PREDICTIVE ML • FINANCE

Real-Time Anomaly & Fraud Engine

Streaming deep learning autoencoders analyzing 1.4 million transactions per second with 140ms latency.

Technical Context

High-frequency trading firms and payment gateways face millisecond fraud windows where traditional rule-based compliance engines fail to flag market manipulation or spoofing patterns until long after execution.

Solution & Architecture

Rohit engineered a **PyTorch Autoencoder + C++ ONNX Streaming Pipeline** connected to Apache Kafka:

  • Deep Autoencoder Reconstruction: Trained on historical tick streams to flag high reconstruction error anomalies in sub-150ms.
  • CUDA Optimization: Deployed model on C++ ONNX Runtime with TensorRT GPU acceleration handling 1.4M events/sec.

Verified Customer Testimonial

"Rohit built our real-time anomaly engine with sub-140ms latency across 1.4 million transactions per second. His deep learning autoencoder approach caught market manipulation anomalies that saved our trading desk over $800k in potential loss. Exceptional technical expertise."

Dr. Aris Thorne

Quantifiable Results

140ms
Latency
98.7%
Precision
1.4M/s
Throughput

Project Specs

Category: Financial Anomaly ML
Status: Active Production
Architect: Rohit
Tech Stack Used
PyTorch Kafka ONNX Runtime CUDA

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