applicant’s credit score from the applicant’s input features. Two public datasets, Australia and German credit ones, have been used to test our method. The experimental results of the real world data showed that the proposed method results in a higher prediction rate than a baseline method for some certain datasets and also shows comparable and sometimes better performance than the feature selection methods widely used in credit scoring.

Credit scoring is an essential part of financial risk management, with various techniques and models developed to enhance the accuracy of predicting creditworthiness. A review of the published relevant works in this field concludes that there is no universally accepted ideal model for automatic credit scoring, despite significant advancements in computer technology in recent years. This chapter explores artificial intelligence (AI) and deep learning in credit risk management, analyzing their limited adoption in credit scoring. It presents a practical deep learning model for tabular credit scoring datasets, addressing class imbalance and offering notable performance. Despite superior predictive performance, deep learning models face explainability and regulatory compliance challenges. This chapter explores the application of AI-driven credit risk models in regulated banking, emphasizing how they can improve assessment accuracy while ensuring transparency and regulatory compliance.

One of the most important aspects of risk management for financial institutions is assessing credit risk. Credit scoring models are important tools for evaluating loan applications because they provide a systematic way to assess credit worthiness. While traditional statistical models have been widely used, artificial intelligence (AI) has emerged as a more efficient alternative due to its ability to process large datasets and improve predictive accuracy. The growing reliance on AI-powered models has transformed lending practices, improving decision-making, reducing default risks, and enhancing financial stability. The focus of this research is on exploring AI-based credit scoring models and their impact on financial institutions. Traditional credit scoring methods often lack accuracy and efficiency, leading to increased risks and losses. AI methods like machine learning and deep learning offer a more reliable method, analyzing huge amounts of data and spot patterns that people are not aware of. Gaining insight into how AI affects credit scoring helps with risk management, loan selection, and financial inclusion. Other options for A1, A2, A3, A4, and A5. Income level, credit score, existing debt, and recent credit inquiries are all part of the assessment. The results showed that A3 ranked lowest and A4 ranked best. A1 has the highest value for The Role of AI in Enhancing Credit Scoring Models for Better Lending according to the TOPSIS Method approach.

Neighborhood rough set and SVM based hybrid credit scoring classifier

This paper aims to improve the level of social credit system and the accuracy and efficiency of bank users’ credit scoring by using business intelligence technology based on deep neural network (DNN). Firstly, based on the theory of personal credit evaluation factors, a comprehensive credit evaluation factor system is constructed, taking into account social and economic background, consumption habits, behavior patterns and other factors. Meanwhile, back propagation neural network (BPNN) theory is introduced as the core method of modeling to cope with the nonlinear relationship in the credit scoring task and the demand of large-scale data processing. Secondly, by analyzing the operation process of BPNN in detail, the specific application in credit scoring model is emphasized. Finally, on the basis of theory and operation, this paper implements a credit scoring model for bank users based on BPNN theory. The experimental results show that the model realized in this paper can automatically discover the key attributes and internal rules in the sampled data, and adjust the weight and threshold of the network by modifying the parameters and network structure to meet the expected requirements. The accuracy of the credit score of the predicted sample data reaches 99.5%, and the prediction error is very small, which has a good prediction effect. This paper provides a feasible solution for business intelligence and DNN in the field of credit scoring, and also provides strong empirical support for improving the level of social credit system.