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

Adrian Gepp, Martina K Linnenluecke, Terence J O'Neill

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)
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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.

Original languageEnglish
Pages (from-to)102-115
Number of pages14
JournalJournal of Accounting Literature
Volume40
Early online date1 Feb 2018
DOIs
Publication statusPublished - 1 Jun 2018

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Auditing
Modeling
Genealogy
Financial fraud
Financial distress
Auditors
Alignment
Stock market
Finance
Prediction
Peer to peer

Cite this

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Big data techniques in auditing research and practice: Current trends and future opportunities. / Gepp, Adrian; Linnenluecke, Martina K; O'Neill, Terence J.

In: Journal of Accounting Literature, Vol. 40, 01.06.2018, p. 102-115.

Research output: Contribution to journalArticleResearchpeer-review

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