{"title":"A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes, The IFR² Framework","authors":"Mieke Jans, N. Lybaert, K. Vanhoof","doi":"10.4192/1577-8517-V9_1","DOIUrl":null,"url":null,"abstract":"Fraud is a million dollar business and it is increa sing every year. Both internal and external fraud present a substantial cost to ou r economy worldwide. A review of the academic literature learns that the academic commun ity only addresses external fraud and how to detect this type of fraud. Little or no effo rt to our knowledge has been put in investigating how to prevent and to detect internal fraud, which we call 'internal fraud risk reduction'. Taking together the urge for research i n internal fraud and the lack of it in academic literature, research to reduce internal fr aud risk is pivotal. Only after having a framework in which to implement empirical research, this topic can further be investigated. In this paper we present the IFR≤ framework, deduced from both the academic literature and from current business practices, where the core of this framework suggests to use a data mining approach.","PeriodicalId":404481,"journal":{"name":"The International Journal of Digital Accounting Research","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"50","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The International Journal of Digital Accounting Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4192/1577-8517-V9_1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 50
Abstract
Fraud is a million dollar business and it is increa sing every year. Both internal and external fraud present a substantial cost to ou r economy worldwide. A review of the academic literature learns that the academic commun ity only addresses external fraud and how to detect this type of fraud. Little or no effo rt to our knowledge has been put in investigating how to prevent and to detect internal fraud, which we call 'internal fraud risk reduction'. Taking together the urge for research i n internal fraud and the lack of it in academic literature, research to reduce internal fr aud risk is pivotal. Only after having a framework in which to implement empirical research, this topic can further be investigated. In this paper we present the IFR≤ framework, deduced from both the academic literature and from current business practices, where the core of this framework suggests to use a data mining approach.