ORCID
Krzysztof Spirzewski 0000-0002-9646-5667
Keywords
artificial intelligence; peer-to-peer lending; credit risk assessment; credit scorecards; logistic regression; machine learning.
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.
Recommended Citation
Klimowicz, A., & Spirzewski, K. (2024). Concept of Peer-to-Peer Lending and Application of Machine Learning in Credit Scoring. Journal of Banking and Financial Economics, 2021(16), 25-55. https://doi.org/10.7172/2353-6845.jbfe.2021.2.2
First Page
25
Last Page
55
Page Count
55
Received Date
29 September 2021
Revised Date
23 November 2021
Accept Date
26 November 2021
Online Available Date
20 December 2021
DOI
10.7172/2353-6845.jbfe.2021.2.2
JEL Code
G21; C25
Publisher
University of Warsaw