[Xiong Kewei] Cent China Normal Univ, Wuhan, Peoples R China.
[Peng, Binhui] Univ Warwick, Coventry, W Midlands, England.
[Jiang, Yang] Sun Yat Sen Univ Guangzhou, Guangzhou, Peoples R China.
[Lu, Tiying] Univ Calif Irvine, Irvine, CA USA.
通讯机构：
[Xiong Kewei] C
Cent China Normal Univ, Wuhan, Peoples R China.
语种：
英文
关键词：
Fraud detection;Deep learning;Feature engineering
期刊：
2021 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS AND COMPUTER ENGINEERING (ICCECE)
年：
2021
页码：
431-434
会议名称：
IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)
会议时间：
JAN 15-17, 2021
会议地点：
Guangzhou, PEOPLES R CHINA
会议主办单位：
[Xiong Kewei] Cent China Normal Univ, Wuhan, Peoples R China.^[Peng, Binhui] Univ Warwick, Coventry, W Midlands, England.^[Jiang, Yang] Sun Yat Sen Univ Guangzhou, Guangzhou, Peoples R China.^[Lu, Tiying] Univ Calif Irvine, Irvine, CA USA.
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 ...
