{"title":"基于机器学习算法的贷款逾期预测","authors":"S. Xu, Peng Zhang","doi":"10.1109/ISCEIC53685.2021.00043","DOIUrl":null,"url":null,"abstract":"With the development of the financial Internet, analyzing the repayment ability and willingness of loan objects has become a key link. This paper uses the loan overdue data set of the TianChi platform to predict whether the borrower is in default or not. Firstly, Feature engineering is used to get the useful features for training. Then this paper compares the performance of five machine learning algorithms on predicting the loan overdue. The results show that the LightGBM model has the best performance and stability.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Prediction of Loan Overdue Based On Machine Learning Algorithm\",\"authors\":\"S. Xu, Peng Zhang\",\"doi\":\"10.1109/ISCEIC53685.2021.00043\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the development of the financial Internet, analyzing the repayment ability and willingness of loan objects has become a key link. This paper uses the loan overdue data set of the TianChi platform to predict whether the borrower is in default or not. Firstly, Feature engineering is used to get the useful features for training. Then this paper compares the performance of five machine learning algorithms on predicting the loan overdue. The results show that the LightGBM model has the best performance and stability.\",\"PeriodicalId\":342968,\"journal\":{\"name\":\"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)\",\"volume\":\"220 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCEIC53685.2021.00043\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCEIC53685.2021.00043","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of Loan Overdue Based On Machine Learning Algorithm
With the development of the financial Internet, analyzing the repayment ability and willingness of loan objects has become a key link. This paper uses the loan overdue data set of the TianChi platform to predict whether the borrower is in default or not. Firstly, Feature engineering is used to get the useful features for training. Then this paper compares the performance of five machine learning algorithms on predicting the loan overdue. The results show that the LightGBM model has the best performance and stability.