Real-time Fraud Detection for Fintech
Built ML-powered fraud detection system preventing ₹50L+ in fraudulent transactions monthly.
Fraud Prevented
Monthly savings
False Positives
Reduction
Response Time
Per transaction
System Uptime
Availability
!The Challenge
A growing digital payments startup was losing money to increasingly sophisticated fraud attacks. Their rule-based system couldn't keep up with evolving fraud patterns.
The situation: - ₹15-20L monthly losses to fraud - High false positive rate (8%) blocking legitimate transactions - New fraud patterns emerging weekly - Customer complaints about blocked genuine payments - Regulatory pressure to improve security
Our Solution
We built a real-time ML-powered fraud detection system that learns and adapts to new patterns.
Multi-Layer Detection
- Behavioral biometrics (typing patterns, device fingerprinting) - Transaction pattern analysis (velocity, amount, merchant categories) - Network analysis (device graphs, relationship mapping) - Ensemble ML models with XGBoost, Neural Networks, and Isolation Forests
Real-time Scoring
- Sub-50ms scoring for every transaction - Dynamic risk thresholds by transaction type - Step-up authentication for medium-risk transactions - Automatic blocking for high-risk patterns
Continuous Learning
- Daily model retraining on new fraud patterns - Feedback loop from investigation team - A/B testing of detection strategies
Fraud Detection Architecture • Click to enlarge
Results & Impact
Technologies Used
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