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Service Optimization of p2p Online Loan Platform Based on Big Data Analysis

In: Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023)

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  • Tiantong Yang

    (Shanghai Jiaotong University, School of Finance)

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.

Suggested Citation

  • Tiantong Yang, 2024. "Service Optimization of p2p Online Loan Platform Based on Big Data Analysis," Advances in Economics, Business and Management Research, in: Faruk Balli & Hui Nee Au Yong & Sikandar Ali Qalati & Ziqiang Zeng (ed.), Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023), pages 115-122, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-268-2_15
    DOI: 10.2991/978-94-6463-268-2_15
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