ML models catch synthetic identity fraud that rule-based systems miss entirely
Synthetic identity fraud losses hit $14M in a single quarter. Their rule-based fraud detection system flagged 30% of legitimate applications as suspicious (slowing approvals and angering dealers) while missing the actual fraudsters who'd learned to game the rules. Dealers were threatening to shift volume to competing lenders with faster approvals.
This Irvine-based captive auto finance company processed 800,000 loan applications annually. Their fraud team of 15 analysts manually reviewed every flagged application — but the rules flagged so many false positives that analysts spent 70% of their time clearing legitimate deals. Meanwhile, sophisticated fraud rings using synthetic identities sailed through because they'd specifically engineered applications to avoid the known rule triggers.
We deployed an ML-based fraud detection system that analyzes 200+ behavioral and data signals per application — not just the 12 fields the old rules checked. The model identifies synthetic identities by detecting subtle inconsistencies across credit bureau data, device fingerprints, application velocity, and behavioral biometrics. Our overnight team handles manual review of edge cases flagged during after-hours dealer submissions.
The old system flagged every application from a new apartment complex as suspicious because none of the addresses had credit history. The AI understands that new construction exists. Sounds simple, but that one fix cleared 2,000 false positives per month.— Director of Fraud Prevention
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