Concept of Peer-to-Peer Lending and Application of Machine Learning in Credit Scoring

Aleksy Klimowicz () and Krzysztof Spirzewski ()
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Aleksy Klimowicz: University of Warsaw, Faculty of Economic Sciences
Krzysztof Spirzewski: University of Warsaw, Faculty of Economic Sciences

Journal of Banking and Financial Economics, 2021, vol. 2, issue 16, 25-55

Abstract: Numerous applications of AI are found in the banking sector. Starting from the front-office, enhancing customer recognition and personalized services, continuing in the middle-office with automated fraud-detection systems, ending with the 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 in applying a machine learning algorithm in building a credit scorecard, namely logistic regression.

Keywords: artificial intelligence; peer-to-peer lending; credit risk assessment; credit scorecards; logistic regression; machine learning (search for similar items in EconPapers)
JEL-codes: C25 G21 (search for similar items in EconPapers)
Date: 2021
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