Optimization of Financial Risk Assessment Decision Framework

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.