Search Results (298)

Within a coordinated fiscal financial policy framework, this study combines complex network analysis with cellular automata to construct a contagion model of debt risk across industrial enterprises. It then uses numerical simulations to examine the dynamic evolution and mitigation strategies of debt risk contagion. The results show that the following: (1) As the contagion probability, immunity failure probability, and contagion probability of immune enterprises increase, debt risk contagion among industrial enterprises is strengthened, whereas higher immunity probability and recovery probability improve network stability. (2) Market noise, carbon tax rate, credit interest rate, and risk preference increase the basic reproduction number relative to the critical boundary of one, whereas fiscal subsidy intensity, green credit ratio, and risk assessment capability reduce it. Within the normalized simulation framework, a carbon tax rate around 0.3, fiscal subsidy intensity around 0.15, and green credit ratio around 0.5 serve as illustrative model-based reference values for interpreting changes in debt risk contagion pressure and risk-mitigation effects. (3) Coordinated fiscal–financial intervention can more effectively reduce $R_0$ and narrow the contagion scope than a single policy tool, suggesting that debt risk prevention should combine fiscal support, green credit allocation, risk assessment improvement, and carbon-policy rhythm management. Full article

Prudential regulation traditionally evaluates credit, market, and liquidity risks through separate indicators, providing a fragmented assessment of institutions’ financial soundness. This study proposes the Risk Capacity Index (ICR) as an integrated measure of the structural risk-bearing capacity of organizations in the Colombian solidarity sector. Rather than measuring individual risks in isolation, the proposed framework evaluates the capacity of available equity to absorb aggregate financial exposure by integrating Expected Loss, Value at Risk, and the Liquidity Gap within a single prudential metric. The conceptual design of the ICR is grounded in the notion that equity constitutes the institution’s ultimate loss-absorbing constraint, while its operational specification is developed using supervisory risk measures applicable to cooperative financial institutions. The methodology combines analytical sensitivity analysis with a forward-looking stress-testing framework based on the Prudential Regulation Authority approach. Results demonstrate that the ICR exhibits nonlinear deterioration as aggregate risk exposure increases, with liquidity risk emerging as the principal determinant of financial fragility and the viability threshold. The theoretical contribution of the ICR lies not in replacing existing prudential ratios, but in providing an integrated institution-level measure that jointly relates available loss-absorbing capital to simultaneous exposures across multiple financial risks within a common analytical framework. The proposed index therefore complements established measures of capital adequacy, liquidity resilience, and financial soundness by offering a consolidated perspective on institutional risk-bearing capacity. Full article

Climate-related risks are now perceived as financially material, affecting banks, investors, firms, regulators, and central banks. This article provides a systematic literature review of the relationship between climate-related risks and financial decision-making. The review, which is based on Scopus and Web of Science and follows a PRISMA-based selection process, includes a final sample of 80 studies retrieved using database publication-year filters for 2015–2025, including three online-first records subsequently assigned to 2026 issues. A Quality Appraisal Matrix, descriptive analysis, keyword co-occurrence mapping with VOSviewer version 1.6.20, and cluster-based thematic synthesis are used. The results reveal four key streams in the literature: climate risk, Environmental, Social, and Governance (ESG), and financial modeling; climate change, sustainable finance, and systemic stability; transition risk, investment, and risk assessment; and banks, performance, and financial impact. The review underscores the role of climate risks in asset pricing, portfolio allocation, lending, credit-risk assessment, disclosure, stress testing, and financial stability supervision, and identifies key gaps with respect to emerging economies, data quality, and the practical incorporation of climate risk considerations into financial decisions. Full article

This paper examines the threshold impact of media-based attention to transition and physical climate risks on banks’ credit risk among the 230 largest US commercial banks from 2011 to 2022. Using a dynamic panel threshold model, our analysis reveals a non-linear relationship between media-based climate risk and banks’ credit risk. The empirical results indicate the existence of a significant threshold dividing the data into lower and upper regimes for both climate transition risks and physical climate risks. More specifically, the estimated threshold levels are 0.500 for the transition risk index and 0.571 for the physical climate risk index. Below these critical thresholds, banks appear resilient to increased media attention to climate risks; however, once these thresholds are exceeded, growing concern about climate risks significantly increases banks’ vulnerability to credit risk. These findings highlight the critical implications of physical and transition risks for financial stability. Our results are robust to a range of alternative measures and model specifications, providing valuable insights for bank managers, regulators, and policymakers, while emphasizing the need to integrate media-based climate risk considerations into credit risk assessments and policy frameworks to strengthen the banking sector’s resilience. Full article

Public-sector sustainability and climate reporting increasingly address environmental exposure, governance, and financial effects, yet existing frameworks do not adequately disclose preparedness for low-probability, high-impact systemic risks whose probabilities, timing, thresholds, and transmission channels remain deeply uncertain. This article develops a Public-Sector Resilience Reporting Standard (PSRRS) as a pre-standard architecture for government preparedness disclosure. The design has three bounded objectives: diagnose cross-framework disclosure gaps, translate these gaps into a theoretically grounded capability-to-disclosure architecture, and demonstrate its analytical use through an illustrative Florida application and two hazard-neutral stress tests. The documentary corpus includes international sustainability and public-sector reporting standards, ISO and UNDRR resilience and continuity instruments, three Florida resilience documents, and peer-reviewed literature on resilience governance, decision-making under deep uncertainty, critical infrastructure interdependency, catastrophic uncertainty, climate-risk disclosure, public finance, climate-risk pricing, local-government credit risk, investor attention, and ransomware service disruption. A structured interpretive coding protocol classifies each framework as explicit, partial, or not explicit across nine disclosure dimensions; a codebook appendix identifies the assessment criteria, the a priori and inductively refined dimensions, and the validation boundaries. Florida is not treated as a basis for statistical or jurisdictional generalization. Instead, it illustrates how a comparatively developed resilience architecture may disclose statutory continuity, critical-asset data, project ranking, and output metrics while leaving systemic dependencies, adaptive triggers, long-horizon fiscal exposure, residual service risk, distributional effects, and assurance mechanisms insufficiently visible in the reviewed reporting corpus. AMOC and case-grounded cyber-fiscal stress tests show how the PSRRS shifts reporting from hazard inventories and funded projects toward auditable evidence of institutional capacity, adaptive readiness, and public-value protection. The article specifies mandatory, recommended, and optional clauses, evidence requirements, indicator examples, a disclosure index, a sample report structure, and a three-tier pilot conformity model. The contribution is conceptual and operational, but not yet a validated formal standard; cross-jurisdictional piloting, inter-rater coding, cost testing, assurance testing, and stakeholder consultation are identified as the next stage of standardization. Full article

Probability-of-default (PD) estimation under the Basel and IFRS 9 frameworks requires probabilities that are both discriminative and well-calibrated. Large language models applied to credit assessment via prompting or supervised fine-tuning (SFT) yield poorly calibrated probabilities, while reinforcement learning with binary correctness rewards is structurally unsuitable for probability prediction under extreme class imbalance. We propose CreditR1, a three-stage framework: an SFT cold start on evidence-filtered reasoning chains; Group Relative Policy Optimization, guided by a composite verifiable reward combining Brier-score calibration—a strictly proper scoring rule—pairwise ranking, evidence anchoring, and format compliance; and an anti-contamination evaluation protocol. On Chinese A-share corporate credit data, CreditR1 matches gradient-boosted baselines in discrimination (AUC: $0.883\pm 0.004$ vs. 0.891 for XGBoost) while reducing expected calibration error by 24.2% versus isotonic-calibrated XGBoost (ECE: 0.047 vs. 0.062) and by 47.2% versus uncalibrated XGBoost (0.089). Because the test set contains only 119 default events, all comparisons carry firm-level bootstrap confidence intervals; the calibration advantage remains significant against every baseline after Holm–Bonferroni correction, including Platt, beta, and Bayesian-binning recalibrations. Ablations confirm each reward component is necessary. CreditR1 delivers calibrated PDs with evidence-grounded reasoning that supports internal model validation and human review; transferability beyond the Chinese A-share market remains an open empirical question. Full article

Credit risk assessment is a core component of financial decision-making. This study develops an explainable machine learning framework for modeling loan approval decisions on heterogeneous tabular data, centered on a Cross-Attentional Tabular Transformer that applies bidirectional cross-attention between numerical and categorical feature groups. The prediction target is historical loan-approval status, treated as a proxy for, not a direct measure of, borrower default risk; a supplementary validation on a dataset with an authentic default label is also reported. Class imbalance is addressed through focal loss, and post hoc interpretability is provided through SHAP analysis. Three classifiers, Random Forest, Gradient Boosting, and the proposed transformer, are evaluated on a 5000-sample credit dataset using accuracy, precision, recall, F1-score, ROC-AUC, and average precision. Gradient Boosting achieves the best performance (accuracy 0.9640, F1-score 0.9189), with Random Forest comparable; the proposed transformer reaches 0.9530 accuracy and 0.8949 F1, without surpassing the ensembles and at substantially higher computational cost. A five-split robustness comparison additionally evaluates XGBoost, LightGBM, CatBoost, and calibrated logistic regression: all three Gradient-Boosting variants and both classical ensembles exceed the transformer’s performance on every metric, while calibrated logistic regression does not. The evaluated baseline set excludes deep tabular architectures such as TabNet, FT-Transformer, SAINT, and TabPFN-style methods. Across the three primary classifiers, SHAP identifies credit score, employment status, and income as the dominant features, consistent with domain expectations. The results characterize the observed performance–efficiency trade-off between ensemble methods and attention-based tabular learning under the evaluated data conditions. Full article

This study examines the relationship between ESG performance and financial performance and resilience in the banking sector, using a sample of systemically important banking institutions in Romania and Poland for the period 2020–2024. The study aims to assess the extent to which ESG performance contributes to improving financial performance and strengthening the financial resilience of banking institutions operating in these two emerging economies in Central and Eastern Europe. The research employs an empirical framework based on correlation analysis, panel regression models (Fixed Effects and Random Effects), selected on the basis of the Hausman test, with robust standard errors, as well as a robustness analysis using ESG variables lagged by one year. Financial performance is assessed using the Return on Assets (ROA) and Return on Equity (ROE) indicators, whilst financial resilience is analysed using the Capital Adequacy Ratio (CAR), Liquidity Coverage Ratio (LCR), Non-Performing Loans (NPLs) and Cost of Risk (CoR). ESG performance is examined both through the aggregate ESG score and through its individual environmental, social and governance components. The results highlight that ESG performance does not show statistically significant associations with traditional indicators of financial performance. Instead, the analysis reveals differentiated associations between the ESG components and indicators of financial resilience, with the social dimension being associated with credit risk indicators (NPL and CoR), whilst the environmental and governance components do not show significant effects in the estimated models. The study’s contribution lies in the simultaneous analysis of financial performance and financial resilience using a panel framework applied to banks in Romania and Poland, as well as in highlighting the heterogeneous nature of the relationship between ESG components and the various dimensions of financial resilience. The results complement the literature on the banking sector in Central and Eastern Europe and offer relevant implications for banking institutions, investors and regulators, without implying causal relationships between the variables analysed. Full article

Artificial intelligence, digital payments and platform-based services are reshaping the operating conditions of Africa’s informal economy and micro-enterprise sector. While mobile money, digital marketplaces, app-mediated logistics and algorithmic scoring systems are commonly presented as instruments of financial inclusion and enterprise modernisation, their effects on informal traders remain uneven, conditional and under-governed. This systematic review synthesises recent peer-reviewed evidence on AI-mediated digitalisation pathways for informal and micro-enterprises in Africa, with particular attention to mobile money, platform payments, app-based logistics, digital credit, algorithmic management and platform governance. Following PRISMA-informed systematic review procedures, this review analyses 60 peer-reviewed, DOI-bearing articles published between April 2022 and June 2026 through a mechanism–outcome synthesis approach. The final corpus was selected through database searching, duplicate removal, title-and-abstract screening, full-text eligibility assessment, quality appraisal and mechanism–outcome coding. The findings show that digitalisation can expand market access, reduce cash-handling risks, create transaction histories, strengthen customer reach, improve operational continuity and support household resilience. However, the same digital infrastructures may also intensify livelihood vulnerability through opaque scoring, unexplained account freezes, fee shocks, exclusionary verification procedures, algorithmic ranking losses, data extraction and weak dispute-resolution mechanisms. The review therefore argues that informal enterprise digitalisation should not be understood only as a technology adoption issue, but as a socio-technical systems governance challenge. The article contributes a governance-and-risk framework linking local infrastructure, platform and payment design, AI (artificial intelligence) mediation, adoption conditions and livelihood outcomes. It further identifies minimum policy and design protections required for inclusive and resilient participation, including transparent fees, proportionate verification, human appeal channels, explainable restrictions, data-use consent, timely settlement and contingency mechanisms during digital outages or erroneous flags. The review concludes that sustainable digital inclusion for African informal micro-enterprises depends not merely on access to digital tools, but on the fairness, transparency, recoverability and accountability of the systems through which traders participate. Full article

Accurate default risk prediction for agricultural loans is a prerequisite for balancing financial inclusion and safety in rural finance, yet traditional assessment methods have a limited capacity to capture exogenous variables such as climate risk. To this end, this paper constructs a credit risk prediction framework integrating multi-source remote sensing data with explainable machine learning to optimize the credit profile of agricultural loans. Controlling for conventional agricultural loan variables, a logistic regression model examines the statistical association between multi-source remote sensing features and farmers’ default risk. A comparative analysis of multiple machine learning models further demonstrates that incorporating remote sensing data helps improve prediction accuracy, with temperature and precipitation volatility emerging as the most important remote sensing predictors, capturing the predominant climate-related variations in default prediction. Analysis using the Explainable Boosting Machine (EBM) quantifies the contribution of these variables to default risk prediction and identifies notable interaction patterns between remote sensing indicators and traditional agricultural loan variables. Full article

The co-pyrolysis of waste sawdust and non-recyclable polypropylene at 500 °C was investigated for low-cost adsorbent production and solid waste valorization in rural Thailand. Sustainability was evaluated through techno-economic analysis, gate-to-gate environmental screening, CO₂ emission accounting, adsorption cost analysis, and social return on investment assessment. The sawdust–polypropylene biochar produced at 500 °C production system requires a total capital expenditure of 46,000 THB and achieves a unit production cost of 316 THB kg⁻¹ at a 35% w/w biochar yield, 7–14 times lower than commercial granular-activated carbon and powdered-activated carbon. Based on a hypothetical community-scale deployment scenario, the estimated capital expenditure payback period under in-house granular-activated carbon substitution falls below six months. All annual techno-economic and SROI results presented in this study represent scenario-based screening estimates and should not be interpreted as demonstrated community-scale performance. Gate-to-gate environmental screening estimated gross production emissions of 8.771 kg CO₂e kg⁻¹ SPB-500. A consequential waste-diversion scenario incorporating carbon sequestration and avoided-disposal credits yielded a hybrid scenario-based net greenhouse-gas balance of +3.082 kg CO₂e kg⁻¹ SPB-500, supporting its potential application under the evaluated scenario of approximately 0.4–5.9 kg CO₂e kg⁻¹ relative to commercial-activated carbon benchmarks. Social return on investment analysis yields a base–case ratio of 1.39:1 (five-year total present value: 6,025,841 THB; minimum across sensitivity scenarios: 1.13:1), with water quality improvement (SDG 6; 46.1%) and health risk reduction (20.7%) jointly accounting for 66.8% of monetized outcomes, confirming investment justification from public health benefits alone. Quantifiable alignment is demonstrated across five UN Sustainable Development Goals. Collectively, these findings suggest that SPB-500 co-pyrolysis has the potential to be an economically accessible and socially beneficial waste-valorization technology under the evaluated scenario, supporting its potential application in decentralized heavy-metal remediation in resource-constrained communities. Full article

The European sustainable finance agenda has increased the demand for ESG information, yet most small and medium-sized enterprises (SMEs) remain outside mandatory sustainability reporting requirements, which are largely designed for large listed firms. This creates an information gap for banks required to integrate ESG risks into credit assessment, while SMEs often lack proportionate tools to disclose and signal their sustainability-related practices. This paper addresses this gap by examining how an SME-oriented ESG rating can structure sustainability information in bank lending and which criteria and data can support a proportionate assessment framework for resource-constrained firms. Using Banca Etica’s internal socio-environmental rating model and a unique dataset of 2395 Italian SMEs, the study provides descriptive evidence on ESG score patterns by firm size, legal form and economic sector. The results suggest that Social and Governance dimensions are more readily assessable within the rating model, as they rely on observable organisational practices such as labour conditions, gender balance, stakeholder relations and governance structures. By contrast, Environmental scores are systematically lower, suggesting that environmental practices are more difficult to formalise and document through standardised assessment tools, especially when they require monitoring systems, certifications, technological adaptation and upfront investments. The paper contributes to the literature by linking the SME regulatory gap with a parallel gap in ESG rating research and by documenting how an internal bank-based ESG rating can structure SME sustainability information and support credit assessment processes. Rather than providing direct evidence of improved creditworthiness, the study shows how such ratings may function as potential signalling mechanisms for SMEs within relationship-based lending. Full article

With the broad application of machine learning (ML) and deep learning (DL) models in financial markets, it has become increasingly important to evaluate their performance compared with traditional methods, especially for credit risk scoring in financial services. This mechanism determines the probability of default (PD) for borrowers, in other words, how likely a client is to fail to repay their debt. With the rapid development and growing availability of ML/DL technologies, it is essential for banks, financial institutions, auditors and regulatory bodies to assess whether these approaches truly outperform traditional models in terms of accuracy, stability and interpretability or whether their complexity comes at a cost. A systematic review following PRISMA examined credit risk scoring models. From 520 initial articles, 117 were analyzed to compare ML/DL approaches with traditional methods. This review evaluates accuracy, stability and interpretability, offering guidance for model selection in real-world credit scoring. Logistic regression remains essential in regulated contexts requiring transparency, supporting informed decisions on balancing performance and explainability. Full article

This study examines South Africa’s financial inclusion landscape, with a focus on how regulatory design, consumer capability, and community-based financial ecosystems shape equitable access to financial services. Despite sustained policy commitments, financial inclusion outcomes remain suboptimal, with approximately 3.9 million low-income adults lacking access to basic financial services such as savings and credit accounts. The paper contributes to the financial inclusion discourse by assessing whether government and regulatory institutions have enabled access for low-income groups or reinforced existing patterns of exclusion. Using a qualitative thematic analysis of academic literature, national policy documents, and regulatory frameworks, the study identifies key structural barriers and enabling factors influencing financial inclusion in South Africa. Adopting an institutional-capability approach, the analysis integrates regulatory theory with insights from behavioural finance and community-based financial systems. The findings indicate that while the Financial Sector Regulation Act represents a significant milestone by establishing financial inclusion as a statutory objective, meaningful progress depends on a stronger emphasis on financial literacy, differentiated consumer education, and context-responsive product design. The study further highlights that fintech innovation presents both opportunities and risks, expanding access while intensifying regulatory asymmetries and operational vulnerabilities. A holistic policy approach that promotes inclusive institutional design, revised credit risk assessment frameworks, and gender-responsive financial products is therefore essential to advancing South Africa’s financial inclusion agenda. Full article

Banks must balance deposit stability, loan expansion, and regulatory compliance while operating under liquidity constraints and financial risks. This study presents a mathematical model to examine the dynamics of bank deposits and loans under the influence of liquidity mechanisms and regulatory policies. The model proceeds in three stages: a deterministic nonlinear model, a dynamic optimal control model, and a stochastic model. Under the deterministic model, deposit withdrawals are liquidity-dependent, leading to a feedback mechanism in which liquidity improves deposit stability while financing loan growth. The theoretical results demonstrate the model’s positive and bounded solutions and show the existence and local stability of equilibria. Several parameters are based on regulatory policies or calibrated from Indonesian banking data, while the unknown parameters are estimated using the particle swarm optimization (PSO) algorithm. The results show that the proposed model is capable of fitting and predicting the data and has slightly lower mean absolute percentage errors for in-sample and out-of-sample compared with the benchmark model, and achieves comparable directional forecasting performance based on the index of directionality. Sensitivity analysis shows that the capital adequacy ratio supports lending, whereas an increased reserve requirement limits lending. An optimal control approach is developed by considering the reserve and capital requirements as time-varying policy variables. By applying Pontryagin’s maximum principle, we establish the necessary conditions for optimality. Numerical experiments demonstrate that the optimal control regulation enhances financial ratios, particularly the loan-to-deposit and liquidity ratios, at a reasonable cost. Finally, the stochastic model accounts for random variations in withdrawals and credit risks. Simulation-based observations reveal that although the system becomes more volatile, the mean dynamics are close to the deterministic case. Our framework offers a data-based and analytically tractable approach for studying the dynamics of banking variables and the effects of regulatory policies. The proposed model provides a mathematical tool for assessing the long-term effects of regulatory policies on banking performance and can assist bank managers and regulators in designing strategies that balance lending activity and liquidity resilience. Full article