Explainable Machine Learning for Governance-Driven Enterprise Risk Management (ERM)

Overview

Traditional enterprise risk management systems often rely on fixed indicators and manual analysis. While these methods remain useful, they can be limited when risk patterns are temporal, non-linear, and distributed across heterogeneous enterprise data sources.

This work presents a proposed framework that combines explainable machine learning and governance-oriented risk interpretation to improve risk prediction quality and decision support.

Research Problem

Common limitations in traditional ERM pipelines include:

• ▸Static indicators with limited adaptability to changing conditions
• ▸Linear modeling assumptions for non-linear risk behavior
• ▸Manual analysis cycles that can delay decision response
• ▸Difficulty integrating heterogeneous financial, demographic, and behavioral data
• ▸Missing values and class imbalance in real-world risk datasets
• ▸Limited explainability in some ML-driven risk scoring approaches

Proposed Framework

The proposed Botfip-LLM + ESCO + DRQL framework includes:

• ▸Botfip-LLM for aligning heterogeneous financial and behavioral data representations
• ▸ESCO (Enhanced Swarm Coyote Optimization) for selecting relevant predictive features
• ▸DRQL (Deep Recurrent Q-Learning) for temporal risk prediction with sequential dependencies
• ▸Explainable outputs in the form of interpretable risk scores and early warning indicators

Dataset and Preprocessing

The study references a Financial Risk Assessment dataset with 10,000+ records, 20+ features, and demographic, income/expense, credit, and behavioral variables. Preprocessing includes missing-value handling, class-balancing with SMOTE, feature preparation, and an 80/20 train-test split.

Results

Governance Value

Explainable risk scores can support governance-oriented decision-making across lending and credit-risk review workflows, investment and portfolio risk monitoring, FinTech risk controls and auditability, and enterprise risk monitoring programs.

Conclusion

This paper contributes a proposed explainable AI architecture for governance-driven ERM that combines heterogeneous data alignment, optimized feature selection, and temporal prediction under a unified framework. It provides a representative basis for further enterprise-scale validation and calibration.