Search Results (141)

This paper investigates credit risk management as a dynamic system. Panel Vector Autoregression (PVAR) is employed to model interrelationships among four key components: Non-Performing Loans (NPLs), Loan Loss Provision (LLP), loan charge-off (LCO) and capital. The Cost-to-Income ratio (CIR) and Size and Net Profit-to-Equity ratio (ROE) are used as control variables. The panel dataset comprises 1461 conventional rural banks in Indonesia with a quarterly frequency from June 2010 to March 2024. There are several key findings of this study. First, credit risk management practices in rural banks predominantly follow an incurred loss approach, although the expected loss model appears to be more commonly adopted by larger institutions. Second, capital serves a critical function as a buffer against credit losses. Third, subsample investigation reveals a significant role of accounting discretionary. This study offers significant implications for both policy development and academic research in microfinance. Full article

Credit risk refers to the possibility that a borrower fails to meet contractual repayment obligations, posing potential losses to lenders. This study aims to enhance post-loan default prediction in credit risk management by constructing a time-series modeling framework based on repayment behavior data, enabling the capture of repayment risks that emerge after loan issuance. To achieve this objective, a Residual Enhanced Encoder Bidirectional Long Short-Term Memory (ResE-BiLSTM) model is proposed, in which the attention mechanism is responsible for discovering long-range correlations, while the residual connections ensure the preservation of distant information. This design mitigates the tendency of conventional recurrent architectures to overemphasize recent inputs while underrepresenting distant temporal information in long-term dependency modeling. Using the real-world large-scale Freddie Mac Single-Family Loan-Level Dataset, the model is evaluated on 44 independent cohorts and compared with five baseline models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) across multiple evaluation metrics. The experimental results demonstrate that ResE-BiLSTM achieves superior performance on key indicators such as F1 and AUC, with average values of 0.92 and 0.97, respectively, and demonstrates robust performance across different feature window lengths and resampling settings. Ablation experiments and SHapley Additive exPlanations (SHAP)-based interpretability analyses further reveal that the model captures non-monotonic temporal importance patterns across key financial features. This study advances time-series–based anomaly detection for credit risk prediction by integrating global and local temporal learning. The findings offer practical value for financial institutions and risk management practitioners, while also providing methodological insights and a transferable modeling paradigm for future research on credit risk assessment. Full article

Leveraging the exogenous shock of Chinese commercial banks’ adoption of the United Nations Principles for Responsible Banking (UNPRB) as a quasi-natural experiment, this study employs a Time-Varying Difference-in-Differences (TV-DID) approach to investigate how formally committed responsible credit, augmented by Fintech, enhances ESG performance in private enterprises. The findings reveal that banks adopting UNPRB significantly improve the post-loan ESG performance of their private enterprise borrowers compared to non-adopting banks, with Fintech serving as a positive moderator. Mechanism analysis indicates that, under the empowerment of financial technology, commercial banks that extend loans to enterprises are influenced by the signing of the United Nations Principles for Responsible Banking (UNPRB). Banks promote the sustainable development of enterprises through pre-loan “screening effects” and post-loan green “governance effects”. Heterogeneity analysis indicates stronger ESG improvement effects for enterprises in environmentally sensitive industries, those with high capital intensity, and those holding long-term loans. Extended research further identifies a significant enhancement in ESG alignment between banks and enterprises following UNPRB adoption. By examining responsible credit investment, this study not only broadens the scholarly discourse on sustainable finance and Fintech but also offers empirical insights from a representative emerging market context. Full article

In a situation where the number of non-performing loans (NPLs) increases, lenders may raise interest rates to compensate for potential losses, and the amount of credit granted in the market may decrease, leading to credit rationing. Such actions may become vital based on their potential consequences for the economy, entrepreneurs and consumers, which makes this topic extremely important. This study, by using an empirical VAR analysis, has strived to determine whether credit rationing by banks operating in the Polish banking sector is driven by risky loans (which are the main determinant of credit rationing and are represented by the ratio of NPLs to total loans). According to the results, it has been found that credit rationing, made by Polish banks, is not statistically significant when the risk in the credit market rises due to non-performing loans. Therefore, it can be claimed that the risky structure due to NPL in the credit market may not be one of the determinant factors of credit rationing in the Polish banking sector. The low sensitivity of the Polish banking sector to the risky structure of the credit market may result from the relatively low share of loans in total assets compared to debt instruments. Furthermore, restrictive lending policies and the predominance of mortgage loans secured directly by real estate limit portfolio risk, which may reduce the need for a risk-sensitive lending strategy. Full article

This study examines the association between Saudi banks’ internal adoption of AI-enabled FinTech-related digital tools and their financial performance, sustainability performance, and financial stability over the period 2015–2024. Using a panel dataset of 10 banks, the analysis investigates how the adoption of AI-driven technologies—such as machine-learning credit assessment, robo-advisory systems, and automated compliance tools—is related to market performance (Tobin’s Q), accounting performance (ROA and ROE), financial stability (Z-Score), and sustainability outcomes measured by both Bloomberg ESG Disclosure Score and the LSEG ESG performance-oriented score. To ensure robust inference and reduce simultaneity concerns, the empirical strategy employs Pooled OLS and Fixed Effects Models with Driscoll–Kraay standard errors, as well as a dynamic Fixed Effects Models incorporating lagged dependent variables, lagged independent variables, and shock-interaction terms. Bank-specific characteristics—including size, age, leverage, liquidity, loan-to-deposit ratio, non-performing loans, net interest margin, market capitalization, and board size—are included as controls. The findings indicate a positive and statistically significant relationship between banks’ internal adoption of AI-enabled digital/FinTech-related technologies and their financial performance, sustainability performance, and financial stability. These relationships remain robust across estimation approaches, providing insights for policymakers, regulators, and bank managers seeking to advance digital transformation while safeguarding financial soundness and supporting sustainable development in the Saudi banking sector. Full article

Integrating Environmental, Social, and Governance (ESG) metrics into supply chain finance is critical for promoting sustainable development. However, the dynamic mechanisms through which real-time ESG performance influences credit allocation and, consequently, shapes credit risk and environmental risk exposures for financial institutions, remain poorly understood, especially when compared to traditional static and retrospective ESG evaluations. To address this, we developed an agent-based model that simulates interactions among green enterprises, a financial institution, and a regulator, featuring a dynamic credit algorithm that adjusts credit lines based on real-time ESG scores. Our simulations demonstrate that ESG-driven credit policies significantly boost green technology adoption among SMEs, raising adoption rates from 20% to over 85% under strong incentives, which in turn drives a substantial reduction of the supply chain’s carbon footprint by more than 50%. Notably, this environmental benefit is achieved without a commensurate surge in credit risk, as the non-performing loan ratio only experienced a moderate increase. Additionally, sensitivity analysis reveals a non-linear relationship between the ESG weighting in credit decisions and environmental outcomes, identifying a critical threshold for policy effectiveness. Our findings offer risk managers and policymakers evidence-backed strategies for designing dynamic incentives that effectively promote supply chain decarbonization while managing associated financial risks. Full article

The rapid proliferation of fintech has created unprecedented opportunities for enhancing bank credit-risk management and promoting financial sustainability. Using an unbalanced panel dataset of Chinese commercial banks spanning 2013–2023, we construct a bank-specific fintech index through text mining of annual reports combined with an entropy-weighted methodology, and systematically examine the relationship between fintech adoption and credit risk. Our empirical findings reveal that fintech adoption significantly mitigates credit risk, reducing the non-performing loan ratio by an average of 0.9 percentage points. This effect is more pronounced among non-state-owned banks and in regions with less developed service sectors. Mechanism analysis further demonstrates that financial sustainability is a critical transmission mechanism: fintech mitigates credit risk by improving both cost efficiency and asset efficiency, thereby enhancing banks’ economic resilience. Additionally, we find that regional green development is a powerful moderator that significantly amplifies the risk-reducing impact of fintech. These findings offer robust empirical evidence for guiding commercial banks’ digital transformation strategies and informing regulators’ green finance policy formulation. Our results underscore the strategic importance of fintech investment in building more resilient and sustainable banking systems. Full article

This study uses a dataset of conventional banks from 2010 to 2022 to investigate the moderating effect of board characteristics (BC) on the relationship between corporate social responsibility (CSR) and bank stability in the MENA region. Bank stability is measured using the Z-ROA index, which captures a bank’s ability to withstand financial shocks. The study addresses endogeneity and heterogeneity concerns using the system generalized method of moments (SGMM), with diagnostic tests confirming the validity of instruments and the absence of second-order autocorrelation. Three main conclusions are presented. First, CSR has a major detrimental impact on bank stability, indicating that when poorly managed or misaligned with strategic objectives, CSR initiatives may weaken financial resilience. Second, board attributes such as independence, diversity, and experience have a positive impact on bank stability, highlighting the importance of sound governance in ensuring prudent financial management. Third, the interaction between CSR and board characteristics exerts a positive and significant influence on bank stability, suggesting that well-structured boards can enhance the strategic value of CSR initiatives. As a robustness check, the study re-estimates the model using non-performing loans (NPLs) as an alternative measure of bank stability. The results remain consistent with the baseline findings, confirming the robustness and credibility of the conclusions. CSR continues to show a positive association with NPLs, while board characteristics and their interaction with CSR maintain negative and significant effects. These findings reinforce that effective board governance can transform CSR practices into stability-enhancing strategies. For policymakers and banking executives seeking to integrate sustainability into governance frameworks, the results underscore the crucial role of corporate governance in translating CSR efforts into tangible stability outcomes. The study calls for greater regulatory focus on board structures to maximize the stability benefits of CSR in the banking sector, contributing to the growing body of research on CSR and financial stability in developing economies. Full article

This study investigated how banks’ balance sheet fundamentals shape their credit growth using panel co-integration methods and two estimation methods—pooled mean group (PMG) and dynamic fixed effects (DFE). Both approaches yielded consistent core results. First, weaker asset quality, proxied by higher non-performing loans (NPLs), was strongly and negatively related to credit growth: PMG produced a large negative long-run coefficient, and DFE’s error-correction form confirmed a significant adverse effect, consistent with higher provisioning, thinner capital buffers, and lower risk-taking. Second, capitalization (equity to assets) supported long-run growth under PMG, while DFE—imposing common slopes—did not, suggesting heterogeneous capitalization effects across banks that PMG captured but DFE muted. Third, operating expense intensity showed a positive long-run association with credit growth in both models, consistent with expansionary spending accompanying durable lending rather than costs causing lending. Long-run effects for liquidity and market-risk sensitivity were weaker or mixed: liquidity’s role was imprecise, and market-risk sensitivity was positive in PMG but not significant in DFE, again pointing to cross-sectional heterogeneity. Error-correction terms were large, negative, and highly significant in both models, indicating rapid convergence—near full adjustment within one period, with slight overshooting in DFE. Short-run results showed that higher liquidity and temporary cost spikes dampened contemporaneous growth. Policy implications emphasize sustained oversight of asset quality and prudent capital planning to support long-run credit supply. Full article

This study explores the effect of the energy transition on financial stability in the context of 13 OECD countries during the period from 2009 to 2019. In this sense, the soundness of the financial system is expressed through two dimensions: the Zscore and the volume of non-performing loans (NPLs). Using a dynamic panel estimation with the Generalized Method of Moments (GMM), the results highlight the complex effects of the energy transition on financial stability. Switching from fossil to clean energy improves the Zscore and reduces NPLs. In addition, the study reveals heterogeneous impacts depending on the renewable energy source involved. In fact, wind energy makes a positive contribution to both dimensions of financial stability. By linking the dynamics of the energy transition with the resilience of the banking sector, this study provides new insights into how sustainable energy policies can foster long-term financial sustainability. The effects of solar power and hydroelectricity, while positive overall, are not without nuances. Specifically, the former reduces the NPLs but also the Zscore, while the latter has the opposite effect on both aspects of financial stability. At this point, it is crucial to take into account the varying effects of different renewable energy sources when assessing the financial repercussions of the energy transition. Full article

Banks in the United States face persistent challenges from non-performing loans (NPLs), despite conducting thorough client evaluations before issuing loans. To mitigate the impact of NPLs and support both local and global growth, banks must adopt effective risk management strategies. This study investigates the effect of NPLs on bank growth and the moderating of bank size and Capital Adequacy Ratio (CAR) through the lens of the Resource-Based View (RBV) theory. A sample of 253 banks listed on the New York Stock Exchange from 2006 to 2023 was selected using specific inclusion criteria from the Thomson Reuters Eikon DataStream. To address cross-sectional dependence and endogeneity, advanced estimation techniques—Feasible Generalized Least Squares (FGLS), Driscoll and Kraay standard errors, and the Generalized Method of Moments (GMM)—were employed. The results show that NPLs have a significant negative impact on banks’ asset and income growth. Furthermore, bank size and capital adequacy ratio (CAR) negatively and significantly moderate this relationship. These findings underscore the need for banks to enhance credit risk management by strengthening loan approval processes and leveraging advanced analytics to assess borrower risk more accurately. Full article

We investigate whether and how the policy of establishing green finance pilot zones affects corporate environmental performance in China, by employing the DID model and taking 2324 Chinese A-share listed companies as the empirical sample. The main findings show that the green finance policy can significantly improve corporate environmental performance in the green finance pilot zones. The policy effect varies according to enterprise ownership, sector, and degree of environmental supervision. In particular, compared with private enterprises and enterprises subject to key pollution monitoring, the environmental performance of state-owned firms and non-key pollution-monitored firms is more positively affected by the green finance policy. Through a mechanism analysis, we find that corporate innovation and financial constraints can play partially mediating roles in the linkage of green finance policy and corporate environmental performance. Among them, the mediating effects of green innovation and financial constraints are more prominent in private enterprises and key pollution-monitored enterprises. However, although the green finance policy can positively influence bank loans obtained by enterprises, there is no evidence to suggest that bank credit plays a significant mediating role between the green finance policy and corporate environmental performance. Our findings are helpful for understanding the effect of green finance policy on environmental sustainability and could provide some references for policymakers. In particular, we suggest that private and key pollution-monitored enterprises should actively respond to the green finance policy, broaden financing channels, and enhance capability of green innovation, thereby improving their environmental performance. Full article

The significant increase in non-performing loans (NPLs) during the escalating recession of the Greek economy motivates us to study the predictive power of credit rating models in periods of economic shocks. In parallel, we examined the responsibilities of bank management in the expansion of NPLs in this adverse environment. Certain studies connect bad loans with turbulent conditions. Our paper weighs the relative significance of both economic shock and management effectiveness using data at an individual level, which provides the originality of our study. We use a unique dataset of small business loans that were granted during 2005 (expansion period) by a large commercial Greek bank, and we explore their performance between 2010 and 2012 (early recession period). In the context of a stepwise methodology, we compare the Bank’s credit scoring model with three other prediction models (binomial logistic regression, decision tree, and multilayer perceptron neural network) to check both the predictive ability of credit scoring models during recession and the effectiveness of bank management. The comparative analysis confirms the management’s responsibilities in granting NPLs, since the Bank’s model exhibited the worst predictive performance. Additionally, we find that adverse external conditions lead to an increase in NPLs and decrease the predictive performance of all credit scoring models. The study offers a reliable methodological tool for lending management in economic downturns. Full article

This study explores how FinTech and artificial intelligence (AI) adoption shape efficiency and financial stability in dual-banking systems. It focuses on 26 listed Islamic and conventional banks across 11 countries in the MENA and Southeast Asia regions between 2020 and 2024. To measure digital adoption, we create a seven-component FinTech Adoption Index. We use fixed-effects regressions to examine its impact on cost efficiency, profitability, solvency stability, and credit risk. This analysis also controls bank size, capitalization, and macroeconomic conditions. The results show a clear adoption gap. Conventional banks consistently score 0.5–0.8 points higher on the FinTech Index compared to Islamic banks. Each additional FinTech component raised operating costs by about 0.8%, but improved profitability slightly by only 0.03%. This suggests that technological integration creates upfront costs before any real efficiency gains are seen. However, the stability benefits are stronger. FinTech adoption increases the Z-score by 3.6 points and lowers the non-performing loan ratio by 0.1%. Islamic banks gain more stability benefits due to their risk-sharing contracts and asset-backed financing structures. Overall, an efficiency–stability trade-off emerges. Conventional banks focus more on profitability, while Islamic banks gain resilience, but face slower efficiency improvements. By combining the Resource-Based View and Financial Stability Theory, this study provides the first multi-country evidence of how governance structures shape digital transformation in dual-banking markets. The findings offer practical guidance for regulators and bank managers around balancing innovation, efficiency, and stability. Full article

The importance of non-performing loans (NPLs) for the stability of financial sectors is difficult to overestimate. The NPL level depends on numerous factors; this study’s goal is to determine the impact of macroeconomic factors on NPLs with the mediation effect of foreign, saving and social factors in Kazakhstan’s banking sector. To determine the affecting factors, the authors performed a systematic literature review. To determine the dependencies between constructs, the Partial Least Squares Structural Equation Modeling (PLS-SEM) method was used. Macroeconomic factors’ direct effect on non-performing loans (NPLs) was examined; a significant negative dependence was determined. The mediation effect of foreign, saving, and social factors was investigated. Foreign factors have a mediation effect, strengthening the dependence between macro factors and NPLs. Nevertheless, they do not have a mediating effect; moreover, they balance and make the effect of macro factors on NPLs statistically insignificant. These findings allow policy-makers to stabilize the situation on NPLs in the financial markets of developing countries like Kazakhstan by directly influencing not only the financial sector but also other sectors of the national economy. Full article