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

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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 journalArticlepeer-review

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Gepp, A, Linnenluecke, MK & O'Neill, TJ 2018, 'Big data techniques in auditing research and practice: Current trends and future opportunities', Journal of Accounting Literature, vol. 40, pp. 102-115. https://doi.org/10.1016/j.acclit.2017.05.003

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MLA

Gepp, Adrian, Martina K Linnenluecke and Terence J O'Neill. "Big data techniques in auditing research and practice: Current trends and future opportunities". Journal of Accounting Literature. 2018, 40. 102-115. https://doi.org/10.1016/j.acclit.2017.05.003

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Gepp A, Linnenluecke MK, O'Neill TJ. Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature. 2018 Jun 1;40:102-115. doi: 10.1016/j.acclit.2017.05.003

Author

Gepp, Adrian ; Linnenluecke, Martina K ; O'Neill, Terence J. / Big data techniques in auditing research and practice: Current trends and future opportunities. In: Journal of Accounting Literature. 2018 ; Vol. 40. pp. 102-115.

RIS

TY - JOUR

T1 - Big data techniques in auditing research and practice: Current trends and future opportunities

AU - Gepp, Adrian

AU - Linnenluecke, Martina K

AU - O'Neill, Terence J

PY - 2018/6/1

Y1 - 2018/6/1

N2 - 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.

AB - 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.

U2 - 10.1016/j.acclit.2017.05.003

DO - 10.1016/j.acclit.2017.05.003

M3 - Article

VL - 40

SP - 102

EP - 115

JO - Journal of Accounting Literature

JF - Journal of Accounting Literature

SN - 0737-4607

ER -