Big data techniques in auditing research and practice: Current trends and future opportunities
By Adrian Gepp, M. Linnenluecke, T. O'Neill, Thomas J. Smith. Journal of Accounting Literature, 2018
323 citations recorded by the source corpus.
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.
Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/02e0982171b3eed379f4dea393e1aa61d4f0a9da
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In Accounting
In Management Science and Operations Research
Citations in the corpus
- The digital transformation of external audit and its impact on corporate governance (2020, background): “Gepp et al. (2018) argue that big data analysis is not yet widespread and that more studies need to identify the opportunity of using such technology.”