A big data-based anti-fraud model for internet finance

被引：3
|
作者：
Liu F. [1 ]
You Y. [2 ]
机构：
[1] Economics and Management School, Wuhan University, Wuhan
[2] Institute of Finance, Chinese Academy of Social Sciences, China Banking and Insurance Regulatory Commission, Beijing
来源：
Revue d'Intelligence Artificielle | 2020年 / 34卷 / 04期
关键词：
Anti-fraud model; Big data analysis; Internet finance; Machine learning (ML); Random forest (RF) algorithm; Risk control model;
D O I：
10.18280/ria.340416
中图分类号：
学科分类号：
摘要：
Fraud has become a serious risk to the burgeoning industry of Internet finance. To predict and prevent the fraud, this paper develops a risk control model that accurately predicts the fraud behaviors of online loan borrowers, based on their social network data and consumption information. Firstly, statistical analysis was carried out to clarify the relationship between user behavior and fraud, and that between social network and fraud. Next, suitable variables were selected through big data analysis, and used to build the risk control model, in combination with random forest (RF) algorithm and modern financial theory. The effectiveness of the proposed model was confirmed through contrastive experiments. Our model provides peer-to-peer (P2P) online loan platforms with an effective tool to prevent fraud. © 2020 Lavoisier. All rights reserved.
引用
收藏
页码：501 / 506
页数：5
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