Hybrid Explainable AI Framework for Predictive Operational Risk Management in Financial Services

Abstract

The digital transformation of financial services has increased organizational dependence on complex information systems, automated processes, third-party service providers, algorithmic decision systems, and interconnected digital infrastructures. Although these developments improve efficiency and customer service, they simultaneously amplify operational exposures arising from process failures, internal and external fraud, cyber incidents, system disruptions, human error, regulatory non-compliance, and third-party dependencies. Traditional operational risk management approaches remain predominantly retrospective and frequently depend on historical loss databases, risk and control self-assessments, key risk indicators, and expert judgment. Such mechanisms are valuable for governance but are often insufficient for predicting rapidly evolving and low-frequency operational risk events. This study proposes a Hybrid Explainable Artificial Intelligence Framework for Predictive Operational Risk Management (HXAI-ORM) that integrates supervised machine learning, anomaly detection, interpretable statistical modelling, explainable artificial intelligence, and human-in-the-loop governance. The framework combines logistic regression, Random Forest, XGBoost, and Isolation Forest outputs to produce probability-, severity-, and anomaly-adjusted operational risk scores. SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), rule-based explanations, and counterfactual reasoning are incorporated to translate predictive outputs into auditable risk drivers and actionable control recommendations. The framework is aligned with the Basel Committee’s principles for operational risk and operational resilience and with the NIST Artificial Intelligence Risk Management Framework. Unlike conventional black-box prediction architectures, the proposed framework explicitly incorporates explanation fidelity, probability calibration, model stability, human review, model-risk controls, and continuous monitoring. The study contributes to the literature by repositioning explainability from a supplementary visualization mechanism to an integral component of predictive operational risk governance. A structured empirical validation protocol is also developed for subsequent implementation with financial-institution data. The framework offers financial institutions a practical foundation for moving from retrospective loss reporting toward anticipatory, transparent, and governance-compatible operational risk management.