{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:18:21Z","timestamp":1774023501307,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Peer-to-peer lending (P2P lending) has proliferated in recent years thanks to Fintech and big data advancements. However, P2P lending platforms are not tightly governed by relevant laws yet, as their development speed has far exceeded that of regulations. Therefore, P2P lending operations are still subject to risks. This paper proposes prediction models to mitigate the risks of default and asymmetric information on P2P lending platforms. Specifically, we designed sophisticated procedures to pre-process mass data extracted from Lending Club in 2018 Q3\u20132019 Q2. After that, three statistical models, namely, Logistic Regression, Bayesian Classifier, and Linear Discriminant Analysis (LDA), and five AI models, namely, Decision Tree, Random Forest, LightGBM, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN), were utilized for data analysis. The loan statuses of Lending Club\u2019s customers were rationally classified. To evaluate the models, we adopted the confusion matrix series of metrics, AUC-ROC curve, Kolmogorov\u2013Smirnov chart (KS), and Student\u2019s t-test. Empirical studies show that LightGBM produces the best performance and is 2.91% more accurate than the other models, resulting in a revenue improvement of nearly USD 24 million for Lending Club. Student\u2019s t-test proves that the differences between models are statistically significant.<\/jats:p>","DOI":"10.3390\/e24060801","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:22:39Z","timestamp":1654820559000},"page":"801","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["P2P Lending Default Prediction Based on AI and Statistical Models"],"prefix":"10.3390","volume":"24","author":[{"given":"Po-Chang","family":"Ko","sequence":"first","affiliation":[{"name":"Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan"},{"name":"AI Fintech Center, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan"}]},{"given":"Ping-Chen","family":"Lin","sequence":"additional","affiliation":[{"name":"AI Fintech Center, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan"},{"name":"Department of Finance and Information, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9742-4456","authenticated-orcid":false,"given":"Hoang-Thu","family":"Do","sequence":"additional","affiliation":[{"name":"Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan"},{"name":"The Faculty of E-Commerce, University of Economics, The University of Danang, Danang 550000, Vietnam"}]},{"given":"You-Fu","family":"Huang","sequence":"additional","affiliation":[{"name":"AI Fintech Center, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1017\/S1867299X00010126","article-title":"Peer-to-peer lending: Opportunities and risks","volume":"7","author":"Lenz","year":"2016","journal-title":"Eur. 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