Representation Learning in Graphs for Credit Card Fraud Detection

Abstract

Representation learning in graphs has proven useful for many predictive tasks. In this paper we assess the feasibility of representation learning in a credit card fraud setting. Data analytics has been successful in predicting fraud in previous research. However, the research field has focused on techniques which require tedious and expensive hand-crafting of features. In addition, existing works often ignore information related to the network of transactions. Representation learning in graphs tackles both of these challenges. First, it provides the possibility to tap into the relational and structural aspects of the transaction network and leverage these in a predictive model. Second, it featurizes the graph without the need for manual feature engineering. This work contributes to the literature by being the first to explicitly and extensively show how fraud detection modeling can benefit from representation learning. We discern three different approaches in this paper: traditional network featurization, an inductive representation learning algorithm and a transductive representational learner. Through extensive experimental evaluation on a real-world dataset we show that state-of-the-art representation learning in graphs outperforms traditional graph featurization.