Sequential Behavioral Data Processing Using Deep Learning and the Markov Transition Field in Online Fraud Detection
This work addresses fraud detection for online financial transactions, offering a domain-specific incremental improvement.
The paper tackled online fraud detection by proposing a deep learning model combining RNN and Markov Transition Field to process sequential behavioral data, achieving significant improvement over baseline methods like multilayer perceptron and DTW-based classifiers.
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.