Rules Extraction and Deep Learning for e-Commerce Fraud Detection

Deep learning methods and applications have become an integral part of all finance subfields. Fraud detection is one of these subfields where these applications managed to save billions of dollars every year. However, one weakness of these models is that we cannot understand what rules they follow to make their decisions. This paper aims to introduce a novel deep learning approach to detect fraud in e-commerce, taking into consideration the problem mentioned above by extracting if-then rules using CRED algorithm (a Continuous/discrete Rule Extractor via Decision tree induction) that employs decision trees to extract these rules. Besides using feature aggregation and re-sampling method on two highly imbalanced data-sets, we conducted a comparative study with previous work. The results show that our model gives better performance.