Search Results (938)

As variable renewable energy penetration increases, resource adequacy becomes strongly seasonal, while annual accreditation can mask temporal reliability differences. This paper proposes a Sub-Annual Capacity Market and compares it with an Annual Capacity Market and an uncapped Energy-Only benchmark. Capacity credits are calculated using a marginal ELCC formulation based on Expected Energy Not Served and embedded into phase-specific clearing constraints. Using a Shanxi case study, we examine both deterministic and stochastic settings with 151 jointly perturbed load and renewable scenarios. Results show that ACM and SubACM can both approximate EO outcomes when parameters are well calibrated, but SubACM yields more stable economic performance under uncertainty, with 29% lower cost-deviation standard deviation and 67% fewer tail-risk scenarios, as confirmed by formal dispersion tests. The main benefit of sub-annual design is improved temporal alignment between capacity payments and physical reliability contribution, rather than guaranteed large average cost reductions. Full article

The integration of algorithmic systems into critical digital infrastructure is no longer peripheral to governance, it is governance. As AI-mediated decisions influence credit access, clinical diagnoses, criminal risk scores, and infrastructure routing, the question of who controls these algorithms and whether that control is meaningful has become a central concern for states and institutions at every level of development. Existing frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act, have made real progress toward structured AI governance. However, none treats digital sovereignty as a first-order goal, nor do they provide integrated cross-layer guidance applicable across the diverse institutional landscape found worldwide. From this synthesis, we develop the Integrated AI Risk Governance Framework (IARGF): a four-layer structure covering policy and regulations, institutional oversight, technical controls, and operational execution, organized around five risk categories—technical, ethical, security, systemic, and sovereignty-related. A comparative analysis with major existing frameworks highlights the IARGF’s unique contributions, especially its explicit focus on sovereignty, adaptability across different institutional capacities, and recursive feedback mechanisms that connect all four governance layers. The framework is analyzed across three domains—healthcare AI, financial services, and critical infrastructure—to demonstrate its practical utility. Results confirm that governance effectiveness is a system property, not just a feature of individual layers; that digital sovereignty is both a governance goal and a distinct risk dimension with specific technical and institutional needs; and that context-aware, capacity-scaled governance is a design requirement, not a political compromise. The IARGF is presented as a conceptual governance model based on a systematic literature review rather than an empirically validated tool, and it remains to be tested in actual organizational settings. Its main contribution is the comprehensive theoretical integration of sovereignty, institutional capacity, and inter-layer governance dynamics, rather than proven performance advantages over existing models. Future research should aim to validate this framework through longitudinal case studies, expert panels, and retrospective failure analyses. Full article

Using panel data from 278 Chinese prefecture-level cities during 2011–2019, this study employs two-way fixed effects and instrumental variable (2SLS) models to investigate how the distinct dimensions of digital financial inclusion (DFI)—coverage breadth, usage depth, and digitisation level—affect urban economic growth. The results reveal substantial heterogeneity across these DFI dimensions. The expansion of coverage breadth significantly and robustly promotes city-level economic growth. In contrast, greater usage depth exerts a negative effect, possibly due to regulatory lags in internet credit and insurance that intensify financial risks. The digitisation level shows a positive but statistically insignificant impact, indicating that digital infrastructure has not yet been fully transformed into growth-enhancing productivity. Furthermore, the regional heterogeneity analysis reveals a stark divergence: DFI acts as a crucial growth engine in the financially underserved central and western regions, whereas excessive financialisation has exerted a crowding-out effect in eastern cities. These findings suggest that policy efforts should prioritise broadening DFI coverage while strengthening the regulation of usage-related activities, thereby balancing financial innovation with systemic stability. Full article

Forest Rights Mortgage Loans (FRMLs) have grown quickly in China’s collective forest areas, even though the basic conditions for this type of lending remain far from ideal. In many places, forest holdings are small and scattered, property rights are complex and not fully consolidated, and channels for disposing of collateral are limited. Under these circumstances, the Fulin Loan Model (FLM) in Fujian provides a useful case for understanding how forest-rights lending can still function in practice. Drawing on fieldwork, semi-structured interviews, and process tracing, this study explores both how the model was established and how it has been sustained over time. The analysis suggests that the FLM is neither a straightforward market-based lending tool nor merely a top-down policy arrangement. Rather, it relies on a more mixed form of governance in which local government support, banking procedures, and village-level social relations are brought together through specific organizational arrangements. These arrangements help lower the costs of early institutional experimentation, distribute and manage lending risks, and translate locally rooted trust into a form of credit support that formal financial institutions can recognize. As a single-case study, the FLM points to one possible way in which rural finance can be made workable under conditions of incomplete markets and strong social embeddedness. Full article

Rising non-performing assets (NPAs) remain a persistent threat to banking stability in emerging economies, including India. This study examines the role of conventional macroeconomic determinants in shaping NPA dynamics using annual panel data from 30 Indian banks over the period 2003–2022. Employing Robust Least Squares and dynamic modelling techniques, the analysis evaluates the impact of GDP growth, inflation, exchange rate movements, and repo rates, while addressing heteroscedasticity, autocorrelation, and bank-level heterogeneity. The findings indicate that currency depreciation significantly increases NPAs, whereas inflation and tighter monetary policy exert a moderating effect. GDP, however, does not exhibit a significant influence, suggesting limited macroeconomic transmission to banking asset quality. To ensure appropriate model specification, stationarity tests are conducted, guiding the inclusion of dynamic elements in the analysis. Once the model is adjusted accordingly, the results consistently highlight the relative importance of macroeconomic factors without yielding conflicting interpretations. While broader theoretical perspectives such as institutional memory and balance-sheet effects are acknowledged for contextual relevance, they are not empirically tested in this study. Overall, the findings emphasize that conventional macroeconomic variables play a meaningful, though selective, role in explaining NPA behaviour, offering clearer and more consistent insights for policy and banking practice. Full article

The global division of labor system is increasingly refined, and the core components of some manufacturing enterprises are concentrated in a few (or even a single) suppliers, resulting in supply dependence. Excessive concentration of suppliers can lead to a higher risk of supply chain disruption. To this end, taking manufacturing companies listed on the Shanghai and Shenzhen A-share markets in China from 2010 to 2024 as samples and referring to Huazheng ESG rating data, research shows how the ESG performance of manufacturing companies reduces supplier concentration. The research found that (1) the ESG performance of manufacturing enterprises significantly reduces supplier concentration,—this effect is mainly reflected in social responsibility (S dimension)—and firm size has a positive moderating effect; (2) ESG performance has a mediating effect of alleviating financing constraints and enhancing trade credit in the process of reducing supplier concentration; and (3) heterogeneity analysis results show that the inhibitory effect of ESG performance on supplier concentration is more significant in non-state-owned enterprises. Through empirical analysis, the research scope of ESG performance was expanded to the upstream supply chain field, emphasizing the importance of ESG performance in manufacturing enterprises and providing theoretical and empirical evidence for enterprises to achieve high-quality and sustainable development. Full article

Background: The resurgence of Mpox (formerly known as monkeypox) since the 2022 global outbreak has exposed weaknesses in surveillance, diagnosis, and public risk communication systems. Despite increased clinical understanding, limitations in knowledge, attitudes, and practices (KAP) among both healthcare workers (HCWs) and the general population continue to challenge prevention and control measures. Numerous systematic reviews have been published on KAP toward Mpox, yet their findings remain fragmented. This review aimed to consolidate the existing evidence from published systematic reviews to provide a unified understanding of global KAP levels related to Mpox. Methods: We followed the PRISMA guidelines for this systematic review of systematic reviews. The article search was conducted in PubMed, Embase, and the Cochrane Library for systematic reviews published between January 2010 and October 2025. Data was extracted on study design, population, and reported quantitative outcomes. Results: Five studies met the inclusion criteria: three focused on HCWs, while two focused on the general population. Among HCWs, knowledge ranged from 26.0% to 46.7%, and attitudes from 28.2% to 62.2%. In the general population, knowledge ranged from 33.0% to 46.6%, attitudes from 40.0% to 71.9%, and perceptions averaged around 40.0%. Across both groups, Mpox knowledge was limited, attitudes were moderately positive, and preventive behaviors remained consistently low, revealing a persistent gap between awareness and practice. Conclusions: This review highlights persistent gaps in knowledge, attitudes, and practices among HCWs and the general population. Although global attention increased substantially following the 2022 outbreak, important weaknesses remain in translating knowledge into consistent preventive behaviors. Addressing these gaps requires structured and context-specific interventions. Integrating Mpox-focused modules into mandatory Continuing Medical Education credits for HCWs could ensure sustained competency in diagnosis, infection prevention, and outbreak response beyond peak epidemic periods. For the general population, strategic risk communication campaigns should leverage trusted community leaders and social media influencers in high-risk regions to counter misinformation, reduce stigma, and promote evidence-based preventive behaviors. Embedding these targeted strategies within broader pandemic preparedness and global health security frameworks will be essential to strengthening early detection, public trust, and coordinated outbreak response in future Mpox or other emerging infectious disease events. Full article

Predicting loan defaults is a significant challenge for financial institutions; however, current machine learning techniques often encounter issues in areas such as data privacy, cross-institutional cooperation, and model transparency. The restrictions on the practical implementation of advanced predictive models are centralized training paradigms, which limit the application of advanced models because of regulatory and confidentiality issues, and black-box decision making, which diminishes confidence in automated credit risk tools. This study mitigates these problems by adopting a federated-inspired decentralized ensemble learning model combined with explainable artificial intelligence (XAI) in predicting loan defaults. Various machine learning classifiers are trained on partitioned institutional data without the need to share any data; they include K-Nearest Neighbors, support vector machine, random forest, and XGBoost. They use a prediction-level aggregation strategy to simulate the collaborative decision-making process without losing locality of data. SHAP and LIME are used to promote model interpretability by giving both global and local explanations of the consequences of prediction. The proposed framework was tested on a large public dataset of loans that contains more than 116,000 records, including various financial and borrower-related features. The experimental findings show that XGBoost has high and reliable predictive accuracy in both centralized and decentralized scenarios, achieving 99.7% accuracy under federated-inspired evaluation. The explanation analysis shows interest rate spread and upfront charges as the most significant predictors of loan default risk. The main contributions of this research are as follows: (i) a privacy-preserving decentralized ensemble learning framework that is applicable in multi-institutional financial contexts, (ii) a detailed analysis of centralized and decentralized predictive performances, and (iii) the pipeline of the XAI, which can be used to increase its transparency and regulatory confidence in automated credit risk evaluation. These results prove that decentralized learning combined with explainable AI can provide high-performing, transparent and privacy-sensitive loan default prediction systems in practice in real-world banking systems. Full article

We consider the problem of Bayesian parameter inference in the Merton structural credit risk model, where the posterior is induced by a jump-diffusion likelihood and the marginal evidence is not available in closed form. To approximate this posterior, we construct a variational family based on triangular sum-of-squares (SOS) polynomial flows, in which each component map is monotone by construction: its diagonal derivative is a positive definite quadratic form on a monomial basis, yielding a closed-form log-Jacobian and explicit gradients with respect to all flow parameters. The symmetric positive definite matrices parametrizing the flow are optimized by intrinsic Riemannian gradient ascent on the positive definite cone equipped with the affine-invariant metric, which preserves feasibility at every iterate without projection. We show that the rank-one Jacobian gradients produced by the SOS structure have unit norm in the affine-invariant metric, establishing a direct algebraic coupling between the transport family and the optimization geometry and implying a universal 1-Lipschitz bound for the log-Jacobian along geodesics. On the likelihood side, we derive exact score identities for all five structural parameters of the Merton model—drift, volatility, jump intensity, jump mean, and jump volatility—through both the Poisson log-normal mixture and the Fourier inversion representations. Strictly positive parameters are handled via exponential reparametrization, and the resulting gradients propagate end-to-end through the flow. We establish uniform truncation bounds on compact parameter sets for the infinite mixture and its associated score series, providing rigorous control over the finite approximations used in practice. The base distribution is chosen to be uniform on ${[0,1]}^5$, whose bounded support ensures uniform control of the monomial basis and stabilizes the polynomial calculus. These ingredients are assembled into a fully explicit modified ELBO with implementable gradients, combining Euclidean updates for vector parameters and intrinsic manifold updates for matrix parameters. Full article

Artificial intelligence (AI) is increasingly embedded in banking risk management, yet academic research on this topic remains conceptually fragmented and dispersed across multiple disciplines. This study examines global publication trends and thematic structures related to AI applications in banking risk management through a bibliometric analysis of 83 peer-reviewed articles indexed in the Web of Science Core Collection for the period 2020–2024. The analysis was conducted using Bibliometrix (R-package, version 4.1), its web interface Biblioshiny (2024 release), to evaluate publication dynamics, citation performance, authorship patterns, and thematic clusters. Results show a substantial rise in scientific interest, with annual publication growth of 41.4% and international co-authorship reaching 30%. Five major thematic clusters were identified, including AI-enabled credit risk assessment, fraud detection, operational and cyber-risk mitigation, FinTech adoption, and regulatory compliance. Approximately 30% of the articles appeared in the top ten journals publishing on the topic, and the dataset recorded more than 3800 cited references. The findings indicate that AI contributes to enhanced predictive accuracy, real-time anomaly detection, and supervisory efficiency in banking risk management, while persistent challenges relate to model transparency, data quality, and regulatory adaptation. This study offers a systematic, data-driven understanding of the intellectual landscape and research evolution of AI-driven banking risk management from 2020 to 2024. Full article

This paper investigates the interaction between fiscal policy and non-performing loans (NPLs), a nexus often overlooked in banking stability literature. By proposing a generalized theoretical framework that augments the industrial organization (IO) theory of banking with liquidity preference theory, this study explains why a fiscal contraction (an improvement in the primary balance from deficit toward surplus) can decrease NPLs in a bank’s portfolio. Using bank-level quarterly data from Guyana (2009: Q4 to 2024: Q4) and a Panel Autoregressive Distributed Lag Pooled Mean Group (ARDL-PMG) model, we find that a fiscal contraction reduces NPLs in the long run. Specifically, a one-percentage-point improvement in the seasonally adjusted primary balance (as a % of GDP) is associated with a 0.473 percentage point decrease in NPLs in the long run. This finding contrasts with the existing literature, which often suggests that fiscal consolidations increase credit risk. In the short run, however, the results indicate a divergent effect where fiscal contractions lead to a temporary increase in NPLs, with a coefficient of 0.103, likely because of immediate pressure on borrower debt-service capacity. This study contributes to the literature by extending the IO theory of banking to the fiscal policy–NPL relationship in a developing, resource-rich economy. Notably, while higher oil prices and bank efficiency significantly lower NPLs, traditional macroeconomic drivers such as GDP growth, inflation, and the real effective exchange rate—as well as the COVID-19 pandemic—are found to be statistically insignificant in this framework. Full article

Does the digital transformation of major customers foster collaborative financing for upstream suppliers, or does it amplify their bargaining power for rent extraction? This study investigates these competing hypotheses by examining the digital spillovers from major customers to supplier trade credit. Using a unique hand-collected dataset linking Chinese listed suppliers with their top five customers by accounts receivable from 2010 to 2021, we document a “dual enhancement effect”: major customer digitalization significantly increases trade credit scale and improves trade credit quality, effectively rejecting the rent extraction hypothesis. Specifically, trade credit quality is reflected in lower bad debt provision ratios, shorter receivable aging, and lower material default risk. Mechanism tests suggest that improved information transparency and stronger customer market competitiveness are important channels through which digitalization affects supplier trade credit. Cross-sectional analyses show that these effects are more pronounced for non-state-owned or low-asset-specificity suppliers, and for customers with higher asset specificity or lower importance. After ruling out alternative explanations, we further find that this digital spillover strengthens supply chain resilience. Overall, the evidence is more consistent with the collaborative financing view than with the rent extraction view, suggesting that major customer digitalization may help foster more sustainable and cooperative financing relationships within supply chains. Full article

Systemic data bias constitutes a major source of failure in real-world AI systems and represents a regulatory challenge that remains insufficiently addressed by existing legal frameworks, including the EU Artificial Intelligence Act. Although the AI Act introduces a comprehensive risk-based regulatory regime, it does not adequately capture how bias originates, propagates, and manifests across the AI lifecycle. This paper examines systemic data bias through a legal-technical lifecycle analysis that maps recurring bias mechanisms, from data collection and annotation to model training, evaluation, and deployment, to the regulatory control points established under the EU AI Act. Drawing on cross-sectoral examples from employment screening, credit scoring, healthcare risk prediction, biometric identification, and autonomous systems, the analysis demonstrates how technical bias mechanisms translate into systemic governance and accountability challenges. The findings reveal persistent regulatory gaps, including limited auditability of training datasets, the absence of mandatory fairness metrics, insufficient transparency regarding model behavior, and weak mechanisms for post-deployment monitoring and accountability. These results highlight a structural misalignment between lifecycle-based bias dynamics and the Act’s category-driven compliance framework. The paper argues that addressing systemic bias requires a governance approach that integrates technical bias mitigation with legal oversight across the full AI lifecycle rather than relying primarily on post hoc regulatory controls. Full article

The correct valuation of collateral supporting real estate loans has always been a key issue for the stability of the credit system. Substandard lending practices and the absence of uniform valuation approaches have historically contributed to the accumulation of non-performing loans. In recent years, several regulatory measures operating at both the European and national level have introduced principles, rules and procedures aimed at standardizing the valuation of properties pledged as collateral for credit exposures. These interventions seek to promote greater transparency, consistency, and prudence in property appraisals, thereby enhancing the soundness and resilience of the financial system. In January 2025, the updated Regulation (EU) 575/2013 came into force, incorporating the Basel III reform (also referred to as Basel 3+ or Basel IV). Among the innovations introduced, the concept of property value (PV) is particularly relevant, a prudential value that excludes expectations of price growth and considers the sustainability of the value over time in relation to the duration of the loan. PV is defined as a derived value with respect to market value (MV), determined by considering the main current and forward-looking risk factors that may arise during the life of the loan, including environmental, social and governance (ESG) risks, the intrinsic characteristics of the property and expectations regarding the economic cycle. This paper proposes a quantitative model for the determination of PV, applied to a practical case involving a residential property located in a medium-sized city in Italy’s Veneto region. The model adopts a deterministic and a probabilistic approach, the latter implemented through Monte Carlo simulation, which is indeed a generalization of the deterministic one. The model links the assessment of PV to the possible evolution of the property’s key parameters and the real estate cycle over the duration of the loan. It was tested under the assumption of a twenty-year mortgage originated in 2025 for the purchase of a residential property in Italy, considering two alternative locations: a suburban area and a city-centre area. The analysis conducted showed a substantially higher MV haircut for the suburban property compared with the central location. This difference reflects the fact that PV is less sensitive to real estate cycle fluctuations in more premium, central locations. Furthermore, the use of Monte Carlo simulation in the probabilistic approach enabled the calibration of the haircut according to a predefined confidence level, confirming the pattern observed in the deterministic framework. The combined evidence strengthens the empirical robustness of the model and highlights the importance of locational and cyclical dynamics in collateral valuation. Full article

In the context of the rapid advancement of financial technology, the issue of credit card default has become increasingly salient, emerging as one of the crucial risks that financial institutions are eagerly addressing. Traditional credit card default risk prediction models predominantly rely on statistical correlations for feature selection. This approach not only makes it challenging to uncover the genuine causal relationships between variables but also leads to limitations in prediction accuracy and interpretability. To overcome these limitations, this paper presents a novel credit card default risk prediction model that integrates causal feature screening, interaction feature construction, and interpretability enhancement. Initially, by leveraging the information value (IV) and eXtreme gradient boosting (XGBoost), we perform initial feature dimensionality reduction. Subsequently, we introduce the Peter Clark algorithm (PC) augmented with perturbation enhancement and bootstrap sampling to identify a stable set of causal features. Building on this foundation, we proceed to construct higher-order interaction features to bolster the model’s nonlinear modeling capacity. These causal features and their interaction counterparts are then fed into a variety of mainstream machine learning models for training and evaluation purposes. Furthermore, on the basis of the causal feature set identified via the PC algorithm, we construct a causal path diagram. We also incorporate the causal forest double machine learning (causal forest DML) method to estimate the causal effects of features. Additionally, we design a counterfactual explanation mechanism to aid in analyzing the direction and magnitude of the impact of variable interventions on default probability. Empirical tests conducted using four typical credit datasets reveal the following findings: (1) the introduction of causal features generally enhances the model’s performance in terms of the F1 score, area under the curve (AUC), and geometric mean (G-mean). This improvement is especially pronounced in models that are highly reliant on feature quality, such as logistic regression (LR). (2) Causal features offer significant advantages in terms of model interpretability, stability, and compliance, thereby presenting a new research paradigm for credit risk prevention and control in high-risk financial scenarios. Full article