AI in Finance
Financial services is the sector where AI governance stops being theoretical: model risk management, fair lending, market conduct and transaction monitoring all carry enforceable obligations. The governance and security pages below apply directly; the sector-specific pages are being written from regulation and supervisory guidance.
Financial systems and the obligations attached
Obligations and risks that bite hardest in regulated financial institutions.
Double-entry accounting as a database schema - append-only entries, idempotency keys for retried payments, derived balances, and daily reconciliation.
AML and sanctions architecture where false-positive economics dominate - alert generation, triage, tuning as a governed change, and the audit trail.
Why LLM inference is a recurring per-request cost, how input and output tokens are priced separately, and where cached and batch tiers actually apply.
Hosted API against self-hosted open weights - the break-even formula, why utilisation and latency SLOs decide it, and costs that never appear per token.
Regulation (EU) 2024/1689 in detail - the four risk tiers, provider versus deployer duties, GPAI and systemic risk, phased dates and fine ceilings.
Where generative AI processes personal data, the lawful basis and DPIA analysis, erasure against model weights, and the Dutch DPA's deployment checks.
OWASP LLM03:2026 - excessive functionality, permissions, and autonomy turn a misbehaving model into a damaging action. The controls that bound it.