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Optimization of Financial Risk Assessment Decision Framework

Tingting Li et al.

Journal of Organizational and End User Computing2026https://doi.org/10.4018/joeuc.401693article
AJG 1ABDC B
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0.50

Abstract

Small and micro enterprises (SMEs) play a critical role in economic development, yet their access to credit is often constrained by inadequate risk assessment frameworks. Traditional credit scoring models struggle to capture the non-linearity, feature sparsity, and class imbalance inherent in SME financial data. To address these challenges, the authors propose HECRO (Heterogeneous Ensemble Credit Risk Optimizer), a multi-layered framework that integrates kernel-based and heuristic feature selection, ensemble base learners, and a Bayesian-optimized meta-stacking classifier. HECRO leverages KFS-MCLOC and BOWOA-KS for robust feature extraction, followed by GA-BPNN, SMOTE-XGBoost, Wide & Deep, and BD-LR models as diverse predictors, culminating in a BO-XGBoost meta-learner. SHAP-based interpretation enhances post-hoc transparency. These results demonstrate HECRO's superiority in both predictive accuracy and robustness. The study offers a practical and scalable solution for SME credit evaluation, providing new insights into the design of intelligent financial risk assessment systems.

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https://doi.org/https://doi.org/10.4018/joeuc.401693

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@article{tingting2026,
  title        = {{Optimization of Financial Risk Assessment Decision Framework}},
  author       = {Tingting Li et al.},
  journal      = {Journal of Organizational and End User Computing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/joeuc.401693},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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