Credit Card Fraud Detection with Automated Machine Learning Systems
Vasilios Plakandaras, Periklis Gogas, Théophilos Papadimitriou, Ioannis Tsamardinos
Abstract
The steady increase at the turnover of online trading during the last decade and the increasing use of credit cards has subsequently made credit card frauds more prevalent. Machine Learning (ML) models are among the most prominent techniques in detecting illicit transactions. In this paper, we apply the Just-Add-Data (JAD), a system that automates the selection of Machine Learning algorithms, the tuning of their hyper-parameter values, and the estimation of performance in detecting fraudulent transactions using a highly unbalanced dataset, swiftly providing prediction model for credit card fraud detection. The training of the model does not require the user setting up any of the methods’ (hyper)parameters. In addition, it is trivial to retrain the model with the arrival of new data, to visualize, interpret, and share the results at all management levels within a credit card organization, as well as to apply the model. The model selected by JAD identifies 32 out of a total of 39 fraudulent transactions of the test sample, with all missed fraudulent transactions being small transactions below 50€. The comparison with other methods on the same dataset reveals that all the above come with a high forecasting performance that matches the existing literature.