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Artificial intelligence and auditing in small‐ and medium‐sized firms: Expectations and applications

Expectations and Applications
Authors: Rikhardsson , Pall; Kristinn, Thórisson; Bergthorsson , Gudmundur; Batt , Catherine;

Artificial intelligence and auditing in small‐ and medium‐sized firms: Expectations and applications

Abstract

AbstractAuditing is a field of expertise often mentioned as being ripe for automation using artificial intelligence methods at all levels of operations. Primarily, the application of artificial intelligence (AI) in the auditing profession is done by and for large organizations, leveraging large datasets. While AI approaches for big data are continually improving, methods for small data are scarce. Yet most firms in the world employ fewer than 50 people and can, therefore, rarely rely on big data for automation. In our study, we ask auditors, who mainly audit SMEs, about their expectations towards the impact of AI on the auditing profession and where they expect it to provide the most value when it comes to auditing SMEs. We find that these auditors expect significant improvements in their own efficiency on the job, that learning to use AI applications will not be a challenge for them, and that the use of AI in auditing firms will become mandatory in the future. They expect the performance of certain tasks to become AI‐augmented, including risk assessment of individual transactions, conducting audit interviews, performing all manners of analysis, writing confirmation letters, performing the final verification of annual reports, and performing physical observations. Considering these results, we discuss the potential impact of these developments, such as how AI could make the auditing process more effective and efficient but also how AI could lead to an even higher concentration of the auditing service industry.

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    17
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Top 10%
Top 10%
hybrid