Financial services institutions have operated formal model risk management programs for decades, built around independent validation, ongoing performance monitoring, and clear accountability for models that influence lending, trading, and risk decisions. As generative and predictive AI spreads into functions like hiring, customer service, and operational decision-making across every industry, organizations without that regulatory history are discovering they need similar discipline, just without a regulator mandating it yet.
Borrowing structure without the full regulatory overhead
The core model risk management practices translate well beyond finance: an inventory of every model in production and what decisions it influences, independent validation before deployment rather than only the development team’s own testing, ongoing monitoring for performance drift as real-world data diverges from training data, and clear ownership for any model’s outcomes rather than treating the model as an autonomous black box no one is accountable for.
The practical starting point is usually a model inventory, since organizations are frequently surprised to discover how many AI-influenced decisions are already running in production without anyone having formally assessed their risk.
JIG helps enterprises build AI governance programs that adapt proven model risk management discipline for their specific regulatory and operational context.