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Data mining applications in accounting: A review of the literature and organizing framework
This 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...
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Published in: | International journal of accounting information systems 2017-02, Vol.24, p.32-58 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This 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.
•An organizing framework that combines the two main accounting reporting perspectives and the three major goals of data mining.•A taxonomy of four categories of data mining applications in accounting.•The lion’s share of accounting data mining applications goes to assurance and compliance.•The gaps in prescriptive applications indicate opportunities for benefiting further from data mining in these areas.•Accounting DM focuses on compliance, with prediction as main goal, classification as foremost task, NN as prime technique. |
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ISSN: | 1467-0895 1873-4723 |
DOI: | 10.1016/j.accinf.2016.12.004 |