Author:

Keywords:

Credit card transaction fraud, Network analysis, Bipartite graphs, Supervised learning, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Information Systems, Operations Research & Management Science, Computer Science, 01 Mathematical Sciences, 08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, Information Systems, 35 Commerce, management, tourism and services, 46 Information and computing sciences, 49 Mathematical sciences

Abstract:

In the last decade, the ease of online payment has opened up many new opportunities for e-commerce, lowering the geographical boundaries for retail. While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records. This paper proposes APATE, a novel approach to detect fraudulent credit card transactions conducted in online stores. Our approach combines (1) intrinsic features derived from the characteristics of incoming transactions and the customer spending history using the fundamentals of RFM (Recency – Frequency - Monetary); and (2) network-based features by exploiting the network of credit card holders and merchants and deriving a time-dependent suspiciousness score for each network object. Our results show that both intrinsic and network-based features are two strongly intertwined sides of the same picture. The combination of these two types of features leads to the best performing models which reach AUC-scores higher than 0.98.