Real-Time Multi-Risk Early Warning for Community Banks: An Application of Ensemble Anomaly Detection and Explainable Artificial Intelligence

Abstract

This paper presents an integrated framework for real-time multi-risk early warning specifically designed for community banks and small financial institutions. The proposed approach combines ensemble anomaly detection techniques with explainable artificial intelligence to simultaneously monitor market risk, credit risk, and liquidity risk. By leveraging unsupervised learning algorithms including Isolation Forest, autoencoders, and Local Outlier Factor, the framework achieves superior detection performance compared to traditional siloed risk management approaches. Implementation using open-source technologies demonstrates cost-effectiveness and scalability suitable for resource-constrained institutions. Experimental validation shows 85% recall rate for VaR breach prediction with 15% false positive rate, 3-6 month early warning for counterparty defaults, and robust liquidity stress detection capabilities. The framework's SHAP-based explainability layer ensures regulatory compliance while providing actionable insights for risk mitigation.