Risk Management in P2P Lending Markets

DOI

10.2991/978-94-6463-298-9_44How to use a DOI?

Keywords

P2P lending; Prosper platform; risk preferences

Abstract

P2P lending has garnered considerable attention and utilization owing to its minimal entry barriers, reduced expenses, and enhanced efficiency compared to conventional financial institutions. However, alongside its popularity, P2P lending markets also encounter risks, with personal credit risk emerging as the most salient concern. This study aims to explore the determinants that influence borrower risk preferences through the examination of loan data obtained from the Prosper platform.

Using a fixed-effects regression model, this study examines the relationship between key variables and risk preferences. Temporal factors, especially weekdays and weekends, are found to be important factors influencing investor risk preferences. Additionally, this study utilizes machine learning algorithms to develop a default risk prediction model in P2P lending. Through rigorous comparative analysis and experiments, the random forest model demonstrates robust predictive capabilities. Furthermore, a combined learning model utilizing voting and bagging techniques is constructed by integrating random forest, linear regression, and Xgboost models. This ensemble model provides auxiliary support for P2P lending platforms in recommending investable orders to investors.

The findings of this study provide valuable insights into risk management within P2P lending markets, particularly in terms of borrower risk preferences and the utilization of machine learning algorithms for risk prediction. The knowledge acquired from examining loan data from the Prosper platform carries practical implications for P2P lending platforms and risk management practitioners seeking to enhance risk assessment and control strategies.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Jialun Lyu
PY  - 2023
DA  - 2023/11/30
TI  - Risk Management in P2P Lending Markets
BT  - Proceedings of the 2023 International Conference on Finance, Trade and Business Management (FTBM 2023)
PB  - Atlantis Press
SP  - 403
EP  - 410
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-298-9_44
DO  - 10.2991/978-94-6463-298-9_44
ID  - Lyu2023
ER  -