Distributed Monitoring for Data Distribution Shifts in Edge-ML Fraud Detection
This addresses the lack of robust monitoring systems for data distribution shifts in edge-ML fraud detection, which is crucial for maintaining real-time fraud detection accuracy in smartphone payment services.
The paper tackles the problem of monitoring data distribution shifts in distributed edge ML applications for fraud detection, introducing a novel open-source framework that includes an innovative distributed Kolmogorov-Smirnov test and demonstrates effectiveness on real-world and synthetic financial transaction datasets.
The digital era has seen a marked increase in financial fraud. edge ML emerged as a promising solution for smartphone payment services fraud detection, enabling the deployment of ML models directly on edge devices. This approach enables a more personalized real-time fraud detection. However, a significant gap in current research is the lack of a robust system for monitoring data distribution shifts in these distributed edge ML applications. Our work bridges this gap by introducing a novel open-source framework designed for continuous monitoring of data distribution shifts on a network of edge devices. Our system includes an innovative calculation of the Kolmogorov-Smirnov (KS) test over a distributed network of edge devices, enabling efficient and accurate monitoring of users behavior shifts. We comprehensively evaluate the proposed framework employing both real-world and synthetic financial transaction datasets and demonstrate the framework's effectiveness.