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| Title: | P2P借貸中借款人特徵與貸款表現關係之實證研究：以Lending Club和機器學習方法為例
An Empirical Study of the Relationship between Borrower Characteristics and Loan Performance in Peer-to-Peer Lending: Evidence from Lending Club and Machine Learning Techniques |
| Authors: | 陳槐廷
Chen, Huai-Ting |
| Contributors: | 林士貴
翁久幸
Lin, Shih-Kuei
Weng, Chiu-Hsing
陳槐廷
Chen, Huai-Ting |
| Keywords: | P2P 借貸平台
貸款目的
貸款率
違約狀態
機器學習
P2P platform
Purpose
Funded Ratio
Loan Status
Machine Learning |
| Date: | 2023 |
| Issue Date: | 2023-09-01 14:57:57 (UTC+8) |
| Abstract: | 本研究採用 P2P 平台的資料，相較於過往的文獻僅討論小型企業
貸款，本研究將全面探討各種貸款目的下的貸款表現，並從借款者和
投資者兩種不同角度進行分析，這包括債務整合、小型企業貸款以及
信用卡等貸款類型，最後也透過機器學習的方法建構違約及貸款率
模型。P2P 借貸平台中的借款者和投資者方面的重要變數包括借款金
額、工作年限、年收入、債務收入比、循環信貸餘額等，透過提高借
款人的信用特徵和降低投資者的風險意識，可以促進借款人的貸款
通過率，並增加投資者對借款人的信任程度，在特定貸款目的（如教
育、婚禮等）下，借款金額可能較低，因為這些目的不具備賺錢的能
力，可能會增加投資者的風險意識，最後在預測貸款率及違約狀態模
型中，XGBoost 表現最佳。
This study utilizes data from a P2P platform. In comparison to previous literature
that solely focused on small business loans, this research comprehensively discusses
the loan performance across various loan purposes, exploring them from both borrower and investor perspectives. This includes different types of loans such as debt
consolidation, small business loans, credit card loans, and more. Additionally, machine learning methods are employed to construct Loan Status and Funded Ratio
models. Key variables from the borrower and investor aspects in the P2P lending
platform include loan amount, years of employment, annual income, debt-to-income
ratio, revolving credit balance, among others.By enhancing the credit characteristics
of borrowers and reducing investors’ risk perceptions, it is possible to promote higher
loan approval rates for borrowers and increase investors’ trust in borrowers. For specific loan purposes, such as education or weddings, the loan amounts may be lower
as these purposes may not have revenue-generating potential, which could raise investors’ risk awareness.Finally, in predicting loan rates and default status models,
XGBoost outperformed other methods. |
| Reference: | Chen, D., Lai, F., and Lin, Z. (2014). A trust model for online peer-to-peer lending: a lender’s perspective. Information Technology and Management, 15:239–254.
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794.
Han, J.-T., Chen, Q., Liu, J.-G., Luo, X.-L., and Fan, W. (2018). The persuasion of borrowers’voluntary information in peer to peer lending: An empirical study based on elaboration likelihood model. Computers in Human Behavior, 78:200–214.
Jin, Y. and Zhu, Y. (2015). A data-driven approach to predict default risk of loan for online peer-to-peer (p2p) lending. In 2015 Fifth international conference on communication systems and network technologies, pages 609–613. IEEE.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017).Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
Lin, X., Li, X., and Zheng, Z. (2017). Evaluating borrower’s default risk in peer-to-peerlending: evidence from a lending platform in china. Applied Economics, 49(35):3538–3545.
Ma, X., Sha, J., Wang, D., Yu, Y., Yang, Q., and Niu, X. (2018). Study on a prediction of p2p network loan default based on the machine learning lightgbm and xgboost algorithms according to different high dimensional data cleaning. Electronic Commerce Research and Applications, 31:24–39.
Nowak, A., Ross, A., and Yencha, C. (2018). Small business borrowing and peer-to-peer lending: Evidence from lending club. Contemporary Economic Policy, 36(2):318–336.
Serrano-Cinca, C., Gutiérrez-Nieto, B., and López-Palacios, L. (2015). Determinants of default in p2p lending. PloS one, 10(10):e0139427.
Zhou, J., Li, W., Wang, J., Ding, S., and Xia, C. (2019). Default prediction in p2p lending from high-dimensional data based on machine learning. Physica A: Statistical Mechanics and its Applications, 534:122370. |
| Description: | 碩士
國立政治大學
統計學系
110354029 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110354029 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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