Search Results (41)

This study explores the effect that the adoption of International Financial Reporting Standards (IFRS) 9 has on credit risk in commercial banks in Cambodia, focused primarily on non-performing loans (NPLs) as a significant indicator. In the static and dynamic panel estimations, the analysis shows that the NPL behavior is best characterized using a dynamic specification, which passes relevant diagnostic tests and leads to evidence of persistence and endogeneity, which has not been conducted in Cambodia yet. The study covered the period from 2013 to 2024. During this period, 26 commercial banks had complete datasets. Combining time-series and cross-sectional data, the total sample size was 312 observations. The results show substantial path dependence in NPLs, suggesting credit deterioration is persistent and that early measures are needed. We find evidence that the adoption of IFRS 9 is positively and significantly associated with increased measures of NPLs, though we interpret this as consistent with improved transparency and forward-looking recognition of expected credit losses—and not indicative of deterioration in underlying asset quality. Bank-specific determinants such as profitability, size, leverage, and liquidity emerge as key predictors of credit risk; banks with stronger financial fundamentals experience improved asset quality. Macroeconomic factors like economic growth are key to decreasing NPLs in the dynamic framework as well. The results highlight the need for forward-looking accounting standards, prudent bank-level practices, and macroeconomic stability. Policy issues include increased supervisory vigilance, legal conservatism when assessing IFRS 9-related indicators, a revision of the capital and liquidity regulatory framework in relation to counterparties operating with them, as well as coordinated macroeconomic policies aiming at boosting the financial system—economy arterial connection. Full article

Following the global financial crisis, the transition to IFRS 9’s forward-looking Expected Credit Loss (ECL) model has introduced significant implementation complexity, particularly in emerging markets facing data limitations. This study investigates the heterogeneous ECL compliance strategies adopted within the Cambodian banking sector during a period of heightened credit stress, marked by a system-wide non-performing loan ratio of 8.6%. Utilizing a multiple-case study design and replication logic, a qualitative content analysis was conducted on the 2024 audited financial statements of 13 representative institutions, ranging from market leaders to international subsidiaries. The findings reveal a pronounced technical divide: market leaders utilize advanced internal statistical methods, such as cohort analysis, whereas international subsidiaries rely on top-down parent-group proxy models to bridge local data gaps. A “macro-correlation paradox” was identified, where certain institutions prioritize faithful representation by excluding macroeconomic variables when statistical links to historical defaults remain weak. Furthermore, a significant transparency gap exists, where granular disclosures are consistent with a signaling interpretation regarding institutional safety. These results suggest that ECL compliance in data-limited environments may be interpreted as a strategic management choice rather than a standardized technical exercise, highlighting the need for regulatory standardization of modeling assumptions to improve inter-bank comparability. Full article

Credit risk evaluation is a key factor in financial intermediation, regulatory capital provision, and risk management in the portfolio. In this study, we compare the deep learning performance for probability-of-default (PD) estimation with a structured financial econometric model using loan-level data of an Indian non-banking financial agency between May and August 2025. Using the interpretation of PD as a conditional expectation, which is in line with reduced-form default-intensity models, we compare deep learning, logistic regression, and gradient boosting using a pure time-based out-of-sample design. Model assessment focuses on discrimination and calibration, where the area under the precision–recall curve (AUC-PR), Brier score, log-loss, and Hosmer–Lemeshow goodness-of-fit tests are utilized. The findings show that deep learning achieves higher accuracy in terms of calibration but a lower Brier score by about 18; this gap could be reduced by comparing logistic regression with statistically significant improvements in formal tests that compare forecasts. In portfolio back-testing, better probability scaling is translated into an actual loss reduction of about 12–13% for the August 2025 cohort. Although the improvements compared with the advanced ensemble techniques are moderate, the results indicate that deep learning improves the estimation of conditional default probabilities because of the better nonlinear modeling and upper-tail risk perception. This study contributes to the literature via its incorporation of machine learning and credit risk assessment into a formalized risk management and econometric assessment system. Full article

Financial fraud represents one of the most critical operational risks faced by financial institutions, resulting in significant financial losses and destabilizing markets. While machine learning models are effective at prediction, their evaluation is often based on statistical performance metrics that do not directly translate into financial impact. This research develops an evaluation framework that integrates the costs of early fraud detection with predictive effectiveness and economic criteria for decision-making. Several supervised learning models (XGBoost, neural networks, Random Forest, decision trees, and logistic regression) were trained and tested on an imbalanced dataset of credit card transactions. To assess the potential benefit of these models for financial institutions, the savings rate and expected loss were employed alongside conventional metrics such as F1 score, AUC-PR, AUC-ROC, recall, and accuracy. The results show that economic outcomes are highly sensitive even among models with similar predictive performance. The ensemble methods, in particular, achieved the optimal balance between fraud detection capabilities and loss reduction, while models optimized solely for accuracy resulted in higher operating costs due to false positives or undetected fraud. The results indicate that the choice of fraud detection models should not be based solely on predictive accuracy, but also on cost asymmetry and risk tolerance. The proposed framework provides practical guidance to financial institutions seeking to align operational risk management and regulatory requirements with machine learning implementation, enabling risk-informed decision-making. Full article

Credit default prediction from firm-level accounting statements is central to risk management, yet the underlying financial data are highly sensitive and often siloed across banks, auditors, and platforms. Federated learning (FL) offers a practical route to collaborative modeling without centralizing raw records, but standard FL optimization can converge slowly under severe client heterogeneity, heavy-tailed accounting features, and label imbalance typical of default events. This paper proposes FedQuAD, a novel fast-converging FL algorithm that couples (i) quasi-Newton curvature aggregation on the server with a lightweight limited-memory update to accelerate global progress, (ii) a proximal variance-reduced local solver that stabilizes client drift under non-IID accounting distributions, and (iii) federated robust standardization of tabular financial ratios via secure aggregated quantile statistics to mitigate scale instability and outliers. FedQuAD is communication-efficient by design: It transmits compact gradient and curvature sketches and adapts local computation to each client’s stochasticity and drift. We provide convergence guarantees for strongly convex default-risk objectives (logistic and calibrated GLM losses) under bounded heterogeneity, and extend the analysis to nonconvex deep tabular models via expected stationarity bounds. Experiments on public credit-risk benchmarks with simulated cross-silo (institutional) partitions demonstrate that FedQuAD reaches target AUC and calibration error with substantially fewer communication rounds than representative baselines while maintaining privacy constraints compatible with secure aggregation and optional client-level differential privacy accounting. Full article

To mitigate credit risk, banks are required to set aside a specific amount as a safety net to absorb the expected loss on a banks’ loan portfolio called loan loss provisions (LLPs) or provisions for bad debts. All banks worldwide had to adopt International Financial Reporting Standard 9 (IFRS 9) as the financial reporting standard. Unlike its predecessor (i.e., International Accounting Standard 39, IAS 39), IFRS 9 accelerates the recognition of losses by requiring provisions to cover both already-incurred losses and some losses expected in the future by calculating the expected credit loss (ECL). To evaluate if the obligor’s credit quality has deteriorated, the IFRS 9 standard requires banks to compare the obligor’s probability of default (PD) at the inception phase of the loan and at the reporting date. Thus, three methodologies are explored in this study (i.e., Cox proportional hazard (PH), Extended Cox PH, and Random Boosting Forest (RBF)) for computation of the PD term structures using Kaplan–Meier as the benchmark model under IFRS 9. The purpose of this research is to illustrate the application of three methodologies on the publicly available mortgage loan portfolio from Freddie Mac using different measures of goodness-of-fit and the predictive accuracy measure, i.e., the Concordance index (C-index). The comparison analysis reveals that the extended Cox PH and RBF models provide better predictive accuracy (higher C-index) but at the cost of increased complexity and potential overfitting (higher information criteria). However, Cox PH has shown the most efficient fit, and offers a stable and understandable hazard trajectory. Finally, for reproducibility, the SAS and R codes are included to illustrate how each of the results (in form of a table or figure) were obtained. Full article

This study presents a comprehensive analysis of the marginal capacity credit of utility-scale solar and wind power in South Korea using an effective load-carrying capability-based methodology. This research makes three key contributions distinguishing it from previous works. First, the study introduces the concept of marginal capacity credit to quantify the contributions of newly added renewable energy capacities in power systems that already host significant solar and wind power capacities. Second, it evaluates the interaction effects between solar and wind power, revealing their complementary potential in enhancing system adequacy across different penetration levels. Third, it investigates how integrating energy storage systems mitigates intermittency and aligns renewable generation with peak demand. Results indicate that solar power provides relatively high marginal capacity credit at low penetration levels due to its alignment with peak demand, but its contribution declines as deployment expands and peak hours shift. Conversely, wind power maintains more stable marginal capacity credit and eventually surpasses solar power at higher penetration levels due to its broader generation profile. Storage integration notably enhances marginal capacity credit for both resources, with solar power gaining greater benefit from optimized charging and discharging strategies. These findings provide practical guidance for improving power system reliability and capacity planning under growing renewable penetration. Full article

This paper develops a quantitative framework for climate–financial risk measurement that combines a spatially explicit jump–diffusion asset–loss model with prudentially aligned risk metrics. The approach connects regional physical hazards and transition variables derived from climate-consistent pathways to asset returns and credit parameters through the use of climate-adjusted volatilities and jump intensities. Fat tails and geographic heterogeneity are captured by it, which conventional diffusion-based or purely narrative stress tests fail to reflect. The framework delivers portfolio-level Spatial Climate Value-at-Risk (SCVaR) and Expected Shortfall (ES) across scenario–horizon matrices and incorporates an explicit robustness layer (block bootstrap confidence intervals, unconditional/conditional coverage backtests, and structural-stability tests). All ES measures are understood as Conditional Expected Shortfall (CES), i.e., tail expectations evaluated conditional on climate stress scenarios. Applications to bank loan books, pension portfolios, and sovereign exposures show how climate shocks reprice assets, alter default and recovery dynamics, and amplify tail losses in a region- and sector-dependent manner. The resulting, statistically validated outputs are designed to be decision-useful for Internal Capital Adequacy Assessment Process (ICAAP) and Pillar 2: climate-adjusted capital buffers, scenario-based stress calibration, and disclosure bridges that complement alignment metrics such as the Green Asset Ratio (GAR). Overall, the framework operationalises a move from exposure tallies to forward-looking, risk-sensitive, and auditable measures suitable for supervisory dialogue and internal risk appetite. Full article

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

In recent years, the financial sustainability and survival of microfinance institutions (MFIs) have been seriously threatened by factors such as the reduction in donations, cooperation funds and international aid, and increased competition from commercial banks. Faced with this hostile scenario, which may limit access to credit for disadvantaged groups, MFIs must apply techniques to improve their efficiency, viability, lending capacity and survival. The objective of this study is to design a microcredit pricing model based on the Internal Ratings-Based approach, Basel III and probability of default to enhance access to credit for disadvantaged groups. We analysed a sample of 4550 microcredit transactions and 30 influential variables (25 idiosyncratic and 5 systemic). Our empirical results reveal that the IRB system is more equitable for borrowers and more efficient for MFIs, as it allows lower interest rates to be applied to borrowers with better credit histories. The application of the proposed IRB model can improve the sustainability, competitiveness and viability of MFIs by promoting operational efficiency and reducing default rates, thus contributing to financial inclusion by increasing supply. Full article

This study examines how sovereign risk shapes the financial performance of sustainable investments, using the MSCI Emerging Markets ESG Index as a reference. The analysis covers 24 emerging and frontier economies from Latin America, Asia, the Middle East, and Eastern Europe during 2016–2025, a period marked by major global disruptions such as the COVID-19 crisis and post-2022 financial tightening. Sovereign risk dimensions are extracted through Principal Component Analysis (PCA) applied to sovereign CDS spreads, identifying a systemic component linked to global shocks and a structural component associated with domestic fundamentals and governance quality. These factors are integrated into a quantile regression framework alongside control variables—oil prices, interest rates, and global equity indices—capturing key macro-financial transmission channels. Results show a nonlinear, quantile-dependent relationship: systemic risk intensifies ESG losses under adverse conditions, while structural improvements support gains in upper quantiles. Control variables behave as expected, confirming the macro-financial sensitivity of ESG performance. The findings reveal that ESG returns are state-dependent and strongly influenced by sovereign credit dynamics, especially in emerging markets where external shocks and institutional fragility intersect. Strengthening sovereign governance and integrating risk diagnostics into ESG assessments are essential steps to enhance resilience and credibility in sustainable finance. Full article

In-Lieu Fee programs are an important mechanism for compensatory mitigation in the United States and received wide-spread standardization after the regulatory mitigation rule change of 2008. On public lands, they are especially important for pooling funds from numerous small-scale impacts that might otherwise go unmitigated. This study examines the use cases of fee program credits on public lands since 2008. Using data from the Regulatory In-Lieu Fee and Bank Information Tracking System, I analyzed eleven active In-Lieu Fee programs approved post-2008 across 78 service areas, encompassing 1043 credit transactions. Transactions were categorized by credit amount, proportion, target ecosystems, and impact designations. The analysis highlights the influence of residential and commercial development, alongside resource extraction, as major contributors to fee program transactions, underscoring the program’s role in mitigating various development pressures. Residential, commercial, and government projects frequently co-occur within service areas, which can support policy planning to anticipate potential cumulative impacts and expected future impacts and credit demands. Furthermore, my analysis shows that impacts from resource extraction require proportionally larger offsets than those from residential or recreational activities. The findings suggest that programs on public lands can fill a niche distinct from mitigation banks, as they address a multitude of impacts while further allowing for the pooling of resources and funds from small-scale impacts, while the use of advance credits remains contentious for achieving no net loss. Full article

This paper uses survival analysis as a tool to assess credit risk in loan portfolios within the framework of the Basel Internal Ratings-Based (IRB) approach. By modeling the time to default using survival functions, the methodology allows for the estimation of default probabilities and the dynamic evaluation of portfolio performance. The model explicitly accounts for right censoring and demonstrates strong predictive accuracy. Furthermore, by incorporating additional information about the portfolio’s loss process, we show how to empirically estimate key risk measures—such as Value at Risk (VaR) and Expected Shortfall (ES)—that are sensitive to the age of the loans. Through simulations, we illustrate how loss distributions and the corresponding risk measures evolve over the loans’ life cycles. Our approach emphasizes the significant dependence of risk metrics on loan age, illustrating that risk profiles are inherently dynamic rather than static. Using a real-world dataset of 10,479 loans issued by Angolan commercial banks, combined with assumptions regarding loss processes, we demonstrate the practical applicability of the proposed methodology. This approach is particularly relevant for emerging markets with limited access to advanced credit risk modeling infrastructure. Full article

This study examines the long-term financial sustainability of commercial banks, emphasizing the crucial role of credit risk management. Given that the core function of credit creation inherently exposes banks to credit risk, this analysis evaluates how five key bank-specific risk variables, namely expected credit losses (ECL_BS), impairment gains or losses (ECL_IS), non-performing loans (NPLs), common equity tier 1 capital (CET1), and leverage (LEV) affect long-term financial sustainability. Applying a panel error correction model on data from listed South African banks spanning 2006 to 2023, the study reveals a stable long-term relationship, with approximately 74% of short-term deviations corrected over time, indicating convergence towards equilibrium. By taking into account the significance of major exogeneous shocks such as the 2009–2010 global financial crisis and the COVID-19 pandemic, as well as regulatory framework changes, the results reveal persistent relationships between credit risk factors and banks’ long-term financial sustainability in both short and long horizons. Notably, expected credit losses, and impairment gains and losses exert significant negative influence on long-term financial sustainability, while higher CET1 and NPLs exhibit positive effects. The study findings are framed within four complementary theoretical perspectives—the resource-based view, institutional theory, industrial organisation, and the dynamic capabilities framework—highlighting the multidimensional drivers of financial resilience. Thus, the study’s originality lies in its integrated approach to assessing credit risk, offering a holistic model for evaluating its influence on long-term financial sustainability. This integrated framework provides valuable, actionable insights for financial regulators, bank executives, policymakers, and banking practitioners committed to strengthening credit risk frameworks and aligning banking sector stability with broader sustainable development goals. Full article

This study assesses the energy, economic, and environmental implications of switching Tanzania’s road transport sector to natural gas, which is slowly transitioning. In energy, the main goal is to identify the energy demand for petroleum fuel (diesel and petrol) and natural gas during the transition, while in the economy, the government revenue in the form of taxes for shifted and unshifted vehicles, as well as the loss in government revenue from petroleum fuel revenue post-transition, is assessed. In the environment, carbon emission in terms of carbon dioxide equivalent (CO_2e), carbon tax revenues, and carbon credit revenues post-transition is estimated. The shift involved 10, 20, and 30% of the road vehicle population. The 10, 20, and 30% shift targeted about 142,247, 183,893, and 225,540 vehicles, which in turn dropped diesel and petrol demand by 7 and 3.68%, 7 and 3.8%, and 15 and 7.5%, respectively. In natural gas, the demand started at 0.0916 billion kg and grew exponentially by 200% and later by 300%. The transition has consequences in government revenue, which takes the form of taxes on petroleum products. The shift from 10 to 30% could lead to foregone taxes amounting to Tanzania shilling TZS 0.09, 0.31, and 0.54 trillion (US$ 33,358,680, US$ 11,490,212, and US$ 20,015,208), indicating a tax loss of about 3, 9, and 15%. Contrary, the government may benefit from these losses by lowering the amount of foreign currency necessary for oil importation. In environmental benefits, the 10, 20, and 30% shift could offset approximately 8,959,198.92119, 8,438,863.65528, and 7,918,528.38937 tCO_2e, equivalent to 5.4, 10.97, and 16.47% of the road emissions. The post-transition road emissions might result in a carbon tax revenue of about US$ 71,673,591.37, 67,510,909.24, and 63,348,227.11 per year. The post-transition carbon credit revenue of about US$ 20,813,410.64, 41,626,821.27, and 62,440,231.91 is expected annually. The findings are critical for policy design and promoting a transition in the road transport sector. Full article