{"title":"P2P贷款互联网平台风险量化模型研究","authors":"Fangqiang Zhong, Min Tu, Zhen Wang","doi":"10.1109/ECICE55674.2022.10042863","DOIUrl":null,"url":null,"abstract":"This study aims to quantify the risk of each major P2P lending platform. The special feature is defined based on the “user comments” text data of the platforms from the lender with the combined Word2Vec keyword extraction technology. A quantitative model of an online lending platform is proposed with the feature. The results show that the model more accurately explores the loan Internet platform with high similarity with higher accuracy in the quantitative calculation of VaR.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Risk Quantification Model of P2P Loan Internet Platform\",\"authors\":\"Fangqiang Zhong, Min Tu, Zhen Wang\",\"doi\":\"10.1109/ECICE55674.2022.10042863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aims to quantify the risk of each major P2P lending platform. The special feature is defined based on the “user comments” text data of the platforms from the lender with the combined Word2Vec keyword extraction technology. A quantitative model of an online lending platform is proposed with the feature. The results show that the model more accurately explores the loan Internet platform with high similarity with higher accuracy in the quantitative calculation of VaR.\",\"PeriodicalId\":282635,\"journal\":{\"name\":\"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECICE55674.2022.10042863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECICE55674.2022.10042863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Risk Quantification Model of P2P Loan Internet Platform
This study aims to quantify the risk of each major P2P lending platform. The special feature is defined based on the “user comments” text data of the platforms from the lender with the combined Word2Vec keyword extraction technology. A quantitative model of an online lending platform is proposed with the feature. The results show that the model more accurately explores the loan Internet platform with high similarity with higher accuracy in the quantitative calculation of VaR.
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