Development of a methodology for detecting fraud with bank loans for legal entities

Tolstyakov, Nikita; Mamedova, Natalia

doi:10.1051/shsconf/202110601016

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Detecting fraud users (Hu et al 2019;Liu et al 2018;Wang et al 2019;Cheng et al 2020;Jiang, Ni, and Wang 2021;Zhang et al 2022;Wang et al 2019;Zhan and Yin 2018;Qian et al 2021;Wang et al 2021;Tolstyakov and Mamedova 2021;Yang et al 2022) is a crucial part of risk forecasting. The purpose of fraud user detection is to predict whether one user will fail to make required payments at the stipulated repayment time.…”

Section: Introductionmentioning

confidence: 99%

Loan Fraud Users Detection in Online Lending Leveraging Multiple Data Views

Zhao

Huang

Chen³

et al. 2023

AAAI

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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-to-end 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.

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