One of the most heavily regulated domains for AI
Finance is one of the most heavily regulated environments in which AI is deployed, and for good reason: decisions made by these models affect people's access to credit, their savings, and the stability of markets that touch the broader economy. Financial regulators in many jurisdictions have long required firms to practice model risk management, a formal discipline covering how models are developed, validated, monitored, and eventually retired, originally built for traditional statistical models and now extended to cover AI systems as well.
Model risk management typically requires documenting what a model does, testing it before deployment, monitoring its performance over time in production, and having a plan for what happens when a model starts to behave unexpectedly or its performance degrades. None of this is optional paperwork, regulators can and do examine these practices directly, and gaps in model governance are treated as a real compliance risk, not just an engineering concern.