Credit Risk Models Research Articles

Developing Predictive Models for Financial Stability: Integrating Behavioral Analytics into Credit Risk Management

In this article, its author takes an attempt at investigating the use of behavioral analytics to complement credit risk management in the world of finance. Current mainstream credit risk models very much depend on conventional methods that incorporate historical financial information like credit scores and income while ignoring client behavioral contemporaneous patterns essential for modeling their risk levels. By this, the transactional patterns; payment behaviour and digital activities included besides the traditional financial parameters allows the financial institution to make superior models that gives better results on credit worthiness and risk identification earlier. It is crucial for both better risk assessment and for embracing more inclusion within lending networks and financial creativity. Analyzing the informational component of behavioral data as well as its applicability in credit risk modeling and considering the difficulties of implementing it in practice, the article provides an outline of the approaches to telemetry integration.

A simple and consistent credit risk model for Basel II/III, IFRS 9 and stress testing when loan data history is short

A simple and consistent credit risk model for Basel II/III, IFRS 9 and stress testing when loan data history is short

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

Pricing a Defaultable Zero-Coupon Bond under Imperfect Information and Regime Switching

We propose a pricing formula for a defaultable zero-coupon bond with imperfect information under a regime switching model using a structural form of credit risk modelling. This paper provides explicit representations of risky debt under regime switching with a constant interest rate and risky debt under regime switching with a regime switching interest rate. While the value of the firm’s equity is observed continuously, we assume that the total value of the firm is only observed at discrete times, such as the dates of the release of the firm’s annual reports, or quarterly reports. This uncertainty about the true value of the firm results in credit spreads that do not approach zero as the debt approaches maturity, which is a problem with many structural models. The firm’s value is typically decomposed into its equity and debt; however, we consider the asset–to–equity ratio, an accounting ratio used to examine a firm’s financial well-being. The parameters in our model are regime switching, where the regime can be thought of as the state of the economy. A Markov chain with a constant transition rate matrix produces the regime switching.

Corporate credit risk modeling under carbon pricing uncertainty: A Knightian uncertainty approach

This study examines the financial implications of carbon pricing policies within the Knightian uncertainty framework. Employing a dynamic behavioural credit risk model driven by Lévy jump-diffusion, we scrutinise how carbon pricing uncertainty influences default probability and securities value. We explore investors' strategic responses to ambiguity and assess their impact on their investment decisions. Our findings reveal that carbon pricing uncertainty exacerbates the margin of default risk, has a moderating effect on stock value, and makes investors more cautious, thereby altering corporate capital structures. This study contributes to the discourse on carbon credit risk assessment and sustainable finance by addressing policy-driven uncertainties in the financial markets.

P2P credit risk management with KG-GNN: a knowledge graph and graph neural network-based approach

Credit risk management is crucial for the credit loan decision-making process on P2P platforms, essential for mitigating credit defaults. The rapid decline of China’s P2P platform market highlights the urgent need for Internet financial institutions to enhance their credit risk management strategies. Previous studies have applied machine learning to assess credit risk; however, their effectiveness is often hampered by a lack of consideration for semantic information and individual correlation. In response, we propose an innovative approach, KG-GNN, which integrates knowledge graph (KG) and graph neural network (GNN). KG-GNN leverages KG to encapsulate semantic information within complex categorical features and explore potential relationships between borrowers. Utilizing GNN, our framework extracts representation features from the KG to build comprehensive and accurate credit risk models. Our findings indicate that KG-GNN not only can predict credit risk more accurately than conventional machine learning models but also improves the stream model performance by over 20% through KG and GNN-based data augmentation techniques. By integrating KG with GNN, our approach enriches the methodologies for credit risk management and can be adapted to other data mining challenges that require processing complex semantic and relational information, thereby enhancing model learning capabilities.

The risk and return of equity and credit index options

We develop a structural credit risk model, which allows us to price equity/credit indices and their options through the asset dynamics of index constituents. We estimate the model via MLE and find that equity and credit index option prices are well explained out-of-sample. Contrary to recent empirical findings, the two option markets are not inconsistently priced through the lens of our model. Returns on both options, while extreme, do not indicate any evidence of mispricing. Our analysis suggests that jointly addressing the pricing of various instruments requires properly attributing three different sources of systematic risk: asset, variance, and jump risks.

Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models

This research assesses machine learning models' validity, clarity, and equity, compared to classical models and especially logistic regression in credit risk evaluation. In the traditional model of data management, efficiency and the accuracy of information are challenges; an issue of machine learning models is model selection and multicollinearity. The study intends to help financial institutions establish the best strategy for their needs. Furthermore, it delves into the effect of heterogeneous data sources on the credit risk model using machine learning. The research analyses the implications of using machine learning in assessing credit risk. Interestingly, focusing on peer-to-peer lending platforms, the research aims to deal with the need for more attention to combining machine learning and traditional models in the literature. The deductive method is the application of inferential analyses, the Traditional model is logistic regression, and the Machine Learning model is a neural network (CNN model) based on secondary data from the Kaggle peer-to-peer lending dataset. With likely findings expected to comprise prediction of the probability of default and better availability of loans, risk analysis leads to formulated lending decisions managing a financial portfolio.

A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation

The recent developments in the credit and banking industry brought by technology has led to increased competition and the rise of risks and challenges. Credit scoring is one of the core items that keeps this industry competitive and profitable. The creation of credit score models to assess the ability of the loan applicant to repay his or her loan remains an active field of research. Practically, the existing models ignore the factor of inflation in determining the credit score of a loan applicant. Inflation affect the performance of the financing institution negatively because it makes some of the borrowers struggle to repay the loan and so leading to some bad debts that might end up being written off. By integrating the inflation factor to the Extreme gradient boosting algorithm led to improved accuracy of the model. In this paper, a new model that uses the inflation rate of a specific region or country in the regularization term of the extreme gradient boosting model has been developed. The evaluation of the model is by comparison with the other common models using ROC, Accuracy, precision and recall. The developed model emerge the second best in terms of performance but better than the standard extreme gradient boosting model.

Structural credit risk models with stochastic default barriers and jump clustering using Hawkes jump-diffusion processes

Structural credit risk models with stochastic default barriers and jump clustering using Hawkes jump-diffusion processes

Advancing credit risk modelling with Machine Learning: A comprehensive review of the state-of-the-art

Ensuring financial stability necessitates responsible credit granting, so lending institutions maintain sufficient regulatory capital to withstand losses from defaults. Classification methods like Logistic Regression (LR) have long been the standard for estimating default likelihood, but they struggle with increasing operational demands and regulatory standards. Advances in Machine Learning (ML) now allow models to handle larger datasets with greater predictive power. Algorithms such as Decision Trees (DT), Support Vector Machines (SVM), and Neural Networks (NN), though not readily transferable to practical applications, have the potential to enhance the credit risk modelling process. While much of the literature on ML in credit risk focuses on sophisticated, experimental algorithms not yet ready for commercial use, few address the steps and nuances of model development. Our article fills this gap by systematically reviewing the literature on ML in consumer credit risk modelling, highlighting the current state of scientific knowledge. We analyze the frequent steps involved and demonstrate how ML's strengths can be leveraged throughout them. From pre-processing steps like data cleaning, transformation, and reduction, to variable selection and parameter optimization, our findings indicate that ML is becoming increasingly important in financial risk management. However, standardized modelling procedures are needed before complex ML classifiers can be commercially deployed. Meanwhile, although relatively interpretable ML models built around DTs and Random Forests (RF) do showcase increased performance in popular credit risk datasets, it is clear that the potential to benefit from ML spans further, with complex models enabling increased performance by aiding simpler, explainable classification algorithms.

An Alternative Approach to Parliamentary Oversight of Sovereign Guarantees in South Africa

The research paper constructs and compares two structural risk models. A South African sovereign risk model and an ESKOM credit risk model. The interaction between these models explains the statistically significant causal relationship between corporate governance practices at the level of a public entity and government finance conditions between 2004 and 2019. It shows that a more effective approach to parliamentary oversight of government guarantees in South Africa can deliver better sovereign credit rating outcomes in post-COVID-19 South Africa. It will require proactive innovative measures in the application of government guarantees issued to SOEs aimed at improving institutional operational performance within a government-wide risk management framework. The paper concludes that economic transformation, with restitutive intent, is advanced with the adoption of a dynamic and evidenced-informed approach in addressing the complex interplay of prevailing socio-economic, government finance, and political environmental conditions in South Africa.

Credit risk evaluation on technological SMEs in China

ABSTRACT China’s reform and opening-up policies have prioritized technological advancement, with technological SMEs driving employment and economic growth. Despite their significance, these SMEs face substantial financing and operational risks due to inadequate credit measurement tools. This study reviews the historical financing challenges of technological SMEs since the 1980s, summarizes their current credit risk status, and compares four modern credit risk models: Credit Metrics, Credit Risk+, Credit Portfolio View, and KMV. We propose a pioneering KMV Strategy for real-time risk analysis, contributing to accurate credit metrics for these SMEs. Finally, we suggest policies for managing their credit risks through prevention, control, and governance.

Macroeconomic environment and the future performance of loans: Evidence from three peer-to-peer platforms

The literature on peer-to-peer loan market performance focuses predominantly on the microlevel variables connected to individual borrower or loan characteristics. We hypothesize that the economic environment, reflected by macroeconomic variables at the time of loan origination, should play a role in explaining loans’ future performance. Our dataset comprises two US-based platforms and one UK-based platform, which provides a rich field to verify our hypothesis with numerous loan-related variables. Specifically, we consider Lending Club with 1,169,976 individual loans and 128 variables, Prosper with 386,685 loans and 142 variables, and Zopa with 440,493 loans and 192 variables. Using linear and threshold regressions, we observe three main findings: (i) accounting for the macroeconomic environment systematically improves our understanding of the variation in the future performance of individual loans; (ii) demand-side variables, particularly the unemployment rate and industrial production, have stronger effects as supply-side and economic uncertainty variables; and (iii) the importance (effect size) of the macroeconomic environment is at least at the level of that of calendar and geographic variables but much smaller than the importance of loan or borrower characteristics. These results suggest that the economic environment might be useful in individual loan-level credit risk models.

Comparative Analysis of Machine Learning Algorithms for Consumer Credit Risk Assessment

In the rapidly evolving landscape of financial technology, machine learning algorithms are increasingly supplanting traditional methodologies for evaluating consumer credit risk. This study leverages a comprehensive dataset comprising 10,000 credit accounts to conduct a comparative analysis of four prevalent machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Machine (GBM). The results distinctly favor GBM, which achieves an AUC of 0.87, closely followed by Random Forest with an AUC of 0.85. In stark contrast, Logistic Regression and Decision Tree recorded lower AUCs of 0.78 and 0.72, respectively. GBM and Random Forest significantly outperform in classification accuracy, attaining 92% and 90%, respectively, far exceeding the 86% by Logistic Regression and 80% by Decision Tree. Notably, GBM exhibited 95% specificity and 90% sensitivity, efficiently identifying high-risk accounts while minimizing false positives among low-risk categories. Furthermore, the study delves into the handling of imbalanced datasets, interpretability, and computational demands of each algorithm, offering quantifiable insights that inform future directions for optimizing credit risk models, particularly in enhancing transparency and scalability.

Profit-sensitive machine learning classification with explanations in credit risk: The case of small businesses in peer-to-peer lending

We propose a comprehensive profit-sensitive approach for credit risk modeling in P2P lending for small businesses, one of the most financially complex segments. We go beyond traditional and cost-sensitive approaches by including the financial costs and incomes through profits and introducing the profit information at three points of the modeling process: the estimation of the learning function of the classification algorithm (XGBoost in our case), the hyperparameter optimization, and the decision function. The profit-sensitive approaches achieve a higher level of profitability than the profit-insensitive approach in the small business case analyzed by granting mostly lower-risk, lower-amount loans. Explainability tools help us to discover the key features of such loans. Our proposal can be extended to other loan markets or other classification problems as long as the cells of the misclassification matrix have an economic value.

Credit Card Fraud Detection Based on Machine Learning Classification Algorithm

Credit risk analysis is a critical process in the financial industry, as it helps lenders assess the likelihood of borrowers defaulting on their loans. With the advent of machine learning algorithms, there has been a growing interest in leveraging these techniques for more accurate and efficient credit risk prediction. Traditional credit risk models often rely on manual processes and limited data sources, resulting in potential biases and inaccuracies. Additionally, the rapid growth of credit card usage and the increasing complexity of financial transactions have made it challenging to accurately assess credit risk using conventional methods. This review paper aims to provide a comprehensive overview of machine learning algorithms used for credit risk prediction in the context of credit card lending. It explores classification techniques and their applications in credit risk analysis. The paper also discusses the challenges and limitations associated with these algorithms, including data quality, overfitting, and interpretability.

Pricing CDS index tranches under thinning-dependence structure with regime switching

In this paper, we consider a portfolio credit risk model with the thinning-dependence structure under the case with regime switching and the one with no regime switching. We obtain the explicit expressions for the copulas of the default times. Based on the quotation convention of CDS index tranches, we derive some closed-form expressions for the pricing formulas for the CDS index tranche via the copulas and the marginal distribution functions of the default times. As an empirical example, we consider the pricing of the CDX NA IG Series 25. We demonstrate how the model parameter can be calibrated based on market data and how the numerical results can be calculated via the proposed pricing formulas. Compared to the empirical results under the pricing model with some commonly used copulas, such as the Clayton copula, the Gumbel copula, and the Frank copula, we find that the proposed pricing model with regime switching can provide a valuable reference for the market quotes for the CDX NA IG Series 25.

A comparative analysis of consumer credit risk models in Peer-to-Peer Lending

PurposeThe purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.Design/methodology/approachThe author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.FindingsThe results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.Originality/valueThe main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.