Model Governance and Feature Store Design for Intelligent Risk Scoring Systems: A Comprehensive Framework

Abstract

Organizations implementing machine learning in regulated environments face critical challenges in maintaining transparency, explainability, and compliance as automated decision-making proliferates across financial services, healthcare, and retail sectors. This paper presents a comprehensive framework addressing these challenges through three integrated components: a unified metadata system capturing complete decision context, a scalable feature store architecture supporting dual-mode access patterns, and transparent risk scoring mechanisms generating human-interpretable explanations. The proposed architecture enables intelligent risk scoring systems that balance high performance with regulatory compliance through versioned feature repositories, structured lifecycle management, and continuous learning capabilities. Novel contributions include: (1) unified metadata architecture enabling sub-second lineage queries through graph-based navigation, (2) dual-mode feature store eliminating train-serve skew via synchronized batch and streaming interfaces, and (3) interpretable risk scoring combining SHAP-based attribution with automated explanation generation for regulatory compliance. Implementation across three financial institutions demonstrates measurable improvements in decision traceability, model stability, and operational efficiency while preserving the agility essential for effective machine learning deployments in regulated domains.