Learning Latent Customer Representations for Credit Card Fraud Detection

A growing amount of consumers are making purchases online. Due to this rise in online retail, online credit card fraud is increasingly becoming a common type of theft. Previously used rule based systems are no longer scalable, because fraudsters can adapt their strategies over time. The advantage of using machine learning is that it does not require an expert to design rules which need to be updated periodically. Furthermore, algorithms can adapt to new fraudulent behaviour by retraining on newer transactions. Nevertheless, fraud detection by means of data mining and machine learning comes with a few challenges as well. The very unbalanced nature of the data and the fact that most payment processing companies only process a fragment of the incoming traffic from merchants, makes it hard to detect reliable patterns. Previously done research has focussed mainly on augmenting the data with useful features in order to improve the detectable patterns. These papers have proven that focussing on customer transaction behavior provides the necessary patterns in order to detect fraudulent behavior. In this thesis we propose several bayesian network models which rely on latent representations of fraudulent transactions, non-fraudulent transactions and customers. These representations are learned using unsupervised learning techniques. We show that the methods proposed in this thesis significantly outperform state-of-the-art models without using elaborate feature engineering strategies. A portion of this thesis focuses on re-implementing two of these feature engineering strategies in order to support this claim. Results from these experiments show that modeling fraudulent and non-fraudulent transactions individually generates the best performance in terms of classification accuracy. In addition, we focus on varying the dimensions of the latent space in order to assess its effect on performance. Our final results show that a higher dimensional latent space does not necessarily improve the performance of our models.