Explainable Machine Learning for Governance-Driven Enterprise Risk Management
Presentation on Botfip-LLM, ESCO, and DRQL for explainable enterprise risk prediction and governance-driven ERM.
This presentation covers the Botfip-LLM + ESCO + DRQL framework for explainable enterprise risk management, combining heterogeneous data alignment, optimized feature selection, and temporal prediction under a unified governance-oriented architecture.
The framework addresses limitations in traditional ERM systems — static indicators, linear modeling assumptions, and limited explainability — by applying deep recurrent reinforcement learning with swarm-optimized feature selection.
Experimental results on a 10,000+ record Financial Risk Assessment dataset showed accuracy of 0.941, AUC-ROC of 0.945, and early detection rate of 0.902, outperforming selected baselines across all governance-relevant metrics.
The explainable risk scores are designed to support auditable decision workflows in lending, investment risk, FinTech controls, and enterprise-wide risk monitoring programs.