![]() Finally, Section 5 presents the results, and Section 6 discusses the results, limitations, and suggestions for improvement. Section 3 explains the methodologies employed, and Section 4 explores the Lending Club Loan (LCL) dataset used for this study. Section 2 reviews prior studies on default prediction in online P2P lending. We expect that the data-driven latent relationships information between borrowers can improve default risk prediction. ![]() As it is difficult to discover realistic relationship information between borrowers in a P2P landing platform, this study defines the data-driven latent relationships between borrowers in terms of the similarity of their hard and soft information. Therefore, the use of relationship information among borrowers beyond those provided on the P2P platform is necessary. However, online P2P lending faces a significant problem, such as information asymmetry between borrowers and lenders, that is, the reliability of a borrower’s credit is unknown to the lender. These online P2P lending platforms are gaining popularity due to their low operating costs compared with traditional lending programs. Online P2P lending allows individuals to lend money to other individuals through online platforms without the intervention of a financial institution. Our proposed approach is applied to peer-to-peer (P2P) lending. This network is utilized as a spatial weight matrix for a spatial probit model that reflects different degrees of borrowers’ relation for the prediction of a loan default. In this study, we use a borrower relationship network based on the borrowers’ information provided for loan applications. Relationship among loan applicants that are at high risk of default can also provide valuable information for evaluating default risk. While both hard and soft information has been used in most credit scoring models, what is missing is the potential relation among loan applicants. Existing studies have shown that not only hard information but also soft information, which is less relevant to their financial condition, is helpful in predicting default risk. The former directly reflects the loan applicants’ financial status or creditworthiness, while the latter includes those that do not have a direct relationship to the credit applicant’s financial status or creditworthiness such as age or residence. To predict the probability of default of loan applicant that is essential for credit risk management, machine learning models use two types of borrower information: standard “hard” information and nonstandard “soft” information. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors declare no competing interests.Ĭredit risk management is very important for service firms in the lending business. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting information files.įunding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1A2C2005026). Received: SeptemAccepted: DecemPublished: December 31, 2021Ĭopyright: © 2021 Lee, Sohn. PLoS ONE 16(12):Įditor: Elisa Ughetto, Politecnico di Torino, ITALY ![]() Compare Standard and Premium Digital here.Īny changes made can be done at any time and will become effective at the end of the trial period, allowing you to retain full access for 4 weeks, even if you downgrade or cancel.Citation: Lee JW, Sohn SY (2021) Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network. You may also opt to downgrade to Standard Digital, a robust journalistic offering that fulfils many user’s needs. If you’d like to retain your premium access and save 20%, you can opt to pay annually at the end of the trial. If you do nothing, you will be auto-enrolled in our premium digital monthly subscription plan and retain complete access for $69 per month.įor cost savings, you can change your plan at any time online in the “Settings & Account” section. For a full comparison of Standard and Premium Digital, click here.Ĭhange the plan you will roll onto at any time during your trial by visiting the “Settings & Account” section. Premium Digital includes access to our premier business column, Lex, as well as 15 curated newsletters covering key business themes with original, in-depth reporting. Standard Digital includes access to a wealth of global news, analysis and expert opinion. ![]() During your trial you will have complete digital access to FT.com with everything in both of our Standard Digital and Premium Digital packages. ![]()
0 Comments
Leave a Reply. |