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

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\n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\n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\n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\n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\n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\n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\n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\n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\n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\n型中，XGBoost 表現最佳。

This study utilizes data from a P2P platform. In comparison to previous literature\nthat solely focused on small business loans, this research comprehensively discusses\nthe loan performance across various loan purposes, exploring them from both borrower and investor perspectives. This includes different types of loans such as debt\nconsolidation, small business loans, credit card loans, and more. Additionally, machine learning methods are employed to construct Loan Status and Funded Ratio\nmodels. Key variables from the borrower and investor aspects in the P2P lending\nplatform include loan amount, years of employment, annual income, debt-to-income\nratio, revolving credit balance, among others.By enhancing the credit characteristics\nof borrowers and reducing investors’ risk perceptions, it is possible to promote higher\nloan 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\nas these purposes may not have revenue-generating potential, which could raise investors’ risk awareness.Finally, in predicting loan rates and default status models,\nXGBoost outperformed other methods.