Fraud Prevention and Detection on Heterogeneous Information Networks with Deep Graph Infomax

Abstract

Fraud is ubiquitous in both institutions and private companies, costing $1.9 billion in losses in the US, in 2019 alone. Fraud detection introduces a way to mitigate these losses while ensuring better security and enabling trust between all parties. However, it frequently comes at a great cost of resources needed to locate and oppose fraudulent cases manually. This cost also grows exponentially with the size of a company's financial transaction network. In this work, we propose a novel framework for automatic fraud detection that relies on institutions' readily available data, that aims at reducing the cost of resources outlined above. We evaluate our framework by comparing it against a baseline result and show an increase of 37% in performance expressed as F1-score while providing highly desirable characteristics such as online learning capability and a reduction of 82% in training time on commodity hardware.