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Cited by 352

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Big data techniques in auditing research and practice: Current trends and future opportunities

Open access
Gepp, Adrian, Linnenluecke, Martina K., O’Neill, Terrence J., Smith, Tom·2018·Journal of Accounting Literature

Abstract

This paper analyses 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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Metadata

Authors
Gepp, A., Linnenluecke, M. K., O’Neill, T. J., & Smith, T.
Venue
Journal of Accounting Literature
Publisher
Emerald
Published
2018-02-01
Volume
40
Issue
1
Pages
102-115
ISSN
0737-4607 (ISSN-L)
OpenAlex
W2598798227
Cited by
352
References
6
FWCI
41.19
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Big data techniques in auditing research and practice: Current trends and future opportunities. https://app.cochat.ai/papers/10.1016/j.acclit.2017.05.003
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Gepp, A., Linnenluecke, M. K., O’Neill, T. J., & Smith, T. (2018). Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature. https://doi.org/10.1016/j.acclit.2017.05.003

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.