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Banks have faced increasing attention regarding their ability to balance Corporate Social Responsibility (CSR) initiatives, operational efficiency, and credit risk management, particularly in the wake of global financial challenges. This study examines the interplay between CSR, efficiency, and credit risk in 131 US banks from 2010 to 2018. Using the Choquet integral, two-step Data Envelopment Analysis, and a dynamic panel with the Generalized Method of Moments, the findings reveal a virtuous circle between CSR and credit risk, where CSR enhances credit risk profiles. Similarly, efficiency and risk exhibit mutual reinforcement. However, a vicious circle is identified between CSR and efficiency, indicating trade-offs between CSR objectives and operational efficiency. These insights guide policymakers and bank managers in optimizing this balance. [ABSTRACT FROM AUTHOR]

Purpose: This research analyses the solvency behaviour of systemically important (or systemic) Spanish banks, focusing on their credit risk management during the subprime mortgage crisis and the COVID-19 pandemic. Design/methodology/approach: The top three Spanish banks (BBVA, Banco Santander and Caixabank) were selected as a representative sample. Key indicators, such as the volume of assets, amount of financing or loans to clients, non-performing loan (NPL) ratio, reported volume of write-offs, equity and share capital, were analysed to assess their solvency and credit risk management. Furthermore, from the variables evaluated, a structural equation model has been proposed to evaluate the structural relationships among the variables. Findings: The results indicate a significant reorganisation of these institutions after the subprime crisis. This reorganisation was crucial for providing the necessary room to manoeuvre to overcome the challenges posed by the COVID-19 crisis. However, the study highlights the importance of implementing preventive management policies to handle future crises effectively. Originality/value: This study provides valuable insights into the solvency and credit risk management of systemic Spanish banks during two major financial crises. The evidence presented is particularly relevant for bank managers and policymakers, offering guidance on effective credit risk treatment and crisis management strategies. [ABSTRACT FROM AUTHOR]

Counterparty risk, which combines market and credit risks, gained prominence after the 2008 financial crisis due to its complexity and systemic implications. Traditional management methods, such as netting and collateralization, have become computationally demanding under frameworks like the Fundamental Review of the Trading Book (FRTB). This paper explores the combined application of Gaussian process regression (GPR) and Bayesian quadrature (BQ) to enhance the efficiency and accuracy of counterparty risk metrics, particularly credit valuation adjustment (CVA). This approach balances excellent precision with significant computational performance gains. Focusing on fixed-income derivatives portfolios, such as interest rate swaps and swaptions, within the One-Factor Linear Gaussian Markov (LGM-1F) model framework, we highlight three key contributions. First, we approximate swaption prices using Bachelier's formula, showing that forward-starting swap rates can be modeled as Gaussian dynamics, enabling efficient CVA computations. Second, we demonstrate the practical relevance of an analytical approximation for the CVA of an interest rate swap portfolio. Finally, the combined use of Gaussian processes and Bayesian quadrature underscores a powerful synergy between precision and computational efficiency, making it a valuable tool for credit risk management. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the relationship between borrower's demographic characteristics and default risk in mortgage loans to help financial institutions develop more effective lending policies. Design/Methodology/Approach: Cross-sectional data were elicited from randomly selected 6743 individual accounts from Lebanese housing banks. This study applied the binary logistic and stepwise regression models to analyze the dataset using the Stata statistical software. Model diagnosis is performed using the Hosmer-Lemeshow goodness of fit test, likelihood ratio test, model accuracy classification table and statistically significant test-ROC curve. Findings: The findings revealed that there is a significant relationship between residential loan default risk and borrower's marital status, nature of job occupation, job economic sector, job location and loan purpose. The performance of the binary logistic regression analysis demonstrates the overall percentage who is correctly classified is 91.61%. Conclusion: The log odds of default risk for widowed borrowers are about 90 percent higher than those of divorced borrowers and that of self-employed borrowers is about 54 percent higher than that of employed borrowers. Borrowers working in the banking and real estate sectors have lower default rates than borrowers working in other economic sectors. In addition, loans granted for renovation purposes have the lowest default rates compared to loans provided for purchase, under-construction and construction purposes. Practical Implications: The empirical results help financial institutions to have early warning signals in detecting financial distress and to differentiate between a high- and low-risk group of borrowers, helping in the development of tailored risk mitigation strategies and adjusting the lending criteria. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study analyzes the impact of oil market fluctuations on Sovereign Credit Default Swaps (SCDS) in three key oil-exporting economies: Saudi Arabia, Russia, and the United Arab Emirates (UAE). The study investigates how various oil shocks, namely demand, supply, and market risk, affect sovereign credit risk and how these effects are transmitted within and across these economies. Time-domain and frequencydomain analyses were used to categorize oil market shocks and structural break analysis was incorporated to account for significant global events. The findings indicate that Saudi Arabia is a primary source of credit risk volatility, influencing Russia and the UAE, with the latter being significantly affected as a net recipient of such risks. Structural breaks, such as those associated with the COVID-19 pandemic, introduce shifts in impact patterns. This study underscores the significant role of demand shocks in shaping sovereign credit risk across the countries examined. These insights are essential for policymakers, investors, and financial analysts focused on sovereign credit risk management in oil-exporting economies, highlighting the importance of considering structural changes in economic conditions. [ABSTRACT FROM AUTHOR]

This study develops a novel method for mitigating credit risk through the use of structured derivatives, focusing in particular on the use of European put options as a strategic hedging tool. Inspired by the work of Merton (1974), our approach introduces the concept of default triggered by the stock price ST breaching a predefined barrier B. By establishing a distributional equivalence between an existing default model and P(ST

Firm default prediction is important in credit risk management and understanding economic trends. Both practitioners and academic researchers have long studied it. While traditional statistical methods such as discriminant analysis and logistic regression have been used recently, machine learning and deep learning methods have been widely applied. The graph neural network (GNN) is one of the latest applications of deep-learning approaches. With the use of GNNs, it is possible to reflect the non-linear relationships of features among neighboring companies around the target company, whereas ordinary machine learning and deep learning methods focus only on the features of the target company. However, when handling large-scale graphs such as inter-firm networks, it is difficult to apply vanilla GNNs naively. Although uniform neighbor node sampling is commonly used for large-scale graphs, to the best of our knowledge, no research has focused on better sampling methods for GNN applications for default prediction. From the practical viewpoint, it means which companies should be considered with priority for firm default prediction. In this study, we propose a novel gravity model-informed neighbor sampling method based on the estimated transaction volume by utilizing knowledge from econophysics. The scope of this research is to determine whether we can improve default predictions by considering neighboring companies with larger transaction amounts compared to ordinary uniform sampling. We also verified that the proposed method improves the prediction performance and stability compared to GNNs with other sampling techniques and other machine learning methods using real large-scale inter-firm network data. [ABSTRACT FROM AUTHOR]

This article explores the significance of high-quality healthcare data and the challenges involved in maintaining data quality. Healthcare data possess unique characteristics such as heterogeneity, incompleteness, timeliness, longevity, privacy, and ownership. Inaccurate data can result in financial losses, increased expenses, and can impact the performance of artificial intelligence models. The article emphasizes the necessity for tailored quality assessment indicators and methods for healthcare data, as well as the implementation of quality management measures. Additional research is required to redefine quality assessment criteria and develop quantification methods specifically for healthcare data. The authors stress the importance of interdisciplinary collaboration among experts from diverse fields to ensure effective data quality assessment. [Extracted from the article]

This paper uses a generalised stacking method to introduce a novel hybrid model that combines a one-dimensional convolutional neural network 1DCNN with extreme gradient boosting XGBoost. We compared the predictive accuracies of the proposed hybrid architecture with three conventional algorithms-1DCNN, XGBoost and logistic regression (LR) using a dataset of over twenty thousand peer-to-peer (P2P) consumer credit observations. By leveraging the SHAP algorithm, the research provides a detailed analysis of feature importance, contributing to the model's predictions and offering insights into the overall and individual significance of different features. The findings demonstrate that the hybrid model outperforms the LR, XGBoost and 1DCNN models in terms of classification accuracy. Furthermore, the research addresses concern regarding fairness and bias by showing that removing potentially discriminatory features, such as age and gender, does not significantly impact the hybrid model's classification capabilities. This suggests that fair and unbiased credit scoring models can achieve high effectiveness levels without compromising accuracy. This paper makes significant contributions to academic research and practical applications in credit risk management by presenting a hybrid model that offers superior classification accuracy and promotes interpretability using the model agnostic SHAP framework. [ABSTRACT FROM AUTHOR]

Complex Machine Learning (ML) models used to support decision-making in peer-to-peer (P2P) lending often lack clear, accurate, and interpretable explanations. While the game-theoretic concept of Shapley values and its computationally efficient variant Kernel SHAP may be employed for this aim, similarly to other existing methods, the latter makes the assumption that the features are independent. The assumption of uncorrelated features in credit risk management is fairly restrictive and, thus, prediction explanations coming from correlated features might result in highly misleading Shapley values, even when considering simple models. We therefore propose an evaluation of different dependent-feature estimation methods of Kernel SHAP for classification purposes in credit risk management. We show that dependent-feature estimation of Shapley values can improve the understanding of true prediction explanations, their robustness and is essential for better identifying the most relevant variables to default predictions coming from black-box ML models. RESEARCH HIGHLIGHTS: We propose estimation of feature-dependent Shapley values for P2P credit risk management We consider different linear and non-linear predictive models with varying degrees of dependence Dependent feature estimation of Shapley values can improve prediction explanations and their robustness Loan amount and interest rate are the most determinant features to loan default prediction explanations [ABSTRACT FROM AUTHOR]

We explore the potential of machine learning (ML) models applied in two financial risk management areas, i.e., credit risk management and financial risk hedging, through two practical use cases. This comparative study starts with the issue of explainability in complex ML models used in peer-to-peer lending for credit risk management. The first use case examines the limitations of using Kernel-SHAP with dependent features and evaluates different methods for estimating these dependencies using the Lending Club dataset. Our results suggest that accounting for feature dependence improves the understanding and robustness of prediction explanations. The second use case investigates a dynamic method for hedging foreign exchange risk in international equity portfolios, emphasizing the importance of accurate forecasts of currency returns. The analysis demonstrates that predictions yielded by ML models can significantly enhance the hedging of portfolios against currency risk. These findings highlight the transformative potential of advanced ML models in financial risk management, showcasing their capability to improve financial risk measurement and management. Further, our study outlines future research directions to advance this field. [ABSTRACT FROM AUTHOR]

Copyright of BES Journal: International Journal of Business & Economic Studies is the property of BES Journal - International Journal of Business & Economic Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Humanities Journal of University of Zakho (HJUOZ) is the property of Humanities Journal of University of Zakho and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

Banking risk management systems are sets of work methods for responsible bank departments, facilitating a positive financial result under conditions of uncertainty. The object of the study is the credit risk of corporate borrowers of commercial banks in the Russian Federation. The subject of the study is credit risk management based on internal ratings of corporate borrowers. The purpose of the work is to analyze the risk management system of commercial banks and develop an internal credit risk management model for corporate borrowers. Research methods: content analysis, analytical and statistical processing of information; methods for assessing cause-and-effect relationships and expert assessments. The relevance of the presented model is due to the regulatory need for commercial banks to switch to internal ratings to assess the risk of lending. The advantages of the model include optimized costs for establishing factor indicators, as well as the valence of selected explanatory variables. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the simultaneous effect of variations in the Capital Adequacy Ratio and Credit Risk of Islamic banks of the Gulf Cooperation Council under the influence of the Basel III regulations using an innovative approach. Design/methodology/approach: This approach highlights the critical importance of the Basel III reform in preserving the stability of the regional and international financial sector in the Gulf Cooperation Council and globally by examining the complex dynamics between Capital Adequacy Ratio and Credit Risk and their interaction under regulatory constraints. The annual reports and financial performance of 26 Islamic banks were analyzed over the period 2013–2021. Findings: The findings highlight the critical importance of the Basel III reform in preserving the stability of the regional and international financial sector in the Gulf Cooperation Council and globally by examining the complex dynamics between Capital Adequacy Ratio and Credit Risk and their interaction under regulatory constraints. The annual reports and financial performance of 26 Islamic banks were analyzed over the period 2013–2021. Originality/value: The insights from findings help define effective strategies to manage and mitigate Credit Risk while strengthening solvency under Basel III prudential supervision. Policymakers, regulatory authorities and banking institutions can optimize the management of Credit Risk and create a robust and stable financial environment for Islamic banks. [ABSTRACT FROM AUTHOR]

Lending to the agricultural sector by commercial banks in Ghana is characterized by high credit risk. Empirical evidence suggests that commercial banks in Ghana have credit risk management (CRM) challenges. This study explores the credit risk mitigation strategies adopted by commercial banks to minimize credit risk in agricultural finance in Ghana. The study adopted a mixed-method approach using a survey questionnaire and interview instruments. The findings indicate that some of the strategies used by commercial banks to mitigate credit risk in agricultural finance do not meet commercial banks' CRM needs. In addition, Ghanaian commercial banks have not fully adopted some of the recommended strategies that are used to mitigate credit risk associated with agricultural lending. The study unveils some appropriate strategies used to mitigate credit risk exposure in agricultural finance among commercial banks. These strategies include agricultural value-chain financing, collaboration with off-takers, incentive-based and risk-sharing schemes, adoption of a holistic agricultural value chain financing, policy interventions, use of agricultural insurance pool, and the proper structuring of agricultural loans. [ABSTRACT FROM AUTHOR]

For a bank-based economy like Vietnam, the commercial banking sector's conduct greatly influences Vietnamese economic and social prosperity. In Vietnam, net income from credit activities hold the largest portion of the total revenue of Vietnamese commercial banks. Therefore, in the context of Vietnam, credit risk obviously also plays a pivotal important role in the banking sector. Hence, the risk of credit failure can lead to a bank's collapse and have a profound effect on a country's societal structure. As seen in the previous literature, there are many macroeconomic and bank-level factors that have commonly affected the level of credit risk; however, these factors may change in the recent development era of the banking industry, especially the new impacts of digital transformation and the transition to full Basel III adoption. The overall aim of this study is to analyze the impacts of digital transformation and Basel III implementation on the credit risk level of Vietnamese commercial banks during the period from 2017 to 2023, with a sample of 21 Vietnamese listed commercial banks. This study employs the pooled OLS, fixed effect model (FEM), and random effect model (REM) methods to reach the finding that investing in technology for the readiness of digital transformation and implementing Basel III could adversely affect credit risk. Based on this finding, the authors give some recommendations for commercial banks to enhance the sustainability, safety, and better management of credit risk. [ABSTRACT FROM AUTHOR]

This study examines the efficiency of credit allocation among various types of banks in China. Seven different varieties of Chinese banks' performance are examined from 2011 to 2021. We introduce a novel hybrid pseudo‐Malmquist–Grey–TOPSIS model that effectively addresses the need for integrating uncertainty and heuristics in relocation decision‐making. Our analysis reveals that the Joint‐Stock Commercial Bank demonstrates superior performance in terms of credit allocation efficiency. In contrast, rural commercial banks exhibit suboptimal performance when it comes to credit allocation convenience, indicating the importance of prudent credit risk management for this category of banks. It is important to note that the loan allocation performance of each bank category is primarily influenced by the distinct characteristics associated with that specific bank type. However, it is crucial to understand that the success of each bank category's frontier, regardless of its quality, should not be considered a causative determinant of the overall performance of such types of banks. [ABSTRACT FROM AUTHOR]

Do lenders securitize or price loans in response to credit risk? Exploiting exogenous variation in regional credit risk due to foreclosure law differences along U.S. state borders, we find that lenders securitize mortgages that are eligible for sale to the government-sponsored enterprises (GSEs) rather than price regional credit risk. For non-GSE-eligible mortgages with no GSE buyback provision, lenders increase interest rates as they are unable to shift credit risk to loan purchasers. The results inform the debate surrounding the GSEs' buyback provisions, the constant interest rate policy, and show that underpricing regional credit risk increases the GSEs' debt holdings. [ABSTRACT FROM AUTHOR]

Society is increasingly relying on predictive models in fields like criminal justice, credit risk management, and hiring. To prevent such automated systems from discriminating against people belonging to certain groups, fairness measures have become a crucial component in socially relevant applications of machine learning. However, existing fairness measures have been designed to assess the bias between predictions for protected groups without considering the imbalance in the classes of the target variable. Current research on the potential effect of class imbalance on fairness focuses on practical applications rather than dataset-independent measure properties. In this article, we study the general properties of fairness measures for changing class and protected group proportions. For this purpose, we analyze the probability mass functions of six of the most popular group fairness measures. We also measure how the probability of achieving perfect fairness changes for varying class imbalance ratios. Moreover, we relate the dataset-independent properties of fairness measures described in this work to classifier fairness in real-life tasks. Our results show that measures such as Equal Opportunity and Positive Predictive Parity are more sensitive to changes in class imbalance than Accuracy Equality. These findings can help guide researchers and practitioners in choosing the most appropriate fairness measures for their classification problems. [ABSTRACT FROM AUTHOR]

In the context of the new era, the promotion of rural revitalization has become an important strategic goal for China to achieve common prosperity, and financial services play a crucial supporting role in this process. This paper discusses the synergy between digital finance and traditional finance in rural revitalization in Shaanxi Province and its optimization strategy. By analyzing the financial practices in Shaanxi Province's rural revitalization, including the improvement of financial policies, the improvement of basic rural financial services, the supply and innovation of financial resources, and the development of special financial products, it reveals the key role of financial services in promoting rural economic development and achieving balanced regional development. The study found that the application of digital financial technologies, such as big data and blockchain, is conducive to improving credit assessment and risk management capabilities. And, the continued promotion of new financial products will further support the diversification and sustainable development of the rural economy. [ABSTRACT FROM AUTHOR]

An efficient credit risk management model is a promising technique that provides Financial Institutions or Banks the ability to determine a creditworthy customer from a non-worthy customer. The fact remains that no country’s economy can survive or improve without credit using historically available data. This paper presents an evaluation of several gradient descent techniques, and metaheuristic optimization algorithms implemented in Machine Learning and Multi-layer perceptron for better credit risk prediction. It also handles imbalanced dataset using smote Edited Nearest Neighbour. The study provided various architectures and advantages of the algorithms while addressing how the limitations can be improved to build a better credit risk model and improve model accuracy. The study showed MLP WOA achieved accuracy of 98.56% based on Adam gradient descent to achieve faster convergence and exploration compared to MLP PSO with 98.39%. [ABSTRACT FROM AUTHOR]

This study investigates the influence of Non-Performing Assets (NPAs) on the financial performance and profitability of banks, with a focus on the significance of credit risk management in maintaining financial stability. Credit risk arises when borrowers fail to meet their repayment obligations, posing a challenge to banks' financial health. The research aims to assess how NPAs affect profitability, liquidity, and the overall operations of Indian banks, particularly in the public sector, and explore strategies to manage these risks effectively. The research follows a quantitative approach, relying on secondary data from sources such as bank financial reports and RBI publications. The analysis focuses on evaluating credit risk management practices and their relationship with NPAs, applying statistical methods to draw meaningful insights. The study also examines key RBI guidelines related to credit risk and NPAs. Results reveal that efficient credit risk management, including strong asset monitoring and timely recovery efforts, can significantly reduce the negative effects of NPAs on bank performance. The study recommends adopting comprehensive risk assessment strategies, enhancing recovery processes, and implementing stricter asset quality controls. The research highlights the importance of improving credit risk management practices in public sector banks to reduce NPAs and strengthen their overall performance. Addressing these issues will help banks improve profitability, enhance investor confidence, and contribute to greater financial sector stability. [ABSTRACT FROM AUTHOR]

Financial market dynamics in the context of the foreign exchange and currency markets have undergone changes through diverse changes and transformations including integration of innovations such as artificial intelligence (AI). Financial market strategies including hedging and pricing strategies through the implementation of AI have been capable of impacting the currency, credit, and financial derivative markets against market exposure risks. AI technology acts as an innovative integration that intends to improve the situation of foreign exchange, credit risks, currency market, and financial derivatives strategies of hedging and pricing through its algorithmic and predictive models. AI's predictive and automating capabilities are among its beneficial and useful aspects that contribute to innovate financial derivatives regarding market exposures and risk management of credit and currency markets by reducing the risk of error and enhancing productiveness and preparedness for risk management within the market. [ABSTRACT FROM AUTHOR]

Predicting the corporate default probability accurately is the core of credit risk management. There has been a relatively small amount of the literature on predicting a firm's forward default risk. In particular, we wish to emphasize certain features of the panel data that are often overlooked in the analysis of default forecasting. First, the panel data are observed at discrete-time points with a large unit of time such as month, quarter, or year. Second, repeated survival status outcomes from the same firm are highly correlated. Thus, the continuous-time treatment or an independence assumption is often violated in practice. To avoid these potential drawbacks, we propose an extension of the discrete-time forward hazard model by assigning a frailty variable specifically to each firm. We use a real panel dataset to illustrate the proposed methodology. Using the dataset, our results first support the significance of including the firm-specific frailty variable in the extended model. Then, using an expanding rolling window approach, our results confirm that the extended model provides better and more robust out-of-sample performance than its alternative without frailty. Thus, accounting for firm-specific frailty can consistently yield more accurate predictions of firms' forward default probabilities. [ABSTRACT FROM AUTHOR]

This paper intends to investigate the risk management of inclusive digital financial platforms. First, it explains the idea of smart cities, their function, and inclusive financial risk control technologies based on big data. The varieties of digital inclusive financial platforms and their risk profiles are next examined. The Back Propagation (BP) neural network is used to build a BP-KMV model based on the KMV model. Finally, utilizing M Company as a case study, this paper uses the BP-KMV model to examine the credit risk and risk management of unlisted enterprises on the digital inclusive financial platform. The results show that of the four unlisted companies, L Company has the greatest default rate (7.35%), while J Company has the lowest default rate (4.82%). The highest research and development (R&D) spending rate is 14.1% for J company, while the highest patent ownership rate is 43.09% for L company. The data demonstrates a negative correlation between the percentage of R&D expenditures and the default rate of unlisted enterprises. In other words, a larger default risk is associated with lower R&D expense rates. Additionally, there is a correlation between patent ownership and default rates that is positive, suggesting that higher patent ownership rates are linked to higher default rates. Additionally, the risk management technologies of M business can complement one another. The theoretical research of comprehensive digital inclusive finance risk control can be enriched by the risk analysis of digital inclusive financial platforms utilizing the BP-KMV model in the context of smart cities. [ABSTRACT FROM AUTHOR]

This study examines the determinants of credit risk in Nepalese commercial banks, emphasizing macroeconomic and bank-specific factors. The study utilizes a random effects regression model to investigate the impact of various factors on non-performing loans using panel data from 10 commercial banks in Nepal from 2013-2022. The study's theoretical framework draws on established economic theories, including Kalecki's business cycle theory and Diamond & Dybvig's banking theory. It aims to contextualize the relationship between credit risk and various influencing factors. The theory sets the stage for analyzing credit risk determinants in Nepalese banks. The findings demonstrate that non-performing loans are significantly and positively associated with bank size and return on assets, whereas asset quality and bank age have a negative and significant impact. The capital adequacy ratio exhibits a positive but insignificant impact. Among macroeconomic variables, the inflation rate has a positive and significant impact on nonperforming loan, whereas real gross domestic product growth reveals a positive but insignificant relationship. These findings are of utmost importance for bank managers and policymakers in Nepal, as they provide valuable insights to enhance credit risk management practices and maintain financial stability in the banking sector. [ABSTRACT FROM AUTHOR]

The researcher investigated the effect of credit risk management practices on the financial performance of rural banks. The researcher examined the suitable credit risk environment, credit giving procedures, credit administration, monitoring, and control, and evaluated the substantial influence of these practices on the banks' financial performance. Further, the researcher drew conclusions based on the study findings to which rural Banks has a comprehensive written credit risk management policy in place, and the board of directors is responsible for its execution. To ensure financial stability, credit risk management should be central to a bank's activities. Credit risk management refers to the systems, processes, and controls that a corporation has in place to ensure efficient consumer payment collection and reduce the risk of nonpayment. To attain the goal of wealth maximization, banks must properly manage their assets, liabilities, and capital. Credit policy should include the bank's lending philosophy, particular procedures, and methods for monitoring lending activities. The study found that credit risk management practices had no meaningful impact on rural bank financial performance. [ABSTRACT FROM AUTHOR]

Credit risk migration is often modeled as a first-order, time-homogeneous Markov chain. Due to the high cost of continuously monitoring an obligor, most financial institutions risk-rate loans and other fixed-income securities on an infrequent basis, resulting in discrete point-in-time transition-probability matrixes for credit risk management and/or reporting. Several methods have been proposed for approximating the infinitesimal generator matrix, the matrix exponential of which approximates the observed point-in-time matrix. We motivate and propose a novel non-parametric method for approximating a generator matrix for bond ratings in practice, and compare the results to existing empirical methods. The results suggest that our method has merit and may be more flexible than existing ones. Further, in some cases, the best method to use appears to depend on the bond rating itself. [ABSTRACT FROM AUTHOR]

We examine a comprehensive set of systematic and firm‐specific determinants of the credit default swap (CDS), using a two‐step approach to explore the factor's effect on CDS spread changes. We show that systematic factors are important and account for the most changes in the CDS spreads (with average R2 ${R}^{2}$ of 35%), while firm‐specific factors are limited (with R2 ${R}^{2}$ of 5% in panel regression) with only 4 out of 28 firm‐specific factors being significant. It implies that the systematic factors are overlooked in the literature, and they can provide many implications for practitioners in CDS pricing and the firm's credit risk management. [ABSTRACT FROM AUTHOR]

This study examines the impact of female representation on the Board of Directors (DDP), Board of Commissioners (DKP), and Audit Committee (KAP) on credit risk in commercial banks in Indonesia. Utilizing panel data with 399 observations from various banks over a specified period, the Random Effect Model (REM) was applied to analyze the relationship between the independent variables (DDP, DKP, and KAP) and the dependent variable (credit risk). The results indicate that DDP has a significant negative impact on credit risk (coefficient -4.331768, p = 0.0000), suggesting that increasing the proportion of women on the Board of Directors tends to reduce credit risk. This could be attributed to the diversity of perspectives and caution in decision-making brought by women, as well as a push for higher transparency and accountability. The DKP shows a nearly significant negative impact on credit risk (coefficient -1.371344, p = 0.0593). Although its impact is not as strong as DDP, the presence of women on the Board of Commissioners can also reduce credit risk through enhanced supervision and control. Conversely, KAP does not have a significant impact on credit risk (coefficient 0.508613, p = 0.5055). This suggests that while gender diversity on the audit committee is important for regulatory compliance and internal control, it may not directly influence credit risk management. Theoretically, these findings support the literature that gender diversity on boards improves the quality of decision-making and risk management. Managerial implications emphasize the importance of increasing female representation on the Board of Directors and Board of Commissioners to reduce credit risk and enhance the financial stability of banks. Gender diversity policies should be implemented at all organizational levels to maximize their benefits in corporate governance. This study provides insights for policymakers and practitioners in the banking sector on the importance of gender diversity in managing risk and improving the financial performance of banks. [ABSTRACT FROM AUTHOR]

This study was designed to evaluate managing problem loans and work out the pinnacle of bank failures in Nigeria. One of the recommendations of the Basel Committee on Banking Supervision was credit risk management which is the optimization of the bank's risk-adjusted rate of return by maintaining credit risk exposure within an acceptable level. Descriptive survey research was used and data were collected via annual reports of the sampled bank within the period of 2011-2016. The population of the research included the Deposit Money Banks. Pearson Coefficient of Correlation was the statistical tool used to analyse the hypotheses and this was done with the aid of Statistical Package for Social Sciences (SPSS). The researcher concluded that there is no significant relationship between credit risk management and bank failure in Nigeria. However, there were traces of weak negative relationships which keen interest should be given to because of the sensitive nature of the banking sector. [ABSTRACT FROM AUTHOR]

Copyright of Central Asian Economic Review is the property of Narxoz University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scenario analysis is a comprehensive approach to assess the impact of climate-related transition risk on businesses. Environmental, social, and governance (ESG) scores are popular tools with financial institutions (FI's) for green-scoring practices and since they characterise a company's performance from an ESG perspective, they have been criticised for enabling "greenwashing" when used within the context of climate risk. Commercially available ESG scores are also available for listed entities, while FI counterparties are often unlisted. This study develops a methodology for creating in-house environmental scores (E-scores), which are then used to effectively choose appropriate transition pathways to be used in company-specific forward-looking scenario analysis. Such scenario analysis can be used to forecast the company's financial position, including the cost of its greenhouse gas (GHG) emissions, and quantify the impact of transition climate risk on specified metrics. The choice of metrics depends on what the results of the analysis are used for. Two metrics are identified for being useful for risk management and credit decisions: future profitability and weighted average carbon intensity. Finally, the study demonstrates how this process can be implemented with a practical worked example, using only publicly available data. [ABSTRACT FROM AUTHOR]

Credit risk prediction method acts as a vital financial tool for measuring the default probability of credit applicants. For financial institutions, proper credit risk management becomes mandatory to avoid significant losses incurred by borrowers' default. Thus, statistics are an increasingly vital technique that can analyse and measure credit risk. Generally, manual auditing and statistical methods measure credit risk. Current developments in financial artificial intelligence (AI) evolved from machine learning (ML)-driven credit risk methods that obtained great interest from academia and industry. The most significant step in the process of developing a credit risk assessment method is feature selection, which chooses a subset of appropriate features for enhancing the performance of an ML technique. With this motivation, this study concentrates on the design of sand cat swarm optimization-based feature selection with hybrid deep learning (SCSOFS-HDL) model for credit risk assessment. The presented SCSOFS-HDL technique presents a new SCSOFS technique for the optimal selection of feature subsets from the credit risk data. In addition, the deep LSTM Supervised Autoencoder Neural Network (DLSTM-SANN) model is presented for classification purposes. To enhance the performance of the DLSTM-SANN technique, the political optimizer (PO) methodology is utilized for the hyperparameter tuning process. The experimental validation of the SCSOFS-HDL technique is tested on credit risk datasets and the results highlighted the better performance of the SCSOFS-HDL algorithm with maximum accuracy of 96.49% and 96.12% on German Credit and Australian Credit datasets, respectively. [ABSTRACT FROM AUTHOR]

Through in-depth analysis of credit risk in the context of the development of Internet finance, the study recognized that its diversification and complexity not only include financial risks, but also involve the impact of non-financial factors. Based on this, combined with ESG factors, principal component analysis and grey correlation method are used to optimize the indicators, ensuring the comprehensiveness and accuracy of the indicator system. Using Convolutional Neural Network (CNN) as the warning model, considering the dynamic and static characteristics of data, a sub convolutional network was designed for training different types of data. At the same time, the credit rating division and warning threshold selection methods were optimized, improving the accuracy and practicality of the model. Through experimental verification, we found that the CNN model has a high accuracy in credit risk warning, and it also shows excellent classification performance and comprehensive effect compared to other models. Through systematic research and application, this paper provides a complete solution for the credit risk management of Internet financial enterprises, and makes positive contributions to the stability of the financial system and risk prevention. At the same time, we also emphasize the important role of technological innovation in financial intelligent management, especially the application of innovation systems centered on AI and knowledge graphs in the field of credit management, which points out the direction for the future development of the financial industry. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The importance of enterprise credit risk management is increasingly prominent. Under the background of financial technology and big data, this paper studies a model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) by comprehensively analyzing multi-dimensional data such as corporate financial statements, credit scores and transaction records, so as to capture and learn the complex characteristics of credit risk of Internet financial enterprises. Firstly, this paper collects the relevant data of Internet finance enterprises from multiple data sources, and carries out standardization processing and missing value filling. Then, CNN-LSTM model is constructed based on these data, and model training and hyperparametric optimization are carried out by adjusting convolution layer and LSTM layer. In addition, this study also designed a comparative experiment to evaluate the performance of CNN-LSTM model and CNN and LSTM models alone in prediction time, prediction accuracy and interpretability. The results show that CNN-LSTM model has significant advantages in predicting the credit risk of Internet finance enterprises. The model also shows a faster response speed, and the maximum warning time is only 701ms, which is much lower than the LSTM and CNN models. The highest accuracy of the model is 94.1%, which is significantly higher than that of LSTM and CNN models. In addition, the model also has high confidence in interpretability, which provides a solid basis for financial decision-making. [ABSTRACT FROM AUTHOR]

Nowadays, Russian banks are developing and updating models for assessing the probability of default (PD) for various risk segments of corporate borrowers. This is to enable the use of rating models when assessing regulatory capital, in compliance with the requirements of the Regulations of the Bank of Russia. The application of rating models allows credit institutions to more accurately distribute regulatory and economic capital among borrowers, calculate reserves according to RAS and IFRS, set interest rates for transactions with borrowers as part of the pricing procedure, and conduct stress testing of both borrower portfolios and individual borrowers. The results of stress testing can then be incorporated into strategic planning. One of the Regulator's requirements is to consider assessments of sustainable development and responsible financing ESG (environmental, social, and corporate governance) indicators as part of the credit risk management procedure, particularly in the development of models for assessing the probability of default. This research focuses on the development of an integral module (ESG rating) that includes ESG indicators. The aim is to improve the accuracy of existing models for assessing the probability of default (PD models) for corporate borrowers through the use of ESG factors. The findings of this research can be utilized by Russian banks to enhance the discriminatory and predictive capabilities of their own PD models, and by the regulator to understand the set of ESG indicators that impact the creditworthiness of corporate borrowers. [ABSTRACT FROM AUTHOR]

Credit is an important marketing tool. It bears a value, the value of the vendor having to borrow until the customer's charge arrives. Ideally, that fee is the rate however, as most customers pay later than agreed, the extra unplanned fee erodes the planned net earnings. Risk is described as unsure ensuing in unfavorable outcome, negative in relation to deliberate goal or expectation. It is very difficult o find a threat loose investment. [ABSTRACT FROM AUTHOR]

One of the most critical aspects of credit risk management is determining the capital requirement to cover the credit risk in a bank loan portfolio. This paper discusses how the credit risk of a loan portfolio can be obtained by the stochastic recovery rate based on two approaches: beta distribution and short interest rates. The capital required to cover the credit risk is achieved through the Vasicek model. Also, the Black-Scholes Merton model for the European call option is utilized to quantify the Probability of Default (PD). Value at Risk (VaR) and Conditional Value at Risk (CVaR) are used as measures of risk to evaluate the level of risk obtained by the worst-case PD. A stochastic recovery rate calculates VaR related to the underlying intensity default. In addition, the intensity default process is assumed to be linear in the short-term interest rate, driven by a CIR process. The loan portfolio performance is evaluated by considering the relevant characteristics with the Data Envelopment Analysis (DEA) method. This study proposes the losses driven by the stochastic recovery rate and default probability. The empirical investigation uses the Black-Sholes-Merton model to measure the PD of eighth stocks from different industries of the Iran stock exchange market. [ABSTRACT FROM AUTHOR]

The interest rate hikes intended to combat inflation have not only significantly increased mortgage payments, but also significantly depressed house prices. The probability of default for mortgages is typically estimated through the debt servicing ability of the obligor. The alternative estimation based on the default option, although well studied in literature, is typically not used by practitioners. This option is normally 'deep out of money' and moves in the money if the loan to value (LTV) increases significantly, even creating negative equity. This is typically a remote probability due to the down payment requirements, but not as much under the current environment, where the house price depreciation has significantly increased LTVs, especially for newer mortgages underwritten when house prices were at their peak. This paper discusses the potential risk management oversights in this environment, illustrating that the increasing default risk is not adequately captured in probability of default (PD) models based on debt servicing ability alone. This is also true for the loss given default (LGD) risk if the contemporaneous LTV effects are not captured. Numerical examples are provided to demonstrate the material increase in PD, LGD and the combined expected loss risk with increasing LTV. It also discusses an alternative default option PD model, its calibration and its usage for stress testing along with LGD modelling, which together capture the contemporaneous LTV effects. [ABSTRACT FROM AUTHOR]

Credit risk consists is the expectation of losses stemming from the inability of a borrower to repay a loan. For the purpose of accurate control of credit risks, banking systems seek developing financial information portfolios upon their customers using sophisticated models which are not only restricted to collecting information on borrower's characteristics, but also, provide visibility on their respective default risk. This paper introduces a novel deep learning model to forecast the credit risk of company customers in banking systems. In particular, we develop a hybrid SVMLSTM based neural network that predicts the total turnover of a company given the historical data records of its economic and financial features within specific periods. Through an empirical investigation based on data of 13 Tunisian manufacturing and service companies, we show that our proposed model results in more accurate statistical performances compared to the standard LSTM and to the linear regression that is commonly used in the area of credit risk management. [ABSTRACT FROM AUTHOR]

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This research has investigated the impact of market competition on efficiency and profitability in banking sector of Pakistan with mediating role of credit risk. Panel data of 19 conventional banks operating in Pakistan has been used for the time period of 2006-2016. By using Panel data techniques this study has found that market competition has significant impact on profitability and efficiency of Pakistani banks. Credit risk is positively and significantly related with market competition. Moreover, the impact of credit risk on banks profitability and efficiency is significant and negative. The empirical findings show that credit risk mediates the relationship between market competition and ROA as well as market competition and cost efficiency. The results of this study may help to suggest modifications in current banking principles to boost market competition, to certify financial stability and to increase banks efficiency and profitability. [ABSTRACT FROM AUTHOR]