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.
A novel hybrid model incorporating a network-characterized multidimensional financial distress indicator system to forecast corporate financial distress dynamically is constructed, validating the significance of systemic risk data in forecasting financial instability.
Hao-Zhi Chen, Bing Mo, Yuan Zhao et al.· Journal of Intelligent &...· 0 citations
A robust set of predictors of financial distress and fraud based on transformer-based designs of the board network data are provided and 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 acti...
Muhammad Usman Malik, Maarij Ahmed Siddiqui, Sohail Naseer 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.
T. Le Nam, Tam Phan Huy· Journal of International Com...· 0 citations
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 is constructed.
Regression results reveal that profitability and ESG score significantly reduce credit risk and liquidity risk and monetary freedom increase credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.
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–sensit...
Shi-Liang Chang· ICST Transactions on Scalabl...· 0 citations
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