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AI USE CASE
AI-driven fraud & anomaly detection
Catch fraudulent transactions and operational anomalies before they hit your books.
What it is
An unsupervised + supervised hybrid model continuously scans transactions, refunds and operational events to flag anomalies for human review. Reduces fraud losses and uncovers process leaks.
Data you need
12+ months of clean transactional data with labelled fraud cases.
Required systems
- erp
- data warehouse
Why it works
- Tune threshold to keep alerts manageable for the review team
- Quarterly model audit with a fraud SME
How this goes wrong
- Too many false positives, analysts stop reviewing
- Concept drift after a process change goes unnoticed
When NOT to do this
Don't deploy without a dedicated review team, alerts will be ignored.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.