通过机器学习实现移动设备支付中绑定银行卡环节的欺诈侦测

周皓; 柴洪峰; 邱懋霖

doi:10.1631/FITEE.1800580

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通过机器学习实现移动设备支付中绑定银行卡环节的欺诈侦测

网络空间安全专辑 | Updated：2022-05-19

- 通过机器学习实现移动设备支付中绑定银行卡环节的欺诈侦测
- Fraud detection within bankcard enrollment on mobile device based payment using machine learning
- 信息与电子工程前沿（英文） 2018年19卷第12期页码：1537-1545
- Affiliations：
  
  School of Cyber Security, Shanghai Jiao Tong University, Shanghai 200240, China
  Department of Risk Control, China UnionPay, Shanghai 200135, China
  Office of Board of Directors, China UnionPay, Shanghai 200135, China
  Chinese Academy of Engineering, Beijing 100088, China
- Author bio：
  
  [ "Hao ZHOU, E-mail: zhouhaounionpay@sjtu.edu.cn" ]
- Funds：
  
  Project supported by China UnionPay
- DOI：10.1631/FITEE.1800580
  中图分类号： TP309.2;TP181
- 收稿：2018-09-17，
  
  修回：2018-12-24，
  
  纸质出版：2018-12
- Accepted：
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周皓, 柴洪峰, 邱懋霖. 通过机器学习实现移动设备支付中绑定银行卡环节的欺诈侦测[J]. 信息与电子工程前沿（英文）, 2018,19(12):1537-1545.

Hao ZHOU, Hong-feng CHAI, Mao-lin QIU. Fraud detection within bankcard enrollment on mobile device based payment using machine learning[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(12): 1537-1545.
周皓, 柴洪峰, 邱懋霖. 通过机器学习实现移动设备支付中绑定银行卡环节的欺诈侦测[J]. 信息与电子工程前沿（英文）, 2018,19(12):1537-1545. DOI： 10.1631/FITEE.1800580.

Hao ZHOU, Hong-feng CHAI, Mao-lin QIU. Fraud detection within bankcard enrollment on mobile device based payment using machine learning[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(12): 1537-1545. DOI： 10.1631/FITEE.1800580.

摘要

移动互联网技术的快速增长促进了移动支付的增长，也带来更多移动交易欺诈。作为移动交易的第一步，移动设备上的银行卡绑定已成为欺诈尝试的主要目标。虽然该环节的欺诈成功尝试不会立即造成资金损失，但直接导致后续的快速欺诈交易，且可以欺骗现有欺诈侦测系统。近年来，金融机构和服务提供商通过实施基于规则的专家系统，并采用短消息服务（SMS）认证用户身份解决这个问题。但是，上述解决方案不足以应对数据泄漏和社会工程的挑战。在本研究中，我们介绍了几种传统机器学习算法，最后选择改进的梯度增强决策树（GBDT）算法软件库用于实际系统，即XGBoost。基于对绑卡行为的分析进一步扩展了多个特征，并计划在未来研究中添加历史交易数据进行分析。使用了由全球支付处理商提供的2017年全年真实绑定银行卡的数据集，研究结果和框架已被上述全球支付处理商的移动支付欺诈侦测系统的新设计方案采纳。

Abstract

The rapid growth of mobile Internet technologies has induced a dramatic increase in mobile payments as well as concomitant mobile transaction fraud. As the first step of mobile transactions

bankcard enrollment on mobile devices has become the primary target of fraud attempts. Although no immediate financial loss is incurred after a fraud attempt

subsequent fraudulent transactions can be quickly executed and could easily deceive the fraud detection systems if the fraud attempt succeeds at the bankcard enrollment step. In recent years

financial institutions and service providers have implemented rule-based expert systems and adopted short message service (SMS) user authentication to address this problem. However

the above solution is inadequate to face the challenges of data loss and social engineering. In this study

we introduce several traditional machine learning algorithms and finally choose the improved gradient boosting decision tree (GBDT) algorithm software library for use in a real system

namely

XGBoost. We further expand multiple features based on analysis of the enrollment behavior and plan to add historical transactions in future studies. Subsequently

we use a real card enrollment dataset covering the year 2017

provided by a worldwide payment processor. The results and framework are adopted and absorbed into a new design for a mobile payment fraud detection system within the Chinese payment processor.

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