AI in finance in 2026
Banks, insurers and fintechs drown in documents: contracts, statements, corporate registrations, IDs, regulatory reports. Manual reading and classification cost a fortune and slow underwriting. Modern LLMs hit production-grade extraction rates when properly scoped and monitored. The other angle is analysis: prospectus summaries, transaction anomaly detection, pre-formatted regulatory reporting generation.
But finance tolerates no hand-waving. Every decision made or influenced by a model must be traceable, explainable and auditable. Regulators (EBA, FCA, SEC) are putting clear guardrails on AI in decision chains.
Typical use cases
- Structured extraction from contracts, terms, IDs, payslips, with validation and audit trail
- KYC and KYB automation: document parsing, inconsistency detection, initial scoring before human review
- Classification and automatic routing of client emails and complaints to the right teams
- Internal assistant for advisors: semantic search on product docs, validated email generation
- Transaction reconciliation and anomaly detection on high-volume flows
Stack and specific constraints
Full audit trail on every LLM call: prompt, response, model version, human validator. No PII sent to US providers without a DPA and anonymization. I favor Mistral or self-hosted models when sovereignty matters, Claude or GPT when quality justifies the trade-off. GDPR, DORA compliance, and immutable infra logs. Typical stack: TypeScript, Python for ML pipelines, Postgres with row-level security, Cloudflare R2 or European S3, self-hosted vector DB (Qdrant).
Let’s talk
30 minutes to scope a first use case: Cal.com.