AI Fraud Detection

• An intelligence engine that scores every maintenance invoice against 45 signals and returns a decision in under thirty seconds — approve, review, or hold for payment — with plain-language reasoning for every decision.
• A Findings mechanism that connects the dots across the whole book of business: it detects the pattern that ties orders, vehicles and shops together, then delivers a finished case to the analyst instead of one flagged invoice at a time.
• Independent context that reduces noise: every invoice is checked against the vehicle’s real specifications, the vendor’s real footprint, and national-account pricing benchmarks — so reviewers spend their time on actual risk, not on the invoices that only looked odd.
• An analyst workspace with an executive risk view, a tiered review queue, a network map of vendor collusion, and a full audit trail on every disposition.
• A real-time scoring API embedded inside the client’s existing maintenance systems, so risky invoices can be held at the moment they are created — before payment leaves.

$5.94M Confirmed, Not Estimated

Two cases delivered so far: a $3.89M shell-vendor pair (5,000+ invoices across 400+ vans over 17 months) and a $2.05M tow-billing ring (530 orders across 22 shops and 321 vehicles). Every dollar validated by the client’s own fraud analytics team. Three vendor profiles shut down as a result.

Ready-Made Cases, Not Alerts

Reviewers open a finished case with the orders, vehicles and shops already linked — instead of chasing patterns across hundreds of thousands of separate invoices. Every score explainable, every disposition audit-trailed end to end.