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Peer-To-Peer Lending: Classification in the Loan Application Process

Author

Listed:
  • Xinyuan Wei

    (School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China
    Risk Management and Financial Engineering Lab, Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, Gainesville, FL 32611, USA)

  • Jun-ya Gotoh

    (Department of Industrial and Systems Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan)

  • Stan Uryasev

    (Risk Management and Financial Engineering Lab, Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, Gainesville, FL 32611, USA)

Abstract

This paper studies the peer-to-peer lending and loan application processing of LendingClub. We tried to reproduce the existing loan application processing algorithm and find features used in this process. Loan application processing is considered a binary classification problem. We used the area under the ROC curve (AUC) for evaluation of algorithms. Features were transformed with splines for improving the performance of algorithms. We considered three classification algorithms: logistic regression, buffered AUC (bAUC) maximization, and AUC maximization.With only three features, Debt-to-Income Ratio, Employment Length, and Risk Score, we obtained an AUC close to 1. We have done both in-sample and out-of-sample evaluations. The codes for cross-validation and solving problems in a Portfolio Safeguard (PSG) format are in the Appendix. The calculation results with the data and codes are posted on the website and are available for downloading.

Suggested Citation

  • Xinyuan Wei & Jun-ya Gotoh & Stan Uryasev, 2018. "Peer-To-Peer Lending: Classification in the Loan Application Process," Risks, MDPI, vol. 6(4), pages 1-17, November.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:4:p:129-:d:181849
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    References listed on IDEAS

    as
    1. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    2. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    3. Khwaja, Asim Ijaz & Iyer, Rajkamal & Luttmer, Erzo F.P. & Shue, Kelly, 2009. "Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?," Scholarly Articles 4448882, Harvard Kennedy School of Government.
    4. Justin R. Davis & Stan Uryasev, 2016. "Analysis of tropical storm damage using buffered probability of exceedance," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 465-483, August.
    5. Danjue Shang & Victor Kuzmenko & Stan Uryasev, 2018. "Cash flow matching with risks controlled by buffered probability of exceedance and conditional value-at-risk," Annals of Operations Research, Springer, vol. 260(1), pages 501-514, January.
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    Cited by:

    1. Katsuhiro Tanaka & Rei Yamamoto, 2023. "Ellipsoidal buffered area under the curve maximization model with variable selection in credit risk estimation," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.

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