AI fraud detection: how it works, and what to check before you rely on it

AI fraud detection is software that learns what normal activity looks like from past data, then scores new activity by how far it departs from that pattern. A payment, an account change or an insurance claim that scores high is flagged. We recommend a set-up that sends flagged items to a person who decides, rather than blocking or denying them automatically. Our own insurance page describes the claims version this way: scoring incoming claims against patterns learned from past fraud referrals and investigation outcomes, then routing the highest scoring claims to a special investigations unit for human review (Kriv AI, read 2026-11-07). The same idea applies to payments, accounts and applications.

AI appears on both sides of fraud. Organizations use it to spot suspicious activity, and criminals use it to commit fraud. In November 2024 the US Treasury's FinCEN issued an alert on fraud schemes using deepfake media made with generative AI. It reports an increase in suspicious activity reporting that describes fraudulent identity documents used to get around identity checks, and it asks financial institutions to stay vigilant and report related suspicious activity (FinCEN, 13 November 2024, read 2026-11-07). Our view is that a fraud model built on yesterday's patterns can miss new tricks, so someone has to keep watching it.

Kriv AI is a consultancy that designs, builds and governs AI for regulated organizations. For fraud detection we help a team decide what the model may decide alone and what goes to a person, and we design the review step, the testing and the records. We have an insurance fraud detection accelerator and a claims fraud detection consulting page, linked below. We do not sell a fraud detection product, monitor your transactions or file reports for you, and nothing on this page is legal advice or a statement that any system complies with a rule. Scope and price are set after a short discovery call.