Explainable AI-Driven Predictive Risk Management Framework for Enterprise GRC Platforms

Abstract Governance, Risk and Compliance (GRC) platforms are important in ensuring that there is compliance with regulations and proper management of risks in the modern enterprises. Nonetheless, the conventional GRC systems are essentially reactive and are based on manual and rule bound processes, thus being inefficient in managing the complex and large scale enterprise environments. The paper suggests an Explainable Artificial Intelligence (XAI)-based predictive risk management model to enterprise GRC systems. The suggested solution will apply an Adaptive Dynamic Generative Residual Graph Convolutional Neural Network (ARGNN) to carry out precise risk prediction by acquiring the intricate connections among enterprise data. Shapley Additive Explanations (SHAP) is incorporated to improve transparency and trust to learn how to interpret model predictions and understand the key risk factors that affect decision-making. The framework facilitates real-time monitoring of risks, automated compliance evaluation, and enhancements in the governance procedures. Enterprise risk datasets are used to evaluate the experiment using accuracy, precision, recall, and F1-score. Findings indicate that the suggested model performs better than traditional methods in prediction accuracy as well as effectiveness besides giving interpretable results. All in all, the suggested system will turn traditional GRC platforms into active and intelligent solutions that enhance resilience in organizations, their efficiency in compliance and decision making.