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Financial Services
AI-Powered Fraud Detection for Global Fintech
Built a real-time fraud detection system using machine learning that reduced fraudulent transactions by 94% while processing 50,000+ transactions per second.
Duration
8 months
Team Size
6 engineers
Investment
$450K
Location
London, UK
The Challenge
A major payment processor losing $15M+/year to fraud needed a real-time ML solution with sub-100ms latency and PCI-DSS compliance.
Key challenges:
- 50,000+ transactions/second with sub-100ms latency
- Evolving fraud patterns that change daily
- High false-positive rate frustrating legitimate customers
- PCI-DSS and SOC2 compliance requirements
Our Solution
Multi-layered ML fraud detection combining real-time analysis with continuous learning.
Architecture:
- Ensemble models (XGBoost, LSTM, Isolation Forest)
- Real-time feature engineering with Apache Kafka + Flink
- Graph neural networks for fraud ring detection
- Explainable AI dashboard for compliance teams
Key Features Delivered
Real-time transaction scoring
Behavioral biometrics
Device fingerprinting
Graph-based fraud ring detection
Explainable AI
Automated model retraining
Results & Impact
94%
Fraud reduction
$12M
Annual savings
67%
Fewer false positives
<50ms
Response latency
“DevSimplex delivered a fraud detection system that exceeded our expectations. The reduction in fraud losses paid for the entire project within 3 months.”
James Morrison
Technologies Used
PythonTensorFlowApache KafkaApache FlinkAWSPostgreSQLRedisDockerKubernetes
Services Provided
Machine LearningData EngineeringDevops
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