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題名 Machine learning and artificial neural networks to construct P2P lending credit-scoring model: A case using Lending Club data
作者 蔡瑞煌
Tsaih, Rua-Huan;Chang, An-Hsing;Yang, Li-Kai;Lin, Shih-Kuei
貢獻者 資管系
關鍵詞 P2P lending; credit score; machine learning; artificial neural networks; feature engineering; Lending Club
日期 2022-06
上傳時間 29-Jan-2024 09:12:18 (UTC+8)
摘要 In this study, we constructed the credit-scoring model of P2P loans by using several machine learning and artificial neural network (ANN) methods, including logistic regression (LR), a support vector machine, a decision tree, random forest, XGBoost, LightGBM and 2-layer neural networks. This study explores several hyperparameter settings for each method by performing a grid search and cross-validation to get the most suitable credit-scoring model in terms of training time and test performance. In this study, we get and clean the open P2P loan data from Lending Club with feature engineering concepts. In order to find significant default factors, we used an XGBoost method to pre-train all data and get the feature importance. The 16 selected features can provide economic implications for research about default prediction in P2P loans. Besides, the empirical result shows that gradient-boosting decision tree methods, including XGBoost and LightGBM, outperform ANN and LR methods, which are commonly used for traditional credit scoring. Among all of the methods, XGBoost performed the best.
關聯 Quantitative Finance and Economics, Vol.6, No.2, pp.303-325
資料類型 article
DOI https://doi.org/10.3934/QFE.2022013
dc.contributor 資管系
dc.creator (作者) 蔡瑞煌
dc.creator (作者) Tsaih, Rua-Huan;Chang, An-Hsing;Yang, Li-Kai;Lin, Shih-Kuei
dc.date (日期) 2022-06
dc.date.accessioned 29-Jan-2024 09:12:18 (UTC+8)-
dc.date.available 29-Jan-2024 09:12:18 (UTC+8)-
dc.date.issued (上傳時間) 29-Jan-2024 09:12:18 (UTC+8)-
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=168928-
dc.description.abstract (摘要) In this study, we constructed the credit-scoring model of P2P loans by using several machine learning and artificial neural network (ANN) methods, including logistic regression (LR), a support vector machine, a decision tree, random forest, XGBoost, LightGBM and 2-layer neural networks. This study explores several hyperparameter settings for each method by performing a grid search and cross-validation to get the most suitable credit-scoring model in terms of training time and test performance. In this study, we get and clean the open P2P loan data from Lending Club with feature engineering concepts. In order to find significant default factors, we used an XGBoost method to pre-train all data and get the feature importance. The 16 selected features can provide economic implications for research about default prediction in P2P loans. Besides, the empirical result shows that gradient-boosting decision tree methods, including XGBoost and LightGBM, outperform ANN and LR methods, which are commonly used for traditional credit scoring. Among all of the methods, XGBoost performed the best.
dc.format.extent 99 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Quantitative Finance and Economics, Vol.6, No.2, pp.303-325
dc.subject (關鍵詞) P2P lending; credit score; machine learning; artificial neural networks; feature engineering; Lending Club
dc.title (題名) Machine learning and artificial neural networks to construct P2P lending credit-scoring model: A case using Lending Club data
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.3934/QFE.2022013
dc.doi.uri (DOI) https://doi.org/10.3934/QFE.2022013