Credit Risk Assessment Research Articles

Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the data. This study uses discrete-time survival models with embedded neural networks as estimators of time to default. This provides flexibility to express nonlinearities and interactions between variables and hence allows for models with better overall model fit. Additionally, the neural networks are used to estimate age–period–cohort (APC) models so that default risk can be decomposed into time components for loan age (maturity), origination (vintage), and environment (e.g., economic, operational, and social effects). These can be built as general models or as local APC models for specific customer segments. The local APC models reveal special conditions for different customer groups. The corresponding APC identification problem is solved by a combination of regularization and fitting the decomposed environment time risk component to macroeconomic data since the environmental risk is expected to have a strong relationship with macroeconomic conditions. Our approach is shown to be effective when tested on a large publicly available US mortgage dataset. This novel framework can be adapted by practitioners in the financial industry to improve modeling, estimation, and assessment of credit risk.

Data analysis with different variables and credit risk assessment

Nowadays, credit payment is a very common way to pay, such as credit cards, loans, many people can use their credit as a guarantee to borrow money from the bank, however some people will default. So we have to predict whether the borrower will pay on time, it is known as credit risk assessment. In this paper, we analyze a data set on credit risk to predict whether individuals will be late on their payments, helping financial firms improve their earnings and reduce their losses. We not only made predictions on the data, but also analyzed the relationship between the variables that affect the overdue probability to find some specific associations. Specifically, we performed ANOVA analysis and found that married people borrowed significantly more than other groups, and the delinquency rate of people with higher education was lower, and the delinquency rate of married people was higher than that of unmarried people. In addition, we conducted a binary logistic regression and found that gender had no significant impact on the prediction results, but an individuals amount of bill statement, amount of previous payment, past repayment situation and Amount of the given credit had an impact on the prediction results. Other variables, such as marital status and education, can also impact the predicted results. Our research puts forward more factors affecting credit risk and also different angles that can be used to analyzes individual credit risk. This has a guiding role for financial firms like banks and other companies in the financial industry, providing more ways to help them analyze the credit risk of borrowers.

Fair-CMNB: Advancing Fairness-Aware Stream Learning with Naïve Bayes and Multi-Objective Optimization

Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, etc. This calls for handling massive amounts of incoming information with minimal response delay while ensuring fair and high-quality decisions. Although deep learning has achieved success in various domains, its computational complexity may hinder real-time processing, making traditional algorithms more suitable. In this context, we propose a novel adaptation of Naïve Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance through multi-objective optimization (MOO). Class imbalance is an inherent problem in discrimination-aware learning paradigms. To deal with class imbalance, we propose a dynamic instance weighting module that gives more importance to new instances and less importance to obsolete instances based on their membership in a minority or majority class. We have conducted experiments on a range of streaming and static datasets and concluded that our proposed methodology outperforms existing state-of-the-art (SoTA) fairness-aware methods in terms of both discrimination score and balanced accuracy.

Understanding the Psychological and Financial Correlates for Consumer Credit Use

This study investigated the behavioural and cognitive predictors of consumer credit usage to develop a behavioural credit risk assessment procedure for a factoring company. Participants completed surveys measuring personality traits, self-esteem, material and monetary values, compulsive and impulsive buying tendencies, self-control, and impulsiveness. Financial surveys also assessed financial literacy and knowledge of financial concepts. The results indicated that extraversion, conscientiousness, emotional stability, and experiential self-control were significant predictors of consumer credit usage. These findings suggest that a finance company can use these personality traits and financial characteristics to develop a more accurate and effective credit risk assessment procedure, such as psychometric tests.

Retracted: Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Retracted: Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Analyzing the Impact of Carbon Risk on Firms’ Creditworthiness in the Context of Rising Interest Rates

Carbon risk, a type of climate risk, is expected to have a crucial impact, especially on high-carbon-emitting, “polluting” firms as opposed to less carbon-intensive, “clean” ones. With a rising number of actions and policies being continuously proposed to mitigate these concerns and an increasing number of investors demanding more climate adaptation initiatives, this transition risk will certainly need to be incorporated into a firm’s credit risk assessment. In this paper, we explore the impact of the carbon risk factor, constructed as the daily median difference in default protection between polluting and clean European firms, on firm creditworthiness using quantile regressions on the tail distribution of credit default swap spreads for different maturities between 2020 and 2023. In particular, the recent European interest rate hikes lead to unexpected conclusions about when the carbon risk factor affects firm creditworthiness and how rapidly the net-zero economy transition must occur. Contrary to the previous literature, we find that investors are expecting the transition to occur in the medium-to-long term.

Development of a credit risk evaluation system using multilayer neural networks

This paper deals with the development of a credit risk assessment system using multilayer neural networks. The main objective of this work is to provide a decision support tool for risk assessment, considering relevant variables in the process. To achieve this objective, the backpropagation algorithm and the Adam optimizer were used to train the model. In terms of materials and methods, a training and validation data set including relevant financial information of credit applicants was used. A multilayer neural network was implemented that made predictions and calculated the loss using the categorical cross-entropy function. The results obtained during the development of the system showed a favorable performance and a satisfactory level of accuracy in identifying and classifying different levels of credit risk. However, it is emphasized that the system does not provide absolute results; human intervention is recommended as a last resort for decision making

CREDIT RISK MANAGEMENT OF “HALYK BANK” JSC: PROBLEMS AND SOLUTIONS

The purpose of the research work is to reveal the essence of the credit risk system, study the causes of its occurrence, and conduct a detailed analysis of the loan portfolio and credit risk management methods on the example of the commercial bank "Halyk Bank of the Republic of Kazakhstan" JSC. The authors analyze the loan portfolio of the bank using CAMELS, Z - score methods and build a regression analysis using R-studio, identify the main problems in credit risk management in a commercial bank and propose some solutions. In this article such problems in credit risk management as incomplete credit risk assessment, weak credit risk monitoring, insufficient collateral assessment, improper risk diversification, insufficient credit risk management policy are identified, and several solutions and methods for their improvement are proposed. The informational approach to solving the set goals is formed based on existing scientific research, published periodicals, scientific articles and financial statements of the bank, and its statistical data is used as material for writing a research paper.

Research on the Application of Blockchain in SMEs Credit Risk

The credit of an enterprise is related to its own development. This paper mainly discusses the relationship between the credit risk of small and medium enterprises (SMEs) and the application degree of blockchain. 64 listed companies with block chain technology as the core theme are selected to analyze their comprehensive financial data. Factor analysis is used to quantitatively evaluate the application degree of blockchain in SMEs, and then the Logistic model is used to evaluate the credit risk of SMEs. Finally, combining the application degree of blockchain in small and medium-sized enterprises and the credit risk assessment of these two groups of data. It confirms the conclusion that the higher the degree of blockchain application, the closer the supply chain finance relationship, and the better the credit status.

Influencing Corporate Creditworthiness: Case Study in the Egyptian Banking Sector

Granting loans to corporate clients is the main source of income for banks. However, those loans are associated with a certain level of credit risk that is the inability of clients to meet their obligations toward banks. The occurrence of credit risk can negatively affect banks' profitability and business continuity. Considering the fast-evolving environment, the competition between banks, and the asymmetry of information, mitigating credit risk becomes a main duty of banks. The aim of this qualitative study was to determine the financial and non-financial factors that have a significant impact on corporate clients' creditworthiness. The aim is to help credit risk assessors to enhance the quality of the credit risk assessment and to make timely and accurate credit decisions. The study was focused on the Egyptian banking sector and distinguished between large companies and small and medium enterprises. The study revealed a list of financial and non-financial factors that have a significant impact on the creditworthiness of each category of companies as judged by credit risk assessors. The study also found that there are similarities and differences between both sizes of companies in terms of the factors that affect their creditworthiness.

HFTCRNet: Hierarchical Fusion Transformer for Interbank Credit Rating and Risk Assessment.

As a prominent application of deep neural networks in financial literature, bank credit ratings play a pivotal role in safeguarding global economic stability and preventing crises. In the contemporary financial system, interconnectivity among banks has reached unprecedented levels. However, many existing credit risk models continue to assess each bank independently, resulting in inevitable suboptimal performance. Thus, developing advanced neural networks to model intricate temporal dynamics and interconnected relationships in the banking system is essential for an effective credit rating and risk assessment learning system. To this end, we propose a novel hierarchical fusion transformer for interbank credit rating and risk assessment (HFTCRNet), which includes the long-term temporal transformer (LT 3 ) module, short-term cross-graph transformer (STCGT) module, attentive risk contagion transformer (ARCT) module, and hierarchical fusion transformer (HFT) module to capture the long-term growth trajectories of banks, the short-term interbank network variance, the potential propagation of risks within interbank network, and integrate these information hierarchically. We further develop an interbank credit rating dataset, encompassing quarterly financial data, interbank lending networks, and key indicators such as credit ratings and systemic risk (SRISK) for 4548 banks from 2016Q1 to 2023Q1. Notably, we also adapt the minimum density algorithm to stabilize the interbank loan network over time, aiding in the analysis of long-term and short-term network effects. Our learning system uses semi-supervised learning to handle labels of varying sparsity, integrating credit ratings and SRISK for a comprehensive assessment of individual bank creditworthiness and systemic interbank risk. Extensive experimental results on our interbank dataset show that HFTCRNet not only outperforms all the baselines in terms of credit rating accuracy but also can evaluate the systemic risk within the interbank network. Code will be available at: https://github.com/AI4Risk/HFTCRNet.

Research on the Financial Credit Risk Management Model of Real Estate Supply Chain Based on GA-SVM Algorithm: A Comprehensive Evaluation of AI Model and Traditional Model

By combining machine learning AI model with traditional credit risk measurement model, this paper aims to more accurately measure the credit default risk of real estate enterprises. Through professional credit management knowledge, the integration of these two models can fill the gaps of traditional models outside of financial data, and use the research experience of traditional models in the financial field to provide guidance for machine learning. This paper focuses on the key issues of capital intensity, exploration of Supply Chain Finance Growth and Credit Risk Control within the Real Estate Sector. By combining qualitative and quantitative methods. Through literature research and empirical analysis, this paper develops a supply chain finance credit risk assessment index system tailored for small and medium-sized enterprises operating in China's real estate sector. The index system comprises 23 risk assessment indicators, and comprehensively considers many factors such as corporate financial status and market environment. Then, a credit risk assessment model for small and medium-sized real estate enterprises is developed from a supply chain finance perspective, utilizing genetic algorithm optimization for Support Vector Machine (GA-SVM)., and the advantages of this model compared with the traditional model in accuracy and generalization ability are verified through comparative analysis of experiments. The research results can help reduce the information asymmetry in the real estate industry, enhance the credit rating evaluation process for small and medium-sized enterprises, and furnish references to aid commercial banks in credit and credit risk management within the real estate supply chain finance sector.which has important theoretical and practical value.

Credit Risk Assessment of Listed Companies Based on Vector Angle Cosine

A study is conducted on the credit risk assessment model of listed companies in China. Firstly, factor analysis is used to construct an indicator system from five dimensions: debt paying ability, development ability, operating ability, profitability, and cash flow. Then, the cosine method of vector angle is used to replace the subjective expert scoring method and assign weights to the indicators. Finally, based on the scoring model, the scores of 25 listed companies are calculated and their credit risk is evaluated.

Optimizing bank credit risk assessment models using big data analytics

Abstract With the strong promotion of financial globalization and marketization and the rapid development of financial industry innovation, credit risk management is becoming an important work that banks need to pay more and more attention to. This paper focuses on the construction of the bank credit risk model to carry out research. First of all, according to the credit characteristics selection method, the optimal indicators are combined with the indicators for the correlation test of variables. On this basis, the sample data is calculated using the up-sampling method of SMOTE to complete the algorithm and sampling. After obtaining the data, data preprocessing is carried out using the MinMaxScaler scaler method, and the processed data is inputted into the improved momentum BP neural network to complete the credit risk assessment. Profiling with risk feature data, it is obtained that each feature is not a separate individual from the other. They are interdependent and connected and have obvious correlations. For example, there are 47 features in the dataset with sample missing ratios greater than 0.97, which is too high and indicates an invalid feature. The results of the study show that SMOTE’s up-sampling method and Momentum BP algorithm can quickly utilize big data to provide a more accurate decision basis for bank credit risk assessment.

ESG Risks and Their Impact on the Creditworthiness of Companies

The integration of environmental, social and governance (ESG) criteria into credit risk assessment has gained significant attention recently. Therefore, it is necessary to understand, select and assess the risks of these ESG criteria and evaluate how they can impact company’s creditworthiness. The main objective of this study is to give a thorough overview of the types of ESG risks, their interrelation with credit risk of the company and methodological approaches they can use, along with opportunities for future advancements. The analysis shows that climate change and transition to low-carbon economy provoke two main types of ESG risks – physical risks and transition risks. Both types of ESG risks could affect company’s creditworthiness through its cash flows capital and collateral. In order to assess climate-related types of risks methodologies based on forward-looking scenarios are needed with the employment of data that has not been observed in the past. These results are a basis for further development of methodological architecture of ESG-credit risks.

Credit Risk Evaluation of Real Estate Industry Based on GA-GARCH-KMV Model

Credit risk assessment in the real estate industry has garnered significant attention from government regulators, investors, and business scholars. However, the evaluation of credit risk in this sector poses numerous challenges, primarily due to the intricate interplay of economic cycles and political landscapes. In this study, we propose a novel method that leverages the GARCH(1,1) model in conjunction with the Genetic Algorithm (GA) to enhance the KMV model's performance. By refining the default point and equity value volatility in the KMV model, our approach offers more accurate credit risk evaluations in the real estate industry. Empirical results demonstrate the superior accuracy of our improved KMV model, providing valuable insights for early credit risk warning in the real estate sector.

Artificial Intelligence and Machine Learning in Credit Risk Assessment: Enhancing Accuracy and Ensuring Fairness

Artificial Intelligence and Machine Learning in Credit Risk Assessment: Enhancing Accuracy and Ensuring Fairness

Feature Enhanced Ensemble Modeling With Voting Optimization for Credit Risk Assessment

Feature Enhanced Ensemble Modeling With Voting Optimization for Credit Risk Assessment

Investigation of the Application of Machine Learning Algorithms in Credit Risk Assessment of Medium and Micro Enterprises

Investigation of the Application of Machine Learning Algorithms in Credit Risk Assessment of Medium and Micro Enterprises

The Use of Machine Learning for Credit Risk Assessment

The Use of Machine Learning for Credit Risk Assessment