A Hybrid Deep Learning Model For Online Fraud Detection
Kewei Xiong, Binhui Peng, Yang Jiang, Tiying Lu
Abstract
Nowadays, credit cards are becoming more and more widely used for both online and offline transactions. But along with this trend comes more credit card fraud. According to the Nilson report the global loss to credit card fraud is expected to reach $35 billion this year, so there is a desperate need for accurate and efficient fraud detection systems. In this paper, we propose a deep-learning-based method to tackle this problem. We employed multiple techniques, including feature engineering, memory compression, mixed precision, and ensemble loss to boost the performance of our model. The model is trained and evaluated on the IEEE-CIS fraud dataset provided by Vesta Corporation consisting of over 1 million records. Experiments show that our model outperforms traditional machine-learning-based methods like Bayes and SVM.