Sentinel-TMME is a high-throughput financial anomaly detection engine. It models complex transaction networks to identify fraudulent behavior across millions of records. By representing financial transactions as a highly connected graph rather than isolated tabular data, the system captures the structural and relational dependencies between entities to expose sophisticated fraud rings.
- Framework: PyTorch
- Model: GraphSAGE (Graph Sample and Aggregate)
- Compute Optimization: Fully optimized for CUDA-enabled hardware to accelerate graph convolutions and maintain high training efficiency on large-scale datasets.
- Implements a scalable graph neural network pipeline designed for high-performance throughput.
- Engineered to manage the memory constraints and computational overhead inherent in processing millions of nodes and edges.
- Built with a focus on architectural efficiency, strictly prioritizing rigorous data engineering standards over generic implementations.