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AuthorAmania, Farzaneh A.
AuthorFadlallab, Adam M.
Available date2021-02-08T09:14:53Z
Publication Date2017
Publication NameInternational Journal of Accounting Information Systems
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.accinf.2016.12.004
URIhttp://hdl.handle.net/10576/17600
AbstractThis paper explores the applications of data mining techniques in accounting and proposes an organizing framework for these applications. A large body of literature reported on specific uses of the important data mining paradigm in accounting, but research that takes a holistic view of these uses is lacking. To organize the literature on the applications of data mining in accounting, we create a framework that combines the two well-known accounting reporting perspectives (retrospection and prospection), and the three well-accepted goals of data mining (description, prediction, and prescription). The framework encapsulates a taxonomy of four categories (retrospective-descriptive, retrospective-prescriptive, prospective-prescriptive, and prospective-predictive) of data mining applications in accounting. The proposed framework revealed that the area of accounting that benefited the most from data mining is assurance and compliance, including fraud detection, business health and forensic accounting. The clear gaps seem to be in the two prescriptive application categories (retrospective-prescriptive and prospective-prescriptive), indicating opportunities for benefiting from data mining in these application categories. The framework presents a holistic view of the literature and systematically organizes it in a structurally logical and thematically coherent manner.
Languageen
PublisherElsevier Inc.
SubjectAccounting
Data mining
Framework
Literature review
Prospective
Retrospective
TitleData mining applications in accounting: A review of the literature and organizing framework
TypeArticle
Pagination32-58
Volume Number24
dc.accessType Abstract Only


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