{"title":"贝叶斯预测数据泄露 IBNR 事件","authors":"Maochao Xu, Hong Sun, Peng Zhao","doi":"arxiv-2407.18377","DOIUrl":null,"url":null,"abstract":"The reporting delay in data breach incidents poses a formidable challenge for\nIncurred But Not Reported (IBNR) studies, complicating reserve estimation for\nactuarial professionals. This work presents a novel Bayesian nowcasting model\ndesigned to accurately model and predict the number of IBNR data breach\nincidents. Leveraging a Bayesian modeling framework, the model integrates time\nand heterogeneous effects to enhance predictive accuracy. Synthetic and\nempirical studies demonstrate the superior performance of the proposed model,\nhighlighting its efficacy in addressing the complexities of IBNR estimation.\nFurthermore, we examine reserve estimation for IBNR incidents using the\nproposed model, shedding light on its implications for actuarial practice.","PeriodicalId":501172,"journal":{"name":"arXiv - STAT - Applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian Nowcasting Data Breach IBNR Incidents\",\"authors\":\"Maochao Xu, Hong Sun, Peng Zhao\",\"doi\":\"arxiv-2407.18377\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The reporting delay in data breach incidents poses a formidable challenge for\\nIncurred But Not Reported (IBNR) studies, complicating reserve estimation for\\nactuarial professionals. This work presents a novel Bayesian nowcasting model\\ndesigned to accurately model and predict the number of IBNR data breach\\nincidents. Leveraging a Bayesian modeling framework, the model integrates time\\nand heterogeneous effects to enhance predictive accuracy. Synthetic and\\nempirical studies demonstrate the superior performance of the proposed model,\\nhighlighting its efficacy in addressing the complexities of IBNR estimation.\\nFurthermore, we examine reserve estimation for IBNR incidents using the\\nproposed model, shedding light on its implications for actuarial practice.\",\"PeriodicalId\":501172,\"journal\":{\"name\":\"arXiv - STAT - Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2407.18377\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.18377","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The reporting delay in data breach incidents poses a formidable challenge for
Incurred But Not Reported (IBNR) studies, complicating reserve estimation for
actuarial professionals. This work presents a novel Bayesian nowcasting model
designed to accurately model and predict the number of IBNR data breach
incidents. Leveraging a Bayesian modeling framework, the model integrates time
and heterogeneous effects to enhance predictive accuracy. Synthetic and
empirical studies demonstrate the superior performance of the proposed model,
highlighting its efficacy in addressing the complexities of IBNR estimation.
Furthermore, we examine reserve estimation for IBNR incidents using the
proposed model, shedding light on its implications for actuarial practice.