AI for banking, identity and fraud detection
KYC document intelligence, alert triage and investigation support that keep customer identity data and fraud logic inside your perimeter — where regulators and adversaries both expect them to stay.
Common deployments
KYC document processing
OCR plus visual verification of identity documents and proofs, with confidence scoring and exception queues.
Alert triage support
Transaction-monitoring alerts enriched with context and draft dispositions for analyst review.
Investigation case summarisation
Narratives assembled from transactions, communications and documents for SAR-quality case files.
Sanctions & adverse-media screening support
Local models resolving name matches and summarising findings with sources.
Dispute & chargeback handling
Evidence gathering and response drafting from your records at consistent quality.
Fraud-pattern description
Plain-language summaries of emerging patterns for rules teams and management reporting.
Compliance posture
- Identity documents and case data processed entirely in-perimeter
- Fraud logic and detection patterns never exposed to external providers
- Tamper-evident audit logs suitable for regulatory examination
- Human analysts retain all disposition decisions
- Deterministic cost per case regardless of alert volume spikes
Why on-premise here
Public AI endpoints create a data-processing relationship your compliance team must defend. Keeping inference inside your infrastructure removes that transfer entirely: prompts, documents and outputs never leave systems you control, and audit logs live in your own SIEM.
Frequently asked questions
Why on-premise specifically for fraud work?
Two reasons: identity data is among the most regulated you hold, and your detection logic is exactly what fraudsters want to probe. Keeping both inside your infrastructure removes an entire class of exposure.
Does AI replace our transaction-monitoring system?
No — it sits on top of it. Models reduce the triage burden of alerts your existing system generates and improve case-file quality; your rules and models of record stay authoritative.
How do you handle model errors in this context?
Everything ships with confidence thresholds, exception queues and human sign-off. We measure false-positive and false-negative behaviour on your historical cases before go-live.