  With the explosive growth of e-commerce, online transaction fraud has become
one of the biggest challenges for e-commerce platforms. The historical
behaviors of users provide rich information for digging into the users' fraud
risk. While considerable efforts have been made in this direction, a
long-standing challenge is how to effectively exploit internal user information
and provide explainable prediction results. In fact, the value variations of
same field from different events and the interactions of different fields
inside one event have proven to be strong indicators for fraudulent behaviors.
In this paper, we propose the Dual Importance-aware Factorization Machines
(DIFM), which exploits the internal field information among users' behavior
sequence from dual perspectives, i.e., field value variations and field
interactions simultaneously for fraud detection. The proposed model is deployed
in the risk management system of one of the world's largest e-commerce
platforms, which utilize it to provide real-time transaction fraud detection.
Experimental results on real industrial data from different regions in the
platform clearly demonstrate that our model achieves significant improvements
compared with various state-of-the-art baseline models. Moreover, the DIFM
could also give an insight into the explanation of the prediction results from
dual perspectives.
