Jul 2026· SN Business & Economics· Vol 6· 0 citations· 38 references
TL;DR
This paper presents a unique framework called “Enhancing Corporate Financial Risk Forecasting under Reduced Predictive Reliability Using Residual Knowledge Single-Head and Dandelion Vision Graph Neural Network (RKSH-DVGNN)”, which achieves an accuracy of 98.70% in predicting corporate financial risks and demonstrates competitive performance compared with existing approaches.
As interconnection across sectors and institutions within the financial system increases, the insolvency of corporations generally leads to negative repercussions for the financial health of related firms. This research aims to construct a novel hybrid model incorporating a network-characterized multidimensional financial distress indicator system to forecast corporate financial distress dynamically. Firstly, a volatility spillover network is constructed, and network-based features are extracted using the DCC-GARCH model. Subsequently, experiments are conducted using the hybrid AdaPSO-RF model, which integrates Random Forest, AdaBoost, and Particle Swarm Optimization (PSO) for parameter optimization. The empirical findings indicate that the AdaPSO-RF model exhibits enhanced predictive performance when applied to the hybrid feature dataset, surpassing both baseline and comparable models. The incorporation of network-based attributes results in consistent performance improvements, validating the significance of systemic risk data in forecasting financial instability. The integration of volatility spillover network characteristics into ensemble learning markedly enhances predictive accuracy and resilience, offering substantial assistance for financial risk assessment and decision-making.
Hao-Zhi Chen, Bing Mo, Yuan Zhao et al.· Journal of Intelligent &...· 0 citations
The crossroads of deep learning and corporate governance is a paradigm shift in financial risk analysis, which goes beyond the conventional ratio models and modifies the multifaceted relationship relationships of board organizations and corporate networks. In this article, the authors provide a robust set of predictors of financial distress and fraud based on transformer-based designs of the board network data. We introduce a new methodological framework that combines graph neural networks with attention mechanisms to model director interlocks, committee structures, and measures of governance quality as high-dimensional relational features. The framework employs advanced econometric methods such as difference-in-differences with continuous treatment, propensity score weighting with neural network propensity estimation, and panel VAR with impulse response functions to create a causal identification. Empirical evidence on a decade of board-level data shows that transformer models have better predictive accuracy than conventional methods and that area under the curve (AUC) gains are 12-18 points in predicting financial distress and 22-28 points in predicting fraud. The cognitive interpretability module establishes the board independence, audit committee expertise and the network centrality of directors as the most important determinants of firm resilience. These results indicate that the application of algorithmic governance based on the use of deep learning can improve transparency, reduce agency risks, and give regulators decision-support systems to conduct active risk monitoring.
Research Paper, Muhammad Usman, Malik et al.· The social science· 0 citations
Out-of-time evidence indicates that tree-ensemble methods provide the strongest combination of ranking performance and probability accuracy, with the random forest providing the best out-of-time performance among the evaluated models, with reasonable discrimination and the lowest probability error.
Tuyen Le Nam, Tam Phan Huy· Journal of International Com...· 0 citations
In the context of increasing global economic uncertainty, the financial risks of listed companies exhibit significant dynamic evolution characteristics. Traditional methods based on static indicators are no longer sufficient to meet the requirements of forward-looking early-warning. Considering the intertemporal transmission and path dependence characteristics of enterprise financial risks, from the perspective of deep temporal modeling, this study constructs a Gated Recurrent Unit model (Residual GRU, RGRU) that integrates a residual connection mechanism to enhance the feature transmission ability in the deep network and alleviate the problem of gradient propagation difficulties. Taking Chinese A-share listed companies from the first quarter of 2010 to the fourth quarter of 2024 as the research sample, and using the Special Treatment (ST) or Delisting Risk Warning (*ST) status as the distress determination criterion, an enterprise financial distress early-warning system is constructed based on multi-dimensional financial and corporate governance indicators. On this basis, ablation experiments are set up from two dimensions—the type of recurrent units (GRU and LSTM) and the connection mechanism (residual connection and dense connection)—and performance is evaluated through systematic parameter optimization and multi-layer network structure experiments. The empirical results show that the RGRU model outperforms the comparison model in classification indicators such as accuracy, F1 score, recall, and probability prediction indicators such as Brillouin score and logarithmic loss, and demonstrates better stability and generalizability. The research results indicate that the introduction of residual connections can effectively improve the prediction performance and practical value of recurrent neural networks in the enterprise financial distress early-warning task.
Predicting credit risk is vital for banks as it safeguards financial stability, minimizes default losses, optimizes capital, and ensures regulatory compliance. This study aims to predict credit risk (High/Low) in commercial banks by integrating machine learning with traditional econometric approaches. The Structural Learning in Vague Environments (SLAVE) fuzzy rule-based model handles ambiguity in financial decisions, while the eXtreme Gradient Boosting (XGBoost) uncovers non-linear patterns among predictors. Input variables—profitability, liquidity risk, ESG (environmental, social, and governance) score, and monetary freedom—were selected via multicollinearity tests and three panel regression models, including ordinary least squares (OLS), fixed effects, and random effects models. The empirical investigation uses a panel dataset of forty commercial banks across seven Middle Eastern countries from 2014 to 2023, yielding 400 observations. Regression results reveal that profitability and ESG score significantly reduce credit risk. Liquidity risk and monetary freedom increase credit risk. XGBoost combined with the SHapley Additive exPlanations (SHAP)-based interpretation identifies ESG Score as the most influential predictor. The SLAVE model was evaluated using three data splits: 70/30, 80/20, and 90/10. The 80/20 split achieved the highest accuracy, with superior performance in identifying low-risk banks. Stronger ESG performance and stable monetary environments contribute to fostering sustainable banking and reducing credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.
Jamil J. Jaber, A. A. Alkhawaldeh, Qusay Ayman Sulayman Mazahreh et al.· Risks· 0 citations
Financial risk assessment for manufacturing enterprises supports credit-risk screening and early warning. Existing methods often fuse multi-period financial and industry information through concatenation, shared representations, or unrestricted interactions, making it difficult to separate meaningful disturbance–sensitivity correspondences from irrelevant combinations. This study proposes an industry-disturbance-conditioned credit-scoring framework. Its originality lies in explicitly matching external disturbances with firm-level financial sensitivities rather than treating them as unrestricted features. The framework decomposes financial information into levels, intertemporal changes, and accounting divergences; constructs demand, cost, and production sensitivities; and estimates conditioned exposures through a correspondence matrix and conditional gates. A matching-consistency loss constrains disturbance–sensitivity relationships, while dual-path prediction retains exposure-related and firm-specific risk information. Using Moody’s Orbis and Eurostat Short-Term Business Statistics (STS), the method achieves an AUPRC of 0.512 in the full out-of-time test, exceeding TabPFN, the strongest AUPRC baseline, by 1.4 percentage points. Its AUROC is 0.879, and its FNR of 0.276 is lower than 0.289 for HGNN and 0.291 for TabPFN. Among highly sensitive firms, the proposed model achieves an AUPRC of 0.489 and an FNR of 0.288, improving on HGNN by 1.5 and 2.3 percentage points, respectively. Statistical tests, ablation studies, and repeated runs support the matching and prediction mechanisms. Independent calibration further reduces probability error and calibration bias. The framework supports relative risk ranking and distress screening during industry disturbances, although validation across additional databases and disturbance settings remains necessary.
Shi-Liang Chang· ICST Transactions on Scalabl...· 0 citations
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