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AI-Powered Fraud Detection for FinTech

Global Payment Processor

A leading global payment processor was losing millions annually to fraudulent transactions. They needed a real-time system to detect and prevent fraud without impacting legitimate transactions.

The Challenge

The client was operating with rule-based fraud detection yielding only 60% accuracy and high false positive rates blocking legitimate transactions. Their legacy system couldn't scale to billions of daily transactions.

Our Solution

  • Conducted comprehensive audit of transaction data and fraud patterns
  • Built ensemble machine learning model combining gradient boosting and neural networks
  • Implemented real-time inference pipeline processing 100k+ transactions/second
  • Created feedback loop automatically retraining models weekly
  • Deployed on Kubernetes with 99.99% uptime SLA

Key Outcomes

  • 85% reduction in fraud losses ($12M annual savings)
  • 0.1% false positive rate (previously 15%)
  • Processed 50B+ transactions with < 100ms latency
  • 40% increase in legitimate transaction approval

Technologies Used

TensorFlowXGBoostPythonKubernetesPostgreSQL

Results

The AI-powered system immediately reduced fraud loss by 85%, enabling the client to expand to new markets with confidence. The system continues learning and improving, now handling billions of transactions annually.

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