Xiaoming Zhou, W. Qian, Baifu Huang, Bo Xu, Anlong Su, Wangli Cui, Dawei Wang, Xiaolan Li, Fangyuan Xu
{"title":"公用事业风险索引——个人对个人(P2P)借贷公用事业的案例","authors":"Xiaoming Zhou, W. Qian, Baifu Huang, Bo Xu, Anlong Su, Wangli Cui, Dawei Wang, Xiaolan Li, Fangyuan Xu","doi":"10.1109/icmlc.2018.8527040","DOIUrl":null,"url":null,"abstract":"P2P online loan platform is a new product with the development of Internet finance. Its new financial way can bring the participant both abundant rewards and high risk. Therefore, there are many risk assessing companies springing up. But common companies just forecast if a P2P online loan platform will be absconding according some characteristics and they can't explain the reason why the platform will be absconding in the future. Instead of the directly forecasting the future status of a platform, the risk of a platform absconding is more objective and the decision makers can completely consider a platform's interest, risk and other factors to make their own decision. After all, 51% risk of absconding is not the same as 91% risk of absconding, when the investors are thinking. In this model, the almost features of platforms including absconding and non-absconding will be considered and be disposed according to the type of the data. The feature candidates will be selected by different angles and methods. The forecast model will calculate risks using the input feature and there is a index in this paper to appraise how the model is good.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Utilities Risk Indexing --- A Case for Person-to-Person (P2P) Debit and Credit Utilities\",\"authors\":\"Xiaoming Zhou, W. Qian, Baifu Huang, Bo Xu, Anlong Su, Wangli Cui, Dawei Wang, Xiaolan Li, Fangyuan Xu\",\"doi\":\"10.1109/icmlc.2018.8527040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"P2P online loan platform is a new product with the development of Internet finance. Its new financial way can bring the participant both abundant rewards and high risk. Therefore, there are many risk assessing companies springing up. But common companies just forecast if a P2P online loan platform will be absconding according some characteristics and they can't explain the reason why the platform will be absconding in the future. Instead of the directly forecasting the future status of a platform, the risk of a platform absconding is more objective and the decision makers can completely consider a platform's interest, risk and other factors to make their own decision. After all, 51% risk of absconding is not the same as 91% risk of absconding, when the investors are thinking. In this model, the almost features of platforms including absconding and non-absconding will be considered and be disposed according to the type of the data. The feature candidates will be selected by different angles and methods. The forecast model will calculate risks using the input feature and there is a index in this paper to appraise how the model is good.\",\"PeriodicalId\":309312,\"journal\":{\"name\":\"2018 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"volume\":\"166 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icmlc.2018.8527040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icmlc.2018.8527040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Utilities Risk Indexing --- A Case for Person-to-Person (P2P) Debit and Credit Utilities
P2P online loan platform is a new product with the development of Internet finance. Its new financial way can bring the participant both abundant rewards and high risk. Therefore, there are many risk assessing companies springing up. But common companies just forecast if a P2P online loan platform will be absconding according some characteristics and they can't explain the reason why the platform will be absconding in the future. Instead of the directly forecasting the future status of a platform, the risk of a platform absconding is more objective and the decision makers can completely consider a platform's interest, risk and other factors to make their own decision. After all, 51% risk of absconding is not the same as 91% risk of absconding, when the investors are thinking. In this model, the almost features of platforms including absconding and non-absconding will be considered and be disposed according to the type of the data. The feature candidates will be selected by different angles and methods. The forecast model will calculate risks using the input feature and there is a index in this paper to appraise how the model is good.