Banking risk, FRTB, actuarial science and compliance
Risk Governance in the Age of Artificial Intelligence
Artificial intelligence changes the speed and scale of decisions, but it does not remove accountability. Effective governance begins with the decision being supported, the evidence required and the consequence of error.
Start with the decision
A model used to summarise an internal document does not create the same exposure as a system used for pricing, customer acceptance, sanctions screening or capital allocation. Governance should therefore classify use cases by customer impact, financial materiality, degree of automation, data sensitivity and reversibility.
Build an evidence chain
Every material system needs a traceable chain from data and assumptions to output, review and final decision. Validation should examine accuracy, stability, limitations, bias, security and performance under realistic adverse cases. Human oversight is meaningful only when the reviewer has expertise, time, evidence and authority to reject the result.
Monitor outcomes
Approval is not the end of governance. Organisations should monitor performance, overrides, incidents, data drift and customer outcomes. Material changes in the model, provider, data or process should trigger reassessment. A safe fallback is essential when performance becomes unacceptable.