AI Governance for Banking & Insurance

Problem & risk

UK banks and insurers are deploying co-pilots and autonomous agents against core ledgers, payment APIs, and customer channels. The failure mode is not a bad answer in a chat window—it is an executed transaction, a mis-routed payment, or an irreversible data disclosure. Supervisory expectations on model risk, operational resilience, and consumer outcomes are tightening as AI touches regulated decisions.

Talk to us about where execution sits in your architecture.

Regulatory context

UK firms align model risk with internal governance and supervisory dialogue; the EU AI Act and emerging FCA/PRA expectations on operational resilience and fair outcomes increase the burden to demonstrate control over automated decisions, not only documentation.¹

Solution

TrigGuard sits between models and execution: every action request is evaluated against policy; only PERMIT outcomes reach payments, core banking, or messaging surfaces. You get deterministic enforcement, tamper-evident receipts, and policy change control—so model risk and compliance teams can evidence what was allowed, denied, or silenced.

• Real-time enforcement on the hot path
• Audit-ready decision and receipt trails
• Policy-as-code aligned to your risk taxonomy

Read how execution governance differs from monitoring →

Integration points

Typical insertion points: payment initiation and STP chains, card and faster-payments APIs, credit decision orchestration, fraud case management hooks, customer messaging and document generation prior to send.