  Due to the popularity of the Internet and smart mobile devices, more and more
financial transactions and activities have been digitalized. Compared to
traditional financial fraud detection strategies using credit-related features,
customers are generating a large amount of unstructured behavioral data every
second. In this paper, we propose an Recurrent Neural Netword (RNN) based
deep-learning structure integrated with Markov Transition Field (MTF) for
predicting online fraud behaviors using customer's interactions with websites
or smart-phone apps as a series of states. In practice, we tested and proved
that the proposed network structure for processing sequential behavioral data
could significantly boost fraud predictive ability comparing with the
multilayer perceptron network and distance based classifier with Dynamic Time
Warping(DTW) as distance metric.
