Probabilistic Deep Learning Approach to Credit Card Fraud Detection

Credit card fraud detection deals with algorithms that can determine in real time whether an ongoing transaction is fraudulent, thereby improving the prevention of such transactions. Fraud detection approaches are often based on defining rigid boundaries between normal and fraudulent transactions. However, one of the challenges in establishing fraud detection rules is that the share of frauds in the total number of observed transactions is often not sufficient to enable good generalization when recognizing new frauds. In this article, we present a probabilistic deep learning approach to credit card fraud detection, which efficiently detects fraud based only on learned properties of normal transactions. Furthermore, we show a simple and natural interpretation of the results obtained by such models that can provide additional reasoning as to why a particular transaction can be considered fraudulent. We confirm our findings with tests on a synthetic set of transaction data and compare them with the results of other commonly used approaches.