Detecting Cash-out Users via Dense Subgraphs

Cash-out fraud refers to the withdrawal of cash from a credit card by illegitimate payments with merchants. Conventional data-driven approaches for cash-out detection commonly construct a classifier with domain specific feature engineering. To further spot cash-out behaviors in complex scenarios, recent efforts adopt graph models to exploit the interaction relations rich in financial transactions. However, most existing graph-based methods are proposed for online payment activities in internet financial institutions. Moreover, these methods commonly rely on a large amount of online user data, which are not well suitable for the traditional credit card services in commercial banks. In this paper, we focus on discerning fraudulent cash-out users by taking advantage of only the personal credit card data from banks. To alleviate the scarcity of available labeled data, we formulate the cash-out detection problem as identifying dense blocks. First, we define a bipartite multigraph to hold transactions between users and merchants, where cash-out activities generate cyclically intensive and high-volume flows. Second, we give a formal definition of cash-out behaviors from four perspectives: time, capital, cyclicity, and topotaxy. Then, we develop ANTICO, with a class of metrics to capture suspicious signals of the activities and a greedy algorithm to spot suspicious blocks by optimizing the proposed metric. Theoretical analysis shows a provable upper bound of ANTICO on the effectiveness of detecting cash-out users. Experimental results show that ANTICO outperforms state-of-the-art methods in accurately detecting cash-out users on both synthetic and real-world banking data.