Loan Fraud Users Detection in Online Lending Leveraging Multiple Data Views

Sha Zhao, Yongrui Huang, Ling Chen, Chunping Wang, Shijian Li, Lei CHEN, Gang Pan

Abstract:

In recent years, online lending platforms have been becoming
attractive for micro-financing and popular in financial industries. However, such online lending platforms face a high risk
of failure due to the lack of expertise on borrowers’ creditworthness. Thus, risk forecasting is important to avoid economic loss. Detecting loan fraud users in advance is at the
heart of risk forecasting. The purpose of fraud user (borrower) detection is to predict whether one user will fail to
make required payments in the future. Detecting fraud users
depend on historical loan records. However, a large proportion of users lack such information, especially for new users.
In this paper, we attempt to detect loan fraud users from
cross domain heterogeneous data views, including user attributes, installed app lists, app installation behaviors, and
app-in logs, which compensate for the lack of historical loan
records. However, it is difficult to effectively fuse the multiple heterogeneous data views. Moreover, some samples miss
one or even more data views, increasing the difficulty in fusion. To address the challenges, we propose a novel end-toend deep multiview learning approach, which encodes heterogeneous data views into homogeneous ones, generates the
missing views based on the learned relationship among all the
views, and then fuses all the views together to a comprehensive view for identifying fraud users. Our model is evaluated
on a real-world large-scale dataset consisting of 401,978 loan
records of 228,117 users from January 1, 2019, to September
30, 2019, achieving the state-of-the-art performance.