Peer to Peer Lending Risk Analysis: Predictions from Lending Club

Yueqi Gu; Lingqi Guo; Chongyue Ma; Haoyu Wang; Xiaoran Wei

doi:10.2991/978-94-6463-005-3_76

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Peer to Peer Lending Risk Analysis: Predictions from Lending Club

Authors

Yueqi Gu¹^{, *}, Lingqi Guo², Chongyue Ma³, Haoyu Wang⁴, Xiaoran Wei⁵

¹Statistics, North Carolina State University, Raleigh, USA

²Accounting and Business Analytics, McGill University, Montreal, Canada

³Management, University of Miami, Miami, USA

⁴Pure Mathematics, University of California-Irvine, Irvine, USA

⁵Applied Statistics and Mathematical Science, University of Toronto, Toronto, Canada

^*Corresponding author. Email: ygu5@ncsu.edu

Corresponding Author

Yueqi Gu

Available Online 10 November 2022.

DOI: 10.2991/978-94-6463-005-3_76 How to use a DOI?
Keywords: Lending Company; Machine Learning; Risk Analysis; Data Analysis
Abstract: In this study, we use data findings of a lending club, a p2p company, to visualize, categorize, and use statistical techniques as our research method. In the case of statistical techniques, a combination of logistic regression and random forest is mentioned. The study then analyzes the future risk of the company through two aspects of the lending club: the geographical factors of loan origination and the use of loans. Based on the data, we found that the loans that have the potential to become bad loans are the ones that may lead to a high risk for the lending club in the future. Therefore, with the risk analysis obtained from the data, the lending club needs to anticipate the possibility of bad loans and thus avoid these potential risks.
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.

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Volume Title: Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
Series: Atlantis Highlights in Engineering
Publication Date: 10 November 2022
ISBN: 10.2991/978-94-6463-005-3_76
ISSN: 2589-4943
DOI: 10.2991/978-94-6463-005-3_76 How to use a DOI?
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

ris enw bib

TY  - CONF
AU  - Yueqi Gu
AU  - Lingqi Guo
AU  - Chongyue Ma
AU  - Haoyu Wang
AU  - Xiaoran Wei
PY  - 2022
DA  - 2022/11/10
TI  - Peer to Peer Lending Risk Analysis: Predictions from Lending Club
BT  - Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
PB  - Atlantis Press
SP  - 750
EP  - 759
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-005-3_76
DO  - 10.2991/978-94-6463-005-3_76
ID  - Gu2022
ER  -

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