Risk Register Automation using Stacked Generalization

This paper proposes a hybrid ML/DL approach for automating compliance risk register generation. A stacked generalization model is built using Random Forest, Logistic Regression, and MLP as base learners, combined via a meta-learner to improve risk classification. The system is trained on synthetic compliance data and utilizes encoding and normalization for preprocessing. Integrated with a Streamlit dashboard, the model provides real-time risk prediction with confidence scores, enabling efficient and accurate compliance risk management. Experimental results demonstrate superior performance compared to individual models, achieving 94.2% accuracy in risk categorization with reduced false positive rates. The system incorporates automated risk scoring mechanisms that adapt to evolving compliance landscapes, while maintaining interpretability through feature importance analysis and uncertainty quantification. Additionally, the framework supports multi-jurisdictional compliance requirements and provides automated reporting capabilities for audit trails and regulatory documentation. The ensemble approach leverages the complementary strengths of traditional machine learning and deep learning techniques to enhance prediction robustness across diverse risk scenarios.