Explainable Artificial Intelligence for Operational Risk Prediction and Decision Optimization: A Hybrid Deep Learning Framework

Abstract

With the increasing complexity and uncertainty of modern business operations, accurate operational risk prediction and efficient decision-making have become critical for improving organizational resilience and operational performance. Traditional risk management approaches often rely on statistical models or expert experience, which may have limited capabilities in capturing nonlinear relationships, dynamic interactions, and high-dimensional patterns in operational data. This study proposes an explainable artificial intelligence framework for operational risk prediction and decision optimization based on a hybrid deep learning architecture. The framework integrates multiple deep learning models to extract complementary temporal, nonlinear, and feature-level representations from heterogeneous operational data, while incorporating explainable artificial intelligence techniques to identify the key factors contributing to risk predictions. On this basis, the predicted risk information is incorporated into a decision optimization module to support adaptive resource allocation and risk mitigation decisions. The proposed framework is evaluated using operational risk indicators and comparative experiments with conventional machine learning and individual deep learning models. The results are expected to demonstrate improvements in prediction accuracy, robustness, and decision-making efficiency while maintaining greater interpretability. The study provides a practical analytical framework for integrating risk prediction, explainable artificial intelligence, and operational decision optimization, offering methodological support for intelligent and resilient operational management.