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Concept of peer-to-peer lending and application of machine learning in credit scoring

Author

Listed:
  • Aleksy Klimowicz

    (Faculty of Economic Sciences, University of Warsaw)

  • Krzysztof Spirzewski

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

Numerous applications of AI are found in the banking sector. Starting from front-office, enhancing customer recognition and personalized services, continuing in middle-office with automated fraud-detection systems, ending with back-office and internal processes automatization. In this paper we provide comprehensive information on the phenomenon of peer-to-peer lending in the modern view of alternative finance and crowdfunding from several perspectives. The aim of this research is to explore the phenomenon of peer-to-peer lending market model. We apply and check the suitability and effectiveness of credit scorecards in the marketplace lending along with determining the appropriate cut-off point. We conducted this research by exploring recent studies and open-source data on marketplace lending. The scorecard development is based on the P2P loans open dataset that contains repayments record along with both hard and soft features of each loan. The quantitative part consists of applying a machine learning algorithm in building a credit scorecard, namely logistic regression.

Suggested Citation

  • Aleksy Klimowicz & Krzysztof Spirzewski, 2021. "Concept of peer-to-peer lending and application of machine learning in credit scoring," Working Papers 2021-04, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2021-04
    as

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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/6283/
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    References listed on IDEAS

    as
    1. Olena Havrylchyk & Marianne Verdier, 2018. "The Financial Intermediation Role of the P2P Lending Platforms," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 60(1), pages 115-130, March.
    2. Boris Vallée & Yao Zeng, 2019. "Marketplace Lending: A New Banking Paradigm?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1939-1982.
    3. Maggie Rong Hu & Xiaoyang Li & Yang Shi, 2019. "Adverse Selection and Credit Certificates: Evidence from a P2P Platform," Working Papers id:13038, eSocialSciences.
    4. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    5. Henry Sauermann & Chiara Franzoni & Kourosh Shafi, 2019. "Crowdfunding scientific research: Descriptive insights and correlates of funding success," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-26, January.
    6. Hu, Maggie Rong & Li, Xiaoyang & Shi, Yang, 2019. "Adverse Selection and Credit Certificates: Evidence from a P2P Platform," ADBI Working Papers 942, Asian Development Bank Institute.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    artificial intelligence; peer-to-peer lending; credit risk assessment; credit scorecards; logistic regression; machine learning;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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