This is an outdated version published on 2023-11-23. Read the most recent version.

Prediction of default risk in P2P loans

Authors

  • Kamil Dawid Grzebień Complutense University of Madrid
  • María Jesús Segovia-Vargas Complutense University of Madrid

DOI:

https://doi.org/10.32826/reyf.v1i2.352

Keywords:

P2P lending, Loan default prediction, Credit risk, Machine learning

Abstract

This work uses four machine learning algorithms to predict the probability of loan default on the Lending Club platform. Data preprocessing techniques and a historical loan database including various information such as amount borrowed, loan term, and whether or not a default occurred were used. The results showed that the Random Forest model performs better in predicting defaults compared to logistic regression, multilayer perceptron and C4.5 classification tree. This work demonstrates the potential of machine learning algorithms to predict loan default in peer-to-peer platforms such as Lending Club. An effective use of these tools could help improve credit risk management and avoid potential losses.

References

Ariza-Garzón, M. J., Camacho Miñano, M. M., Segovia-Vargas, M. J., Arroyo, J., 2021. Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions. Elsevier, 49. https://doi.org/10.1016/j.elerap.2021.101079

Ariza-Garzón, M. J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.-J., 2020. Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending. IEEE Access (8), 64873-64890. https://doi.org/10.1109/ACCESS.2020.2984412

Breiman, L., 2001. Random Forests. Machine Learning (45), 5–32. https://doi.org/10.1023/A:1010933404324

Frost, J., Gambacorta, L., Huang, Y., Shin, H. S., Zbinden, P., 2019. BigTech and the changing structure of financial Intermediation: BIS Quarterly Review. Bank for International Settlements. https://www.bis.org/publ/work779.pdf

Kohavi, R., Provost, F., 1998. Glossary of terms. Machine Learning - Special Issue on Applications of Machine Learning and the Knowledge Discovery Process. Machine Learning (30), 271-274. https://doi.org/10.1023/A:1017181826899

Lending Club, 2022. Third Quarter 2022 Results. Lending Club. https://ir.lendingclub.com/news/news-details/2022/LendingClub-Reports-Third-Quarter-2022-Results/default.aspx

Li, P., Han, G., 2018. LendingClub Loan Default and Profitability Prediction: CS 229 projects. Stanford University. https://cs229.stanford.edu/proj2018/report/69.pdf

López, R. F., Fernández, J. M., 2008. Las redes neuronales artificiales. Netbiblo.

Lukason, O., Vissak, T., Segovia-Vargas, M. J., 2021. How Does Managerial Experience Predict the Internationalization Type of a Young Firm? IEEE Access (9), 18148-18166. https://doi.org/10.1109/ACCESS.2021.3054116

Peng, J., Lee, K.,, Ingersoll, G., 2002. An Introduction to Logistic Regression Analysis and Reporting. Journal of Educational Research - J EDUC RES (96), 3-14. https://doi.org/10.1080/00220670209598786

Quinlan, J. R., 1986. Induction of decision trees. Machine Learning (1), 81-106. https://doi.org/10.1007/BF00116251

Quinlan, J. R., 1994. C4.5: Programs for Machine Learning. Machine Learning (16), 235-240. https://doi.org/https://doi.org/10.1007/BF00993309

Reuters, 2019. China gives P2P lenders two years to exit industry. Reuters: https://www.reuters.com/article/us-china-p2p-idUSKBN1Y2039

Serrano-Cinca, C., Gutiérrez-Nieto, B., López-Palacios, L., 2015. Determinants of Default in P2P Lending. PLoS ONE (10). https://doi.org/10.1371/journal.pone.0139427

Tomek, I., 1976. Two modifications of CNN. IEEE Transactions on Systems, Man and Cybernetics (11), 769-772. https://doi.org/10.1109/TSMC.1976.4309452

Vujovic, Z., 2021. Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications (12), 599-606. https://doi.org/10.14569/IJACSA.2021.0120670

Werbos, P. J., 1974. Beyond regression: New tools for prediction and analysis in the behavioral sciences: Tesis doctoral, en Harvard University.

Ziegler, T., Shneor, R., Wenzlaff, K., Suresh, K., Ferri de Camargo Paes, F., Mammadova, L., Wanga, C., Kekre, N., Mutinda, S., Wang, B. W., López Closs, C., Zhang, B., Forbes, H., Soki, E., Alam, N., Knaup, C., 2020. The 2nd Global Alternative Finance Market Benchmarking Report. Cambridge Centre for Alternative Finance. https://www.jbs.cam.ac.uk/wp-content/uploads/2021/06/ccaf-2021-06-report-2nd-global-alternative-finance-benchmarking-study-report.pdf

Downloads

Published

2023-11-23

Versions

Issue

Vol. 1 No. 2 (2023): Revista de Economía y Finanzas (May - August 2023)

Section

Articles

License

Those authors who have publications with this journal accept the following terms:

  1. The authors will retain their copyright and guarantee the journal the right of first publication of their work, which will be simultaneously subject to the Creative Commons Recognition License that allows third parties to share the work whenever its author is indicated and its first publication is this magazine.
  2. Authors may adopt other non-exclusive licensing agreements for the distribution of the published work (eg, deposit it in an institutional telematic file or publish it in a monographic volume) whenever the initial publication is indicated in this magazine.
  3. Authors are allowed and advised to disseminate their work through the Internet (eg in institutional telematic files or on their website) before and during the submission process, which can produce interesting exchanges and increase the citations of the published work. (See The effect of open access ).