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How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments

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  • Fitzpatrick, Trevor
  • Mues, Christophe

Abstract

Successful Peer-to-Peer (P2P) lending requires an evaluation of loan profitability from a large universe of loans. Predictions of loan profitability may be useful to rank potential investments. We investigate whether various types of prediction methods and the types of information contained in loan listing features matter for profitable investment. A range of methods and performance metrics are used to benchmark predictive performance, based on a large dataset of P2P loans issued on Lending Club. Robust linear mixed models are used to investigate performance differences between models, according to whether they assume linearity, whether they build ensembles, and which types of predictors they use. The main findings are that: linear methods perform surprisingly well on several (but not all) criteria; whether ensemble methods perform better than individual methods is measure dependent; the use of alternative text-based information does not improve profit scoring outcomes. We conclude that P2P lenders could potentially increase their investment returns by applying linear methods that directly predict the internal rate of return instead of other dependent variables such as loan default.

Suggested Citation

  • Fitzpatrick, Trevor & Mues, Christophe, 2021. "How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments," European Journal of Operational Research, Elsevier, vol. 294(2), pages 711-722.
  • Handle: RePEc:eee:ejores:v:294:y:2021:i:2:p:711-722
    DOI: 10.1016/j.ejor.2021.01.047
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    1. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    2. Sha, Yezhou, 2022. "Rating manipulation and creditworthiness for platform economy: Evidence from peer-to-peer lending," International Review of Financial Analysis, Elsevier, vol. 84(C).
    3. Ligang Zhou & Chao Ma, 2023. "A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1481-1504, December.
    4. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    5. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    6. Krivorotov, George, 2023. "Machine learning-based profit modeling for credit card underwriting - implications for credit risk," Journal of Banking & Finance, Elsevier, vol. 149(C).
    7. Mahsa Tavakoli & Rohitash Chandra & Fengrui Tian & Cristi'an Bravo, 2023. "Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams," Papers 2304.10740, arXiv.org, revised Nov 2024.

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