From credit scoring and loss forecasting to regulatory reporting and asset valuation, model risk management (MRM) is essential for regulatory compliance, trustworthy decisions, and risk mitigation. However, outdated legacy risk and governance systems are ineffective.
Legacy MRM systems lack full AI lifecycle governance and rely on manual processes and excessive documentation, which makes them costly and cumbersome. They cannot govern new ‘black box’ technologies like GenAI, which makes it difficult to explain decisions to regulators. As more models are used, dependencies increase, allowing small inaccuracies to harm downstream decisions. Often, legacy systems must be re-built to comply with new regulations (e.g., Federal Reserve SR 11-7, SEC Rule on AIML, etc.). That is why modern financial institutions need a modern approach to MRM.