Model Risk

AI & IRC: Smarter Risk Management in Finance The financial sector faces mounting pressure to accurately measure and manage risk. One of the most complex requirements is the Incremental Risk Charge (IRC), a regulatory capital buffer designed to capture model risk and potential losses from inaccuracies in banks’ internal models. Calculating IRC is data-intensive, computationally demanding, and subject to regulatory scrutiny. The Problem: Complex, Costly IRC Calculations IRC calculations require vast historical data, robust model validation, and scenario analysis. Manual processes are slow, error-prone, and resource-intensive. Banks must compare internal model outputs with standardized approaches, quantify discrepancies, and justify their models to regulators. ...

Banks have been using machine learning models for years — credit scoring, fraud detection, anti-money laundering. But generative AI changed the conversation. Regulators who were comfortable with traditional ML models are not comfortable with large language models that cannot explain their decisions. AI governance is the bridge between innovation and compliance. Without it, banks either ban AI (losing competitive advantage) or deploy AI uncontrolled (risking regulatory action). With it, banks can deploy AI in production while satisfying regulators that the models are fair, explainable, and auditable. ...