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


  • Aneta Dzik-Walczak

    (Faculty of Economic Sciences, University of Warsaw)

  • Mateusz Heba

    () (Faculty of Economic Sciences, University of Warsaw)


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.

Suggested Citation

  • Aneta Dzik-Walczak & Mateusz Heba, 2019. "A comparison of credit scoring techniques in Peer-to-Peer lending," Working Papers 2019-16, Faculty of Economic Sciences, University of Warsaw.
  • Handle: RePEc:war:wpaper:2019-16

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    More about this item


    credit scoring; credit risk; Lending Club; logistic regression; neural nets; peer-to-peer lending;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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