Fraud Detection System
Real-time ML fraud detection with anomaly scoring, case management, and adaptive learning.
Duration
10 months
Team Size
6 developers
Industry
FinTech
Category
AI/ML
Real-Time Fraud Detection System
A machine learning-powered fraud detection platform that analyzes transactions in real-time, preventing financial crime while minimizing friction for legitimate customers.
The Challenge
A financial services company struggled with fraud:
- Rising fraud losses - $10M+ annually in chargebacks
- High false positives - Blocking legitimate customers
- Slow detection - Manual review taking days
- Rule fatigue - 1000+ rules becoming unmanageable
They needed intelligent, adaptive fraud detection.
Our Approach
We built an ML-first fraud platform that learns and adapts in real-time.
Detection Strategy
- Multi-Model Ensemble - Different models for different fraud types
- Real-Time Scoring - Sub-100ms decisions
- Adaptive Learning - Models improve from analyst feedback
- Explainable AI - Clear reasons for every decision
The Solution
Real-Time Scoring
- Transaction risk scoring
- Device fingerprinting
- Behavioral biometrics
- Network analysis
Case Management
- Analyst investigation queue
- Evidence aggregation
- Decision workflows
- Feedback loop to ML
Rule Engine
- Business rules overlay
- Velocity checks
- Watchlist matching
- Geographic restrictions
Reporting & Analytics
- Fraud trend dashboards
- False positive analysis
- Model performance metrics
- Regulatory reporting
Technology Stack
| Layer | Technologies |
|---|---|
| ML Models | Python, XGBoost, PyTorch |
| Streaming | Apache Kafka, Flink |
| Backend | Python, FastAPI |
| Database | PostgreSQL, Redis |
| Frontend | React, TypeScript |
| Infrastructure | Kubernetes, AWS |
Results & Impact
The system dramatically improved fraud prevention:
- 95% detection rate for fraud attempts
- 80% fewer false positives blocking good customers
- $50M+ prevented in fraud annually
- Sub-100ms decisions for real-time blocking
ML Features
Model Types
- Transaction anomaly detection
- Account takeover prediction
- Synthetic identity detection
- Merchant fraud scoring
Continuous Learning
- Analyst feedback integration
- Daily model retraining
- A/B testing of models
- Champion/challenger framework
Client Testimonial
"We went from millions in fraud losses to industry-leading prevention. The ML catches patterns our rules never could, and the false positive reduction means happier customers."
— Chief Risk Officer, Financial Services Company
Fighting fraud? Contact us to discuss ML-powered detection solutions.
Key Results
95% fraud detection rate
80% reduction in false positives
$50M+ fraud prevented annually
Sub-100ms decision time
Technology Stack
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