Credit Card Fraud Detection Using Capsule Network

Credit card is now popular in daily life. Meanwhile, credit card fraud events occur more frequently, which result in massive financial losses. There are a number of fraud detection methods, but they do not deeply mine features of customer's transaction behavior so that their detection effectiveness is not too desirable. This paper focuses on two aspects of feature mining. Firstly, the features of credit card transactions are expanded in time dimension to characterize the distinct payment habits of legal users and criminals. Secondly, Capsule Network (CapsNet) is adopted to further dig some deep features on the base of the expanded features, and then a fraud detection model is trained to identify if a transaction is legal or fraud. Through experiments on a real transaction dataset, we demonstrate that the time dimension extension can improve the performance of fraud detection, and then CapsNet is further illustrated to be more advantageous in fraud detection compared with other models.