FinTech · FinEdge Capital
Real-time fraud detection reduces losses 31% for digital lender
A digital lending platform replaced rules-only fraud screening with a real-time ML pipeline scoring 40k applications daily.
31%
fraud losses reduced
58%
fewer false positives
150ms
p99 scoring latency
$4.2M
annual loss prevention
The Problem
FinEdge's rules-based fraud screening produced 22% false positives — rejecting good borrowers — while sophisticated synthetic-identity fraud slipped through, driving charge-off losses up 40% year-over-year.
The Solution
We built a real-time fraud scoring platform combining gradient-boosted models, graph features for synthetic-identity detection and device intelligence. Applications are scored in under 150ms with explainable reason codes for compliance, and a champion–challenger framework retrains models weekly against fresh fraud labels.
Architecture
- Kafka event backbone ingesting application, device and bureau signals
- Feature store with online (Redis) and offline (Snowflake) parity
- XGBoost ensemble + graph-based synthetic identity detection
- Explainability service producing adverse-action reason codes
- Champion–challenger MLOps with weekly automated retraining
ROI
Combined loss prevention and recovered good-borrower approvals delivered 9× return on program cost in the first year.
Engagement
- Client
- FinEdge Capital
- Industry
- FinTech
- Service
- Artificial Intelligence
- Timeline
- 12 weeks to shadow mode; 20 weeks to full production cutover
Technologies
“The false-positive drop alone changed our unit economics. RippleCode delivered a fraud platform that our regulators and our growth team both love.”
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