Credit Risk Research Articles

Will fighting climate change affect commercial banks? A carbon tax policy simulation

Policies implemented to address climate change, especially carbon tax policies, have profound impacts on risk management and credit losses in the financial system. Existing research suggests that climate risks may lead to high‑carbon-emission companies facing asset stranding and credit downgrades; however, their specific effects on credit losses in the banking system have not been thoroughly elucidated. This study uses data from 21 listed Chinese commercial banks and 3163 firms for 2020, applying climate stress-testing to construct 16 carbon tax scenarios. These scenarios simulate the effects of a carbon tax on firms' asset values and financial stability and estimate the potential transmission of these effects to credit losses in Chinese commercial banks. Our findings reveal that introducing a carbon tax significantly increases bank credit losses, with credit losses escalating exponentially as tax rates increase. State-owned commercial banks experience the highest losses, followed by joint-stock and city banks. The primary contributors to these credit losses are high‑carbon industries such as electricity, manufacturing, and transportation. These findings underscore how carbon tax policies can decrease firms' asset values and thereby increase banks' credit risks, providing essential insights for policymakers designing climate policies.

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.

Assessing the value and risk of renewable PPAs

Renewable Energy Power Purchase Agreements (RE PPAs) are considered to be a key tool in order to foster RE deployment, as they allow for the reduction of uncertainty for all parties as well as facilitating access to the long term finance required for such projects. Nevertheless, RE PPA adoption is hampered by a number of barriers, including the high level of guarantees demanded from offtakers, a problem which is related to the shortcomings of existing assessment methodologies, in particular, determining the credit risk of the PPA itself. In this work, we propose an RE PPA assessment model focused on the main drivers of value and risk for the offtaker which are cost and volatility reductions, compared to the electricity market. By identifying and valuing the options for the offtaker embedded in the PPA, it is possible to determine the default probability at any given time and the expected loss for the producer, thus allowing for the estimation of the amount of guarantees needed to hedge the credit risk.

From risk to reward: Unveiling the multidimensional impact of financial risks on the performance of Ghanaian banks

The Ghanaian banking sector, grappling with a spectrum of financial risks, presents a compelling case study for understanding the dynamics of risk and profitability in emerging markets. This study seeks to fortify the financial performance of Ghanaian banks through an innovative application of benchmark regression analysis, focusing on critical financial risk and performance metrics. Employing an explanatory research methodology, we harnessed a panel regression model to scrutinize secondary data extracted from the annual income statements of 23 banks, spanning nearly two decades from 2006 to 2023. Our analytical arsenal encompassed the fixed effects model, the Generalized Method of Moments (GMM) within a fixed-effects framework, and Pooled Ordinary Least Squares (POLS) to ensure methodological rigor and robustness. Our findings illuminate the relationship between financial risks and Return on Equity (ROE). Specifically, credit risk was found to exert a significant positive influence on ROE, suggesting that judicious credit extension can be a conduit for profitability. In contrast, liquidity risk was identified as a determinant negatively impacting ROE, underscoring the imperative for effective liquidity management to safeguard banks' long-term solvency. Additionally, market risk was observed to have a positive association with ROE, indicating that strategic exposure to market volatility can potentially enhance financial performance. The implications of this study are manifold, offering actionable insights for policymakers and stakeholders in the banking sector. It advocates for a recalibrated approach to risk management, where banks proactively embrace credit risk while vigilantly addressing liquidity challenges. The study further posits that by forging strategic interconnections, banks can augment their risk management capabilities, thereby bolstering their financial performance and ensuring resilience in the face of market volatility.

CEO neuroticism and corporate cash holdings: Evidence from CEOs’ tweets

We examine the effects of CEO neuroticism on corporate policies for cash holdings. After hand collecting tweets by CEOs to measure their neuroticism, we find that firms with relatively neurotic CEOs hold more cash than other firms. This positive effect is more pronounced when the firm has a higher precautionary motive to hold cash. The cash held by firms with neurotic CEOs leads to higher firm values and lower credit risks. Overall, neurotic CEOs maintain more conservative corporate policies on holding cash, resulting in a lower cost of financial distress and an improvement in the value of firms.

Private and state-owned banks in times of high instability. Mexico 1977–1990

In 1982, immediately after the Latin American debt crisis began, the Mexican government expropriated the country’s private banks. The debt crisis emerged in the context of macroeconomic instability, which lasted throughout the 1980s. This paper explains how banks navigated macroeconomic instability and demonstrates that even as state-owned intermediaries, they can maintain profitability and soundness. Mexican banks undertook risks that jeopardised their stability before the crisis, but after they became state-owned enterprises, they remained profitable and faced low credit risk. This evidence contrasts with the traditional view that government-owned banks tend to be less profitable. However, this profitability originated mostly from government sources. The government’s strategy was motivated by the need to use the banks to finance its deficit and by its political interest in compensating former shareholders for the expropriation. Consequently, financial development stagnated.

The Relationship between Board Characteristics and Corporate Credit Ratings—Evidence Based on the Chinese Market

Credit ratings serve as a critical mechanism for investors to assess corporate credit risk and mitigate investment risk, while a well-managed board is central to addressing principal-agent problems and thereby effectively reducing a firm's default risk. However, research on the impact of board characteristics on credit ratings rarely focuses on individual characteristics. This study focuses on board composition, board member quality, board motivation, and board governance to provide a more comprehensive description of the impact of board characteristics on corporate credit ratings. Using data on Chinese A-share listed companies from 2010 to 2020, the findings are as follows.(1)Board composition, quality, motivation and leadership have a significant impact on corporate credit ratings. (2)Board characteristics affect corporate credit ratings by influencing internal control, surplus management, and accounting disclosure quality. (3)Under the socialist economy with Chinese characteristics, the influence of board characteristics on corporate credit ratings is significantly higher in regions with higher levels of marketization. Overall, our findings provide new insights into the effect of board characteristics on credit ratings.

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.

IMPACT OF CREDIT RISK MANAGEMENT ON PERFORMANCE OF MICROFINANCE BANKS IN AKURE, ONDO STATE

The study examined the impact of credit risk management on performance of microfinance banks in Akure, Ondo State. The research design used in the study was survey research design where questionnaire was designed and administered to the staff of Moniepoint Microfinance Bank and LAPO microfinance bank in Akure, Ondo State, Nigeria. The sample size for the study was fifty. The study used chi-square as the statistical tool to analysis the hypotheses formulated. The result of the study showed that credit risk management impacts performance of microfinance banks in Akure, Ondo State. Specifically, credit risk appraisal, credit risk monitoring and credit risk mitigation impacts performance of microfinance banks in Akure, Ondo State. Consequently, it is recommended that an effective monitoring system should be implemented in microfinance institutions (MFIs) so that information regarding customers, credit risk exposures, collateral, and borrowers, is up to date.

PENGARUH VARIABEL PENERIMAAN DANA ZISWAF, BOPO, DAN LABA/RUGI NONOPERASIONAL TERHADAP NPF GROSS DENGAN FIRM SIZE SEBAGAI VARIABEL MODERASI PERIODE 2018.Q1–2023.Q3

Abstract :This study aims to determine and examine the effect of receiving ZISWAF, BOPO, and non-operating profit / loss on gross NPF with firm size as a moderation variable for the period 2018.Q1 – 2023.Q3. The sample used in this study is companies in the financial sector listed on the Indonesia Stock Exchange (IDX) in 2018-2023. The sampling technique used was purposive sampling, with a total sample of 5 banks for 6 years. The data analysis techniques used are Panel Data Regression Analysis and Moderated Regression Analysis (MRA) with the Eviews 12 application. The results showed that ziswa funds, BOPO and non-operating profit / loss showed a significant influence on the value of the company. This means that if the value of ziswa funds generated is higher, the funds can be used to strengthen the capital of Islamic financial institutions so that the risk of bad loans will be reduced. High BOPO levels have an impact on Gross NPF levels. Large operational costs can reduce the company's profitability. Good non-operating profit can help reduce NPF Gross by improving the company's financial stability and repayment ability, while large non-operating losses can increase credit risk and contribute to an increase in NPF Gross if not managed properly. Firm size shows a significant positive influence on company value. This means that if the resulting capital structure is high, it will cause a decrease in the value of the company, and vice versa. Then, in the interaction of moderation variables, probability can moderate ziswa funds, BOPO, non-operating profit / loss on NPF against the value of financial sector companies in the 2018Q1-2023Q3 period. Keywords: Islamic Banking,ZISWAF Fund, BOPO,profit and loss, NPF Gross, Firm Size.

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.

Effective Machine Learning Techniques for Dealing with Poor Credit Data

Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. This task uses seven performance measures, including the F1 Score (recall, accuracy, and precision), ROC-AUC, and HL and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of the single machine learning classifiers for predicting credit risk is proposed. The results show that when GANs imputation is incorporated, the decision tree is the best-performing classifier with an accuracy rate of 93.01%, followed by random forest (92.92%), gradient boosting (92.33%), support vector machine (90.83%), logistic regression (90.76%), and naïve Bayes (89.29%), respectively. The classifier is the worst-performing method with a k-NN (88.68%) accuracy rate. Subsequently, when GANs are optimised, the accuracy rate of the naïve Bayes classifier improves significantly to (90%) accuracy rate. Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. In summary, most individual classifiers are more robust to missing data when GANs are used as an imputation technique. The differences in performance of all seven machine learning algorithms are significant at the 95% level.

An Empirical Investigation on the Determinants of Interest Rate Spread of Commercial Banks in Bangladesh

This paper aims to investigate the determinants or factors affecting the interest rate spread of private commercial banks in Bangladesh from 2013 to 2022. For the purpose of the study, the interest rate spread (IRS) of banks has been considered as a dependent variable while bank-specific factors and macroeconomic factors have been considered independent variables. Bank-specific factors are credit risk, bank size, operating cost ratio, liquidity risk, net interest income as a ratio of total income, capital adequacy ratio, and loan to deposit ratio while macroeconomic factors are Inflation and GDP. The Pooled Ordinary Least Square method (OLS), the Fixed Effect method (FE), the Random Effect method (RE), and the Generalized Least Square method (GLS) have been used to investigate the impact of the factors on interest rate spread. The results exhibit that bank-specific factors such as net interest income as a ratio of total income, and capital adequacy ratio are found to be statistically significant and positively impact the interest rate spread. In contrast, the results also exhibit that bank-specific factors such as bank size, operating expense ratio, and loan to deposit ratio are statistically significant and negatively impact the interest rate spread. Again, the results determine that the macroeconomic factor which is inflation found to be statistically significant and positively impacts the interest rate spread. The study’s findings will assist the banks’ regulatory body in formulating and developing strategies to maintain a satisfactory level of interest rate spread.

Risk Typologies and Their Mitigation in Banking: A Study of Azerbaijani Banks

Abstract This paper explores the typologies of risks in the banking sector and examines the strategies employed to mitigate them, with a focus on Azerbaijani banks. The study identifies key risk categories, including credit risk, market risk, liquidity risk, and operational risk, each of which plays a critical role in the financial health and profitability of banks. The research highlights that credit risk, driven by loan defaults, remains the most significant concern for Azerbaijani banks, while market risks, particularly from currency fluctuations, also pose major challenges. Effective risk mitigation strategies, including collateralization and diversified lending practices, are essential to maintaining stability in this emerging market. Additionally, the study emphasizes the importance of integrating modern risk management technologies and adhering to international regulatory standards. Azerbaijani banks that adopt advanced monitoring systems and proactive risk management frameworks demonstrate improved financial performance and resilience in the face of economic volatility. The findings underscore that well-implemented risk management practices are crucial for enhancing profitability and ensuring long-term sustainability in the banking sector.

Analysis of the Implementation of Risk Management to Minimize the Risk of Bad Credit at the Nias Rural Development Savings and Loans Cooperative (KSP3) Gunungsitoli Branch

This study aims to determine the application of risk management carried out by the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch in minimizing the risk of bad loans. In this study, the type of research used is a descriptive type with a qualitative approach. Data collection techniques are used through observation, interviews and documentation. To obtain information, the researcher conducted direct interviews with 6 informants, namely branch heads, 2 employees and 3 members of the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch. The data analysis techniques used in qualitative research include transcripts of interview results, data reduction, data presentation and conclusion drawn. The results of the study show that the implementation of risk management carried out by the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch is carried out through a series of stages, namely risk identification, risk measurement, risk monitoring and risk control. The implementation of risk management to minimize the risk of bad loans has been effectively implemented by KSP3 Nias Gunungsitoli Branch as evidenced by the NPL (Non-Performing Loan) rate from 2020-2022 which is still below 5%.

Inaccurate Borrower Information and Credit Risk: Evidence from Marketplace Loans

Abstract Inaccurate borrower-provided information in marketplace loans is informative about credit risk. An inaccuracy index constructed from the consistency of loan amount with outstanding credit balance, roundness of reported income, and roundness of chosen loan amount predicts the likelihood of default, and the additional default risk is not compensated by higher interest. Inaccurate information is more prevalent in areas with lower social capital and weaker social norms. It is also lower among borrowers whose professions are considered less honest, and among borrowers with higher income uncertainty. These results suggest that inaccuracy is driven by both deliberate misreporting and genuine uncertainty. (JEL D12, D91, G23, G41) Received: 6 April 2023; Editorial decision: 31 March 2024 Editor: Isil Erel Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Co‐CEO Structure and Earnings Smoothing Incentives

AbstractThis paper investigates the effects of a co‐CEO structure on earnings smoothing and its implications for the informativeness of earnings. While the consequences of a co‐CEO structure have been relatively underexplored in prior research, existing studies suggest potential benefits, such as higher valuations and stronger internal governance. This paper delves into the specific consequences of a co‐CEO structure, focusing on earnings smoothing practices. Using data from Korean firms, where co‐CEO structures are prevalent, we find that firms with a co‐CEO structure exhibit smoother earnings compared to those with a sole‐CEO structure. However, the informativeness of these smooth earnings is reduced under a co‐CEO structure, suggesting that co‐CEOs engage in earnings smoothing primarily for their own benefit, rather than to provide more information to investors. Additional analyses reveal that co‐CEOs' earnings smoothing leads to adverse consequences for investors, including lower earnings quality, higher costs of capital, increased financial distress, greater credit risk, and higher stock price crash risk. Our study contributes to the literature by highlighting the potential costs associated with co‐CEO structures, particularly in terms of reduced earnings informativeness.

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.

Measuring Multi-Dimensional Mobile Behavior Effect on Inclusive Finance: Evidence from China

Credit Invisible is one key area that many countries put much effort to solve in decades. According to the 2020 World Bank statistics, for example, there are over 500 million Chinese and 45 million American, classified as credit invisible who don’t have banking and finance history in bank or credit bureau, making them difficult to borrow money from financial institution. Previous studies adopted different non-financial information to evaluate one’s credit worthiness and status to address this issue. However, they provide little information about how real mobile user interactions can be used to solve this issue in inclusive finance. This paper proposes a novel data generative framework to fusion APP data, call detail record data and SMS data with a total of 4,689 attributes derived from a large-scale mobile dataset. We then construct a unique set of mobile behavior-driven credit risk factors based on statistical diversity, intensity, consistency, and regularity of mobile user behavior characterizing user preferences, attitudes, geolocation, and temporal patterns. Empirical analysis demonstrates that the newly discovered mobile behavior factors are useful as new inputs for credit scoring and proves the factors representing new source of positive and negative credit information. Decision tree analysis and Quantile regression are conducted to validate effect of these factors to credit default. It facilitates credit assessment based on non-financial data for the credit invisible people, which promoting inclusive finance to larger community in society. We also analyze implications of mobile user characterization findings in relation to credit default which helps decision makers to optimize credit policy and product design.

Quantitative Easing and Direct Lending in Response to the COVID‐19 Crisis

AbstractThis paper develops a dynamic general equilibrium model to study quantitative easing (QE) and direct lending to firms. QE works through three channels: expanding bank reserves raises liquidity and lowers the liquidity premium, purchasing assets withdraws risk and lowers the volatility risk premium, and the resulting economic stimulus lowers the credit risk premium. When bank reserves are higher, the liquidity premium channel is weaker, and QE is less expansionary. Direct lending is more expansionary than QE because it substitutes bank lending and mitigates the credit risk frictions associated with bank lending, while QE stimulates bank lending and worsens the frictions.