A Survey of Machine Learning Methodologies for Loan Evaluation in Peer-to-Peer (P2P) Lending

Yan Wang and Xuelei Sherry Ni ()
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Yan Wang: Kennesaw State University, School of Analytics and Data Science
Xuelei Sherry Ni: Kennesaw State University, School of Analytics and Data Science

A chapter in Data Analytics for Management, Banking and Finance, 2023, pp 1-49 from Springer

Abstract: Abstract In the peer-to-peer (P2P) lending market, borrowers apply for a loan through a virtual platform and get money from investors if they meet certain criteria. Meanwhile, investors lend money to certain borrowers and possibly earn the profit generated by the interest rate. Compared with the traditional banking system, P2P lending has a lower operating cost and a faster approval process, which makes it a major competitor to the traditional banking system. However, P2P loans are not insured so investors need to tolerate the risk of a financial loss if the borrower defaults on the loan. Over the last decade, numerous studies have been carried out to investigate how machine learning techniques can be utilized to help investors evaluate P2P loans, thereby guiding them to make low-risky and highly profitable investment decisions. This chapter intends to first review the development, business model, and various platforms of P2P lending. Then we provide a comprehensive overview of various loan evaluation approaches, with a focus on the application of machine learning methodologies in P2P lending. At the end of the chapter, we discuss the potential challenges that the P2P market may confront in the future to provide research directions for future researchers.

Date: 2023
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DOI: 10.1007/978-3-031-36570-6_1

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