A comparative study on machine learning models combining with outlier detection and balanced sampling methods for credit scoring

Abstract

Peer-to-peer (P2P) lending platforms have grown rapidly over the past decade as the network infrastructure has improved and the demand for personal lending has grown. Such platforms allow users to create peer-to-peer lending relationships without the help of traditional financial institutions. Assessing the borrowers' credit is crucial to reduce the default rate and benign development of P2P platforms. Building a personal credit scoring machine learning model can effectively predict whether users will repay loans on the P2P platform. And the handling of data outliers and sample imbalance problems can affect the final effect of machine learning models.There have been some studies on balanced sampling methods, but the effect of outlier detection methods and their combination with balanced sampling methods on the effectiveness of machine learning models has not been fully studied. In this paper, the influence of using different outlier detection methods and balanced sampling methods on commonly used machine learning models is investigated. Experiments on 44,487 Lending Club samples show that proper outlier detection can improve the effectiveness of the machine learning model, and the balanced sampling method only has a good effect on a few machine learning models, such as MLP.

Loan Fraud Users Detection in Online Lending Leveraging Multiple Data Views

“…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.…”