{"title":"机器学习方法:存款保险的潜力","authors":"Ryan Defina","doi":"10.2139/ssrn.3887337","DOIUrl":null,"url":null,"abstract":"The field of deposit insurance is yet to realise fully the potential of machine learning, and the substantial benefits that it may present to its operational and policy-oriented activities. There are practical opportunities available (some specified in this paper) that can assist in improving deposit insurances’ relationship with the technology. Sharing of experiences and learnings via international engagement and collaboration is fundamental in developing global best practices in this space.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Methods: Potential for Deposit Insurance\",\"authors\":\"Ryan Defina\",\"doi\":\"10.2139/ssrn.3887337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The field of deposit insurance is yet to realise fully the potential of machine learning, and the substantial benefits that it may present to its operational and policy-oriented activities. There are practical opportunities available (some specified in this paper) that can assist in improving deposit insurances’ relationship with the technology. Sharing of experiences and learnings via international engagement and collaboration is fundamental in developing global best practices in this space.\",\"PeriodicalId\":331807,\"journal\":{\"name\":\"Banking & Insurance eJournal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Banking & Insurance eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3887337\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Banking & Insurance eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3887337","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine Learning Methods: Potential for Deposit Insurance
The field of deposit insurance is yet to realise fully the potential of machine learning, and the substantial benefits that it may present to its operational and policy-oriented activities. There are practical opportunities available (some specified in this paper) that can assist in improving deposit insurances’ relationship with the technology. Sharing of experiences and learnings via international engagement and collaboration is fundamental in developing global best practices in this space.
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