Model Inventory Management

Model metadata, logs, and code scans automate inventory tracking and validation for risk management

Business impact

• Model validation cycle time — Shortens validation duration by automating documentation and monitoring tasks
• Operational cost of model risk management — Lowers costs by improving governance and reducing rework in validation processes
• Model risk exposure — Decreases risk by ensuring comprehensive inventory and continuous monitoring of models

Data requirements

• Model metadata and documentation (Structured) — Used to track model versions, validation status, and governance attributes
• Training and inference logs (Numeric) — Monitors model usage patterns and detects drift or anomalies over time
• Code repositories and deployment environments (Code) — Scans for model code, fine-tunes, and runtime configurations to maintain inventory

AI methods and techniques

• Agentic AI — Automates discovery, monitoring, and reporting of model inventory and risk factors
• Predictive AI — Forecasts model performance degradation and potential compliance risks proactively

AI models and model families

GPT-4o, Claude, Llama-3, Custom fine-tuned risk models