AI-Driven Financial Risk Forecasting: A Multi-Model Ensemble Approach for Enterprise Decision Intelligence

Abstract

Financial institutions increasingly rely on AI to improve risk forecasting and enterprise decision intelligence; however, many deployed solutions remain constrained by static single-model pipelines, limited robustness under class imbalance, and insufficient governance for explainability and regulatory scrutiny. This study proposes the Enterprise Decision Intelligence–Multi-Ensemble Risk Forecasting Framework (EDI-MERF™), a structured, enterprise-grade architecture that integrates heterogeneous financial data pipelines, imbalance-aware preprocessing, diverse base learners, and meta-ensemble optimization using stacking and related strategies. The framework is formally specified through a mathematical ensemble formulation in which base learner outputs are combined via a meta-learner and optimized under regularized loss to control complexity and improve generalization. To strengthen operational validity, the study defines an evaluation protocol using imbalance-sensitive metrics (Precision, Recall, F1), discrimination measures (ROC-AUC), and separation statistics (KS), complemented by ablation analysis and robustness testing under temporal shift and perturbation. In addition to predictive performance, EDI-MERF™ incorporates cross-cutting controls for explainability (e.g., SHAP-style reason codes), audit logging, drift monitoring (e.g., PSI-based indicators), and governance documentation, enabling traceability suitable for regulated enterprise contexts. The results support the conclusion that multi-model integration improves stability and decision utility relative to single-model baselines while providing a practical pathway for deployment via APIs, dashboards, and ERP integration. The paper contributes a reproducible, enterprise-oriented blueprint for AI-driven financial risk forecasting that balances accuracy, interpretability, and compliance readiness.