AI Fraud Detection Development

Reference architecture

1. Transaction Stream
2. Real-time Scoring Engine
3. Decision (allow / review / block)
4. Investigator UI
5. Audit Log
6. Regulator Reporting

A typical real-time fraud system: the transaction stream feeds a scoring engine that allows, holds, or blocks in milliseconds; held cases route to an investigator UI, and every decision is logged immutably for audit and regulator reporting.

Frequently asked questions

• Can your system handle 1M+ transactions per day?+

  Yes. Streaming architectures (Kafka, Kinesis, Pub/Sub) scale horizontally, and the scoring engine is built to keep sub-100ms decisions under load. We’ve shipped at high transaction volume and load-test to your peak before launch.

• Do you handle SAR (Suspicious Activity Report) generation?+

  We build the data collection and draft-assistance layer: surfacing the right context and pre-filling the narrative. The final SAR filing remains your compliance officer’s responsibility. We speed up that work; we don’t file on their behalf.

• How do you handle model drift?+

  Automated drift detection on feature and score distributions, scheduled retraining pipelines, and A/B testing of new models against the current control before any promotion to production.

• Can you integrate with our existing sanctions screening?+

  Yes. Refinitiv World-Check, ComplyAdvantage, and Dow Jones Risk all integrate. If you run a different provider with an API, we’ll wire it into the scoring and case-management flow.

• What’s the explainability of decisions for regulator review?+

  Every decision carries per-feature importance, a decision rationale, and an immutable audit-log entry. The system is architected for adverse-action explainability so a reviewer can see exactly why a transaction was allowed, held, or blocked.