Date of this Version

7-31-2017

Document Type

Journal Article

Publication Details

Accepted version

Gepp, A., Linnenluecke, M.K., O'Neill, T.J., & Smith, T. (2017). Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature.

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© 2017 Elsevier

Distribution License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

ISSN

0737-4607

Abstract

This paper analyzes the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces.

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This document has been peer reviewed.