Financial Risk Analytics
Predictive modeling for real-time fraud detection and prevention in banking.
View All Security Case StudiesThe Challenge
A global banking institution needed to modernize its fraud detection system to handle increasing volumes of transactions while reducing false positives and improving detection accuracy.
Our solution combined real-time behavioral analytics with machine learning models trained on historical fraud patterns, implementing a layered defense strategy across all transaction channels.
The Results
- 92% reduction in fraudulent transactions
- 40% decrease in manual fraud review workload
- Compliance with PCI DSS and Basel III requirements
Technical Implementation
Security Framework
Implemented hybrid cloud architecture with federated learning capabilities, allowing real-time updates to fraud models while maintaining data sovereignty.
Analytics Layer
Deployed ensemble machine learning models combining random forests and neural networks for adaptive fraud pattern recognition across billions of transactions.
Compliance
Automated regulatory reporting tools with built-in explainability features for AI-driven decisions, meeting both internal audit requirements and international financial regulations.
Performance
Optimized inference pipelines using GPU acceleration to process 100,000+ transactions per second with <50ms latency, even during peak loads.