A comparison of credit scoring techniques in Peer-to-Peer lending

Aneta Dzik-Walczak and Mateusz Heba
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Aneta Dzik-Walczak: Faculty of Economic Sciences, University of Warsaw

No 2019-16, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: Credit scoring has become an important issue as the competition among financial institutions becomes very intense and even a slight improvement in accuracy of prediction might translate into significant savings. Financial institutions are seeking optimal strategies through the help of credit scoring models. Therefore credit scoring tools are widely studied. As a result different parametric statistical methods, non-parametric statistical tools and soft-computing approaches have been developed in order to increase the accuracy of credit scoring models. In this paper different approaches to classify customers as those who pay back loan and those who default on a loan will be employed. The purpose of this study is to explore the performance of two credit scoring techniques, the logistic regression model and neural networks. In order to evaluate the feasibility and effectiveness of these methods analysis is performed on Ledning Club data. Peer-to-Peer lending, also called social lending are investigated. On the basis of the results, we can conclude that logistic regression model can provide better performance than neutral nets.

Keywords: credit scoring; credit risk; Lending Club; logistic regression; neural nets; peer-to-peer lending (search for similar items in EconPapers)
JEL-codes: G21 G32 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2019
New Economics Papers: this item is included in nep-ban and nep-ore
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