点对点借贷中消费者信用风险模型的比较分析

Lua Thi Trinh
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引用次数: 0

摘要

本文旨在比较以下九种不同的消费者信用风险评估模型:Logistic Regression (LR)、Naive Bayes (NB)、Linear Discriminant Analysis (LDA)、k-Nearest Neighbor (k-NN)、Support Vector Machine (SVM)、Classification and Regression Tree (CART)、Artificial Neural Network (ANN)、Random Forest (RF) 和 Gradient Boosting Decision Tree (GBDT) 。作者利用 P2P Lending Club(LC)的数据,评估了各种分类模型在不同经济形势下的效率,并通过三个系列的评价指标比较了 P2P 借贷中信用风险模型的排名结果。研究结果研究结果表明,与困难的 2007-2012 年相比,2013-2019 年经济时期的风险分类模型显示出更高的衡量效率。此外,预测违约风险的排序模型结果显示,GBDT 是本研究中大多数指标或指标族的最佳模型。本研究的结果也支持 Tsai 等人(2014 年)以及 Teplý 和 Polena(2019 年)的研究结果,即 LR、ANN 和 LDA 模型能相当稳定、准确地对贷款申请进行分类,而 CART、k-NN 和 NB 在预测 P2P 贷款数据中借款人的违约风险时表现最差。原创性/价值该研究对实证文献综述的主要贡献包括:通过回顾当前经济形势和平台发展两个时期,比较了通过统计和机器学习算法评估的九种消费贷款申请风险预测模型,这些模型的性能指标按照三个独立的指标系列(阈值、排名和概率指标)进行评估,符合 LC 借贷平台的现有数据特征。
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A comparative analysis of consumer credit risk models in Peer-to-Peer Lending
PurposeThe purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.Design/methodology/approachThe author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.FindingsThe results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.Originality/valueThe main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.
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来源期刊
Journal of Economics, Finance and Administrative Science
Journal of Economics, Finance and Administrative Science Economics, Econometrics and Finance-Economics, Econometrics and Finance (all)
CiteScore
5.10
自引率
20.80%
发文量
23
审稿时长
12 weeks
期刊介绍: The Universidad ESAN, with more than 50 years of experience in the higher education field and post graduate studies, desires to contribute to the academic community with the most outstanding pieces of research. We gratefully welcome suggestions and contributions from business areas such as operations, supply chain, economics, finance and administration. We publish twice a year, six articles for each issue.
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