Unsupervised graph anomaly detection - Catching new fraudulent behaviours

This post details the development and application of GraphBEAN, an in-house unsupervised machine learning model for detecting anomalous patterns in bipartite graphs. It describes the graph construction process for consumer-merchant interactions in GrabFood and GrabMart, the GraphBEAN model architecture (an autoencoder with feature and structure decoders), and the computation of edge-level and node-level anomaly scores based on reconstruction error. The post also outlines the full pipeline for anomaly detection and actioning, including a fraud type tagger and subsystems for human expert and automatic actioning.