What is Model Risk Management?
Framework for identifying, measuring, and controlling risks from ML model development and deployment, ensuring models meet performance and regulatory standards
Summary
- Encompasses development risk (data quality, feature selection, algorithm choice), deployment risk (performance degradation, concept drift), and operational risk (unintended consequences, fairness issues) requiring continuous validation and testing
- Implements governance frameworks including model inventory, version control, performance benchmarking, bias testing, explainability requirements, and compliance documentation aligned with regulatory standards like SR 11-7 and AI regulations
- Monitors deployed models through key performance indicators, data drift detection, prediction confidence tracking, and A/B testing, triggering retraining or retirement when performance degrades below acceptable thresholds
Model risk management refers to the supervision of risks from the potential adverse consequences of decisions based on incorrect or misused models. The aim of model risk management is to employ techniques and practices that will identify, measure and mitigate model risks i.e. the possibility of model error or wrongful model usage. In financial services, model risk is the risk of loss resulting from using insufficiently accurate models to make decisions, frequently in the context of valuing financial securities, and becoming prevalent in activities such as assigning consumer credit scores, real-time probability prediction of fraudulent credit card transactions, and money-laundering. Financial institutions are highly reliant on credit, market, and behavioral models for model risk has become a core component of risk management and operational efficiency. These institutions primarily make money by taking risks - they maximize models to evaluate risks, understand customer behavior, assess capital adequacy for compliance, make investment decisions, and manage data analytics. Implementing an effective model risk management framework is a requisite for organizations that are heavily reliant on quantitative models for operations and decision-making.
