Real-time detection at 50M daily transactions
Real-time ensemble models reduced fraud losses 89% while processing 50M daily transactions in under 100 milliseconds
Project Info:
- Industry
- Digital payments
- Company Size
- Digital payments platform
- Timeline
- 14 weeks
- Investment
- $195K
Challenge:
20% annual churn killing growth targets. Unable to identify at-risk customers until cancellation notice received. Retention team reactive instead of proactive.
Solution:
Customer health scoring system analyzing 50+ signals: usage patterns, support interactions, feature adoption, engagement metrics. Identified churn risk 60-90 days before cancellation.
Results:
- 89% reduction in fraud losses ($10.7M saved annually)
- False positive rate: 78% → 12%
- Sub-100ms transaction processing.
- Scaled to 50M+ daily transactions (10x volume growth)
Technical approach:
- Neural networks + gradient trees ensemble
- Graph analytics for fraud ring detection
- Kafka for real-time event streaming
- TensorFlow Serving with Redis caching
Key takeaway:
Shadow mode deployment (monitoring without blocking) for 2 weeks validated performance before going live. Graph analysis exposed fraud rings missed by transaction-level models.
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