Service Optimization of p2p Online Loan Platform Based on Big Data Analysis
- DOI
- 10.2991/978-94-6463-268-2_15How to use a DOI?
- Keywords
- P2P lending platform; loan completion improvement; machine learning algorithm; linear regression model
- Abstract
In view of the credit risk loss brought by incomplete loan transactions to the online P2P lending platform, based on the data set of Prosper Company, this paper, on the one hand, establishes machine learning models such as logistic regression, decision tree, random forest, etc. to predict whether the loan application can be completed, so as to optimize the ranking recommendation logic of the platform, and put forward suggestions according to the borrower’s situation to reduce the ultimate credit risk; on the other hand, formulates the OLS linear regression model, so that through exploratory analysis of loan data and coefficient analysis of the regression model, important characteristics highly related to loan default are obtained, including total income, occupation type, working life, debt-to-income ratio, loan amount, loan term, etc., which helps the platform to better identify valuable potential customers.
- Copyright
- © 2024 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.
Cite this article
TY - CONF AU - Tiantong Yang PY - 2023 DA - 2023/10/10 TI - Service Optimization of p2p Online Loan Platform Based on Big Data Analysis BT - Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023) PB - Atlantis Press SP - 115 EP - 122 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-268-2_15 DO - 10.2991/978-94-6463-268-2_15 ID - Yang2023 ER -