Search Results (136)

Enterprise bankruptcy prediction is a critical research issue in financial risk early warning, credit evaluation, and investment decision-making. To address the limitations of traditional methods in handling high-dimensional, nonlinear, and complex financial data, including parameter sensitivity, susceptibility to local optima, and insufficient prediction stability, this study proposes a multi-strategy enhanced Beaver Behavior Optimizer and applies it to optimize kernel extreme learning machines, constructing the MEBBO KELM prediction model. Three improvement mechanisms are introduced, including an elite pool enhanced exploration strategy, a stochastic centroid reverse learning strategy, and a leader guided boundary control strategy, which improve population diversity, global search capability, boundary handling capacity, and convergence accuracy. The proposed algorithm is evaluated on CEC2017 and CEC2022 benchmark datasets and compared with EWOA, HPHHO, MELGWO, TACPSO, CFOA, ALA, AOO, RIME, and BBO. Statistical analyses are conducted using the Wilcoxon rank sum test and the Friedman test. The results demonstrate that MEBBO achieves superior solution accuracy and stability, indicating strong global optimization capability and robustness. Further experiments on the Wieslaw Corporate Bankruptcy Dataset show that MEBBO-KELM achieves strong and robust performance across multiple evaluation metrics, including ACC, MCC, Sensitivity, Specificity, Precision, Recall, and F1 score. Specifically, ACC reaches 79.7578, MCC reaches 0.6050, and F1 score reaches 78.8504, confirming its effectiveness. Full article

Reform and sustainability of the defined benefit pension system has received considerable attention because it addresses the challenges of an aging population and the risk of fund insolvency. However, previous studies have little consideration to the effects of reemployed retired laborers and the sensitivity of each key reform element. The study established a financial forecasting model incorporating reemployed retired laborers and employed comparative static analysis to examine the effects of several variables on Taiwan’s public labor pension. In Scenario Three, the fund balance was projected to remain positive until 2064. Furthermore, increasing the premium rate to 17% had the strongest positive effect on the fund balance with 16-year delay, while an annual government subsidy of NT$100 billion had the second-most positive effect with 10-year delay. Moreover, solely reducing the old-age annuity amount by 10% had a positive impact on the fund balance with 7-year delay. Furthermore, allowing 50% of reemployed retired laborers to reenroll in the system had the positive effect with 9-year delay before bankruptcy. Finally, the study proposes a comprehensive reform plan for the public labor pension and offers valuable insights for other countries. Full article

This study addresses the gap between predictive optimization and monitoring-oriented risk concentration by introducing a hierarchical Fuzzy Cognitive Map (FCM) framework for financial risk assessment. Financial distress prediction models are employed to estimate firm-level default probabilities and are required to comply with regulatory standards. IFRS 9 and Basel III/IV frameworks emphasize model explainability, scenario analysis and causal transparency, which are essential for compliance purposes. The methodology aggregates correlated financial ratios into financial concepts through unsupervised clustering. Concepts interact through a learned coupling matrix and a controlled multi-step propagation, which enables the amplification of risk signals. A small residual correction is applied at the final readout, preserving the interpretability of the proposed framework. The framework was applied to two severely imbalanced benchmark bankruptcy datasets. It achieved higher precision–recall performance than Logistic Regression (PR–AUC $\approx 0.32$ vs. $0.27$), improved calibration (Brier score $\approx 0.046$ vs. $0.089$) and maintained competitive Recall@Top–K under tight supervisory monitoring budgets. Hierarchical FCM achieved predictive performance comparable to nonlinear models while maintaining concept-level interpretability. Our findings demonstrate that structured concept aggregation combined with interaction-based propagation provides a transparent alternative to purely predictive black-box models in financial distress assessment and is aligned with regulatory frameworks. Full article

This paper proposes a continuous-time dynamic clearing model on a multilayer financial network to study systemic risk propagation and optimal intervention. The model incorporates interbank credit, equity crossholdings, and overlapping portfolios, and models bankruptcy as a jump event triggered by insolvency or illiquidity. Based on the system’s dynamic structure, we develop a model predictive control (MPC) framework that enables forward-looking and flexible allocation of limited bailout resources between debt relief and capital injection. Numerical results show that the proposed MPC strategy substantially outperforms both no-intervention and rule-based policies in terms of financial stability and resource efficiency. Compared with no intervention, the MPC strategy reduces the number of defaulting banks by approximately 56%. In contrast, the simple rule-based intervention achieves a reduction of about 48.83%, while improving rescue efficiency by approximately 28.57%. Overall, the framework provides a unified and effective approach to systemic risk control in financial networks. Full article

Corporate management and their auditors are required to evaluate whether there is a risk that the company’s ability to continue as a going concern is impaired. For fixed asset-intensive firms, however, regulatory inspections consistently identify problems with auditors’ testing of property, plant, and equipment (PPE), raising doubts about whether auditors understand the risks associated with these assets. This paper examines whether auditors incorporate the risks associated with PPE into their going concern evaluation and the accuracy of that evaluation. Using probit regression on financial and auditing data of U.S. public firms contained in S&P Global Compustat North America, Audit Analytics, and the Center for Research in Security Prices (CRSP) from 2000 to 2019, this paper examines the effects of PPE intensity, impairment, and age on the likelihood that an auditor issues a going concern modification. We test the accuracy of the auditor’s going concern evaluation by comparing it to the client’s subsequent viability or bankruptcy. Our results find that PPE intensity and PPE impairments are positively associated with the likelihood of an auditor issuing a going concern modification, indicating that auditors view PPE as contributing to substantial doubt about the entity’s ability to continue as a going concern. We do not find a significant association between PPE age and going concern modification. Additionally, the going concern evaluation is more accurate for firms with higher PPE intensity. These findings imply that auditors appropriately consider PPE assets in their going concern evaluations. Full article

Financial distress prediction models are widely used to support risk management. However, economic turbulence, such as the COVID-19 pandemic, can disrupt the relationships between financial indicators and distress, thus threatening the stability and accuracy of the models’ predictions. In this study, the stability of bankruptcy prediction models is examined on a large sample of small and medium-sized enterprises (SMEs) in Slovakia. Three periods are distinguished: the pre-pandemic years 2018–2019, the COVID-19 pandemic years 2020–2021, and the post-pandemic recovery years 2022–2023. Two approaches to model construction are compared: separate models are estimated for each period, and a single comprehensive model covering all three periods is constructed with a period-specific indicator among the predictors. Publicly available financial data and machine learning methods are employed, and model performance is evaluated using common classification metrics. Differences in performance are revealed, indicating whether period-specific models provide superior predictive accuracy or whether a universal model can adapt to changing economic conditions. The robustness, stability, predictive power, and practical applicability of both approaches are assessed, and the influence of economic fluctuations on accuracy is demonstrated. The findings provide guidance on selecting modelling strategies across different economic environments and offer recommendations for further developing and implementing predictive models in volatile financial conditions. Full article

This study examines how environmental, social, and governance (ESG) performance affects firms’ bankruptcy risk and explores the mechanisms linking ESG engagement to financial stability. Using a panel dataset of Chinese-listed firms from 2009 to 2022, we employ multivariate regression analyses, instrumental variable estimation, and robustness tests to address potential endogeneity. The results indicate that higher ESG performance significantly reduces bankruptcy risk. Mechanism analyses reveal that ESG engagement lowers bankruptcy risk by improving information transparency, alleviating financing constraints, enhancing operating performance, and reducing leverage. The effect is more pronounced for non-state-owned enterprises, firms in economically developed regions, highly competitive industries, and those in the growth and maturity stages. Among the three ESG pillars, corporate governance exerts the strongest influence on mitigating bankruptcy risk. These findings provide new evidence from an emerging market and offer important implications for sustainable corporate finance and risk management. Full article

Climate change observed in recent decades has, in most cases, negatively impacted on the operations of non-financial and agricultural enterprises. Filling a gap in the economic literature, this article presents the results of a study on the impact of rising temperature on the resilience to bankruptcy risk of over four thousand agricultural enterprises operating in Poland between 2016 and 2023, taking into account temperature and macroeconomic conditions of regions of their operation and assessing resilience with Altman (Z-score) and Zmijewski (X-score) methods. Using panel regression, it was demonstrated that temperature changes have a significant nonlinear (parabolic) effect on enterprise resilience. An increase in annual average temperatures above the long-term average weakens enterprise resilience. A generally similar, although individually variable relationship occurs for changes in average temperatures in spring, autumn, and winter. In the summer, this relationship is ambiguous. Furthermore, the resilience to bankruptcy risk improves growth in regional GDP and agricultural production, as well as enterprise’s assets, profitability and the share of equity in the financing structure. The conclusions can be used by agricultural enterprises in preparing contingency plans in the event of potential temperature shocks, and public administration for developing programs to protect agriculture against temperature shocks and food security plans. Full article

Bankruptcy prediction in Slovakia’s industrial manufacturing sector is vital due to its significant role in the national economy. This study aims to develop a predictive model for forecasting corporate bankruptcy within the industrial manufacturing sector in Slovakia. The novelty of this study lies in developing a model tailored to crisis conditions, validated using COVID-19 data, and adapted to the Central European context for greater accuracy and relevance. The model is constructed using financial data extracted from the Orbis database, based on company financial statements from 2020 and 2021, and encompasses firms of various sizes. Employing backwards binary logistic regression, five statistically significant predictors were identified, enabling the model to forecast impending bankruptcy with a one-year lead time. The model was trained on a sample of 1305 companies and achieves an overall prediction accuracy of 83.78%, with an AUC (Area Under the Curve) value of 91.7%, indicating strong discriminative power. The resulting model demonstrates robust predictive capability and may serve as a practical decision-support tool for managers, investors, creditors, and other stakeholders assessing the financial health of firms. Full article

Corporate defaults represent a critical risk factor for financial institutions and stakeholders. In today’s complex economic environment, precise and timely risk assessment has become an essential component of financial strategies. One promising strategy consists of analyzing and learning from financial patterns observed in distressed or bankrupt firms. However, this requires processing highly imbalanced datasets in which bankruptcy cases are substantially underrepresented relative to solvent firms. This imbalance, coupled with the data’s intrinsic complexity—such as overlapping features and nonlinear patterns, poses significant difficulties for traditional classifiers like Support Vector Machines (SVMs), which tend to favor the majority class. To overcome these challenges, we employ a Fuzzy Shadowed SVM, which allows for a more refined modeling of minority class instances. This method leverages granular computing paradigms to enhance predictive robustness. Empirical results based on real-world datasets show that our model significantly outperforms traditional machine learning approaches, particularly in recognizing minority-class instances. Full article

Bankruptcy prediction is critical for financial risk management. This study demonstrates that machine learning models, particularly Random Forest, can substantially improve prediction accuracy compared to traditional approaches. Using data from 8262 U.S. firms (1999–2018), we evaluate Logistic Regression, SVM, Random Forest, ANN, and RNN in combination with robust data preprocessing steps. Random Forest achieved the highest prediction accuracy (~95%), far surpassing Logistic Regression (~57%). Key preprocessing steps included feature engineering of financial ratios, feature selection, class balancing using SMOTE, and scaling. The findings highlight that ensemble and deep learning models—particularly Random Forest and ANN—offer strong predictive performance, suggesting their suitability for early-warning financial distress systems. Full article

Enterprise bankruptcy prediction is a critical task in financial risk management. Traditional methods, such as logistic regression and decision trees, rely heavily on structured financial data, which limits their ability to capture complex relational networks and unstructured industry information. Heterogeneous graph neural networks (HGNNs) offer a solution by modeling multiple relationships between enterprises. However, current models struggle with financial risk graph data challenges, such as the oversimplification of internal financial features and the lack of dynamic imputation for missing external topological features. To address these issues, we propose HGNN-EBP, an enterprise bankruptcy prediction algorithm that integrates both internal and external features. The model constructs a multi-relational heterogeneous graph that combines structured financial data, unstructured textual information, and real-time industry data. A multi-scale graph convolution network captures diverse relationships, while a Transformer-based self-attention mechanism dynamically imputes missing external topological features. Finally, a multi-layer perceptron (MLP) predicts bankruptcy probability. Experimental results on a dataset of 32,459 Chinese enterprises demonstrate that HGNN-EBP outperforms traditional models, especially in handling relational diversity, missing features, and dynamic financial risk data. Full article

The construction sector is particularly prone to financial instability, with insolvencies occurring more frequently among micro- and small-scale firms. The current study explores the application of artificial intelligence (AI) and machine learning (ML) models for predicting insolvency within this sector. The research combined a structured literature review with empirical analysis of construction sector-level insolvency data spanning the recent decade. A critical review of studies highlighted a clear shift from traditional statistical methods to AI/ML-driven approaches, with ensemble learning, neural networks, and hybrid learning models demonstrating superior predictive accuracy and robustness. While current predictive models mostly rely on financial ratio-based inputs, this research complements this foundation by introducing additional sector-specific variables. Empirical analysis reveals persistent patterns of distress, with micro- and small-sized construction businesses accounting for approximately 92% to 96% of insolvency cases each year in the Australian construction sector. Key risk signals such as firm size, cash flow risks, governance breaches and capital adequacy issues were translated into practical features that may enhance the predictive sensitivity of the existing models. The study also emphasises the need for digital self-assessment tools to support micro- and small-scale contractors in evaluating their financial health. By transforming predictive insights into accessible, real-time evaluations, such tools can facilitate early interventions and reduce the risk of insolvency among vulnerable construction firms. The current study combines insights from the review of AI/ML insolvency prediction models with sector-specific feature derivation, potentially providing a foundation for future research and practical adaptation in the construction context. Full article

The application of contagion risk spread modeling within the banking sector is a relatively recent development, emerging as a response to the persistent threat of liquidity risk that has affected financial institutions globally. Liquidity risk is recognized as one of the most destructive financial threats to banks, capable of causing severe and irreparable damage if overlooked or underestimated. This study aims to identify the most effective control strategy for managing financial contagion using a Susceptible–Infected–Recovered (SIR) epidemic model, incorporating time delays in both state and control variables. The proposed strategy seeks to maximize the number of resilient (vulnerable) banks while minimizing the number of infected institutions at risk of bankruptcy. Our goal is to formulate intervention policies that can curtail the propagation of financial contagion and mitigate associated systemic risks. Our model remains a simplification of reality. It does not account for strategic interactions between banks (e.g., panic reactions, network coordination), nor for adaptive regulatory mechanisms. The integration of these aspects will be the subject of future work. We establish the existence of an optimal control strategy and apply Pontryagin’s Maximum Principle to characterize and analyze the control dynamics. To numerically solve the control system, we employ a discretization approach based on forward and backward finite difference approximations. Despite the model’s simplifications, it captures key dynamics relevant to major European banks. Simulations performed using Python 3.12 yield significant results across three distinct scenarios. Notably, in the most severe case (${\alpha }_3=1.0$), the optimal control strategy reduces bankruptcies from 25% to nearly 0% in Spain, and from 12.5% to 0% in France and Germany, demonstrating the effectiveness of timely intervention in containing financial contagion. Full article

Modern financial practices introduce complex risks, which in turn force financial institutions to rely increasingly on computational risk analytics (CRA). The purpose of our research is to attempt to systematically explore the evolution and intellectual structure of CRA in banking using a detailed bibliometric analysis of the literature sourced from Web of Science from 2000 to 2025. A comprehensive search in the Web of Science (WoS) Core Collection yielded 1083 peer-reviewed publications, which we analyzed using analytical tools like VOSviewer 1.6.20 and Bibliometrix (Biblioshiny 5.0) so as to examine the dataset and uncover bibliometric characteristics like citation patterns, keyword occurrences, and thematic clustering. Our initial analysis results uncover the presence of key research clusters focusing on bankruptcy prediction, AI integration in financial services, and advanced deep learning applications. Furthermore, our findings note a transition of CRA from an emerging to an expanding domain, especially after 2019, with terms like machine learning (ML), artificial intelligence (AI), and deep learning (DL) being identified as prominent keywords and a recent shift towards blockchain, explainability, and financial stability being present. We believe that this study tries to address the need for an updated mapping of CRA, providing valuable insights for future academic inquiry and practical financial risk management applications. Full article