CoChat
Sign Up
Cited by 491Robotic Process Automation ApplicationsArtificial Intelligence ApplicationsImpact of AI and Big Data on Business and Society

The DOI matches its Crossref record; 55 of the 58 references it cites were found in a registry or an index.

Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation

Issa, Hussein, Sun, Ting, Vasarhelyi, Miklos A.·2016·Journal of Emerging Technologies in Accounting
DOI 10.2308/jeta-10511
Open this paper in CoChat — free account

Abstract

After decades of frustration with long “AI Winters,” various business industries are witnessing the arrival of AI's “Spring,” with its massive and compelling benefits. Auditing will also evolve with the application of AI. Recently, there has been a progressive evolution of technology aimed at creating “artificially intelligent” devices. Although this evolution has been permeated with false starts and exaggerated claims, there is some convergence on the fact that substantive progress has been obtained in the last few years with the adoption of deep learning in conjunction with much faster machines and dimensionally larger storage spaces (and samples). The area of auditing has lagged business adoption in the past (Oldhouser 2016), but is prime for partial automation due to its labor intensiveness and range of decision structures. Several accounting firms have disclosed substantive investments in the AI fields. This paper proposes various areas of AI-related research to examine where this emerging technology is most promising. Moreover, this paper raises a series of methodological and evolutionary research questions aiming to study the AI-driven transformation of today's world of audit into the assurance of the future.

Overview

Integrity signals

Registry

Crossref · 10.2308/jeta-10511

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 58 references

Research works printed but not linked: 20

  1. Bedard, 1994, Auditors' knowledge organization: Observations from audit practice and their implications, Auditing: A Journal of Practice & Theory, 13, 73
  2. Cho, J., K. Lee, E. Shin, G. Choy, and S. Do. 2016. How Much Data Is Needed to Train a Medical Image Deep Learning System to Achieve Necessary High Accuracy?Working paper, Massachusetts General Hospital and Harvard Medical School. arXiv: 1511.06348v2. · arXiv
  3. Courbariaux, M., I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio. 2016. Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or −1. Working paper. Available at: https://arxiv.org/pdf/1602.02830v3.pdf · arXiv
  4. Deloitte. 2016. Deloitte Forms Alliance with Kira Systems to Drive the Adoption of Artificial Intelligence in the Workplace. Available at: https://www2.deloitte.com/us/en/pages/about-deloitte/articles/press-releases/deloitte-forms-alliance-with-kira-systems-to-drive-the-adoption-of-artificial-intelligence-in-the-workplace.html
  5. Eining, 1991, The impact of expert system usage on experiential learning in an auditing setting, Journal of Information Systems, 5, 1
  6. Gillett, 1993, Automated dynamic audit program tailoring: An expert system approach, Auditing: A Journal of Practice & Theory, 12, 173
  7. Graham, 1991, Developing risk advisorism: An expert system for risk identification, Auditing: A Journal of Practice & Theory, 10, 69
  8. Gray, 1991, The future impact of expert systems and decision support systems in auditing, Advances in Accounting, 9, 249
  9. Hobbes, T. 1651. Leviathan. Reprinted 1969. Menston, U.K.: Scolar Press.
  10. Kozlowski, S. 2016. Audit Ecosystem. Doctoral dissertation, Rutgers, The State University of New Jersey.
  11. McCarthy, 2006, A proposal for the Dartmouth summer research project on artificial intelligence August 31, 1955, AI Magazine, 27, 12
  12. Sun, T., and M. Vasarhelyi. 2016. Sentiment Features of Conference Calls and Internal Control Quality: An Application of Deep Learning Technology. Working paper, Rutgers, The State University of New Jersey.
  13. Swinney, 1999, Consideration of the social context of auditors' reliance on expert system output during evaluation of loan loss reserves, International Journal of Intelligent Systems in Accounting, Finance and Management, 8, 199
  14. Vasarhelyi, 1993, Discussion of automated dynamic audit program tailoring: An expert system approach, Auditing: A Journal of Practice & Theory, 12, 190
  15. Vasarhelyi, M., and A. Kogan. 1989. Artificial Intelligence and Expert Systems in Accounting and Auditing. Vol. 1. Princeton, NJ: Markus Wiener Publishers.
  16. Vasarhelyi, M., and A. Kogan. 1994 a. Artificial Intelligence and Expert Systems in Accounting and Auditing. Vol. 3. Princeton, NJ: Markus Wiener Publishers.
  17. Vasarhelyi, M., and A. Kogan. 1994 b. Artificial Intelligence and Expert Systems in Accounting and Auditing. Vol. 2. Princeton, NJ: Markus Wiener Publishers.
  18. Vasarhelyi, M., and A. Kogan. 1998. Artificial Intelligence in Accounting and Auditing: Towards New Paradigms. Vol. 4. Princeton, NJ: Markus Wiener Publishers.
  19. Vasarhelyi, M., and D. O'Leary. 2005. Artificial Intelligence in Accounting and Auditing: Adding Value to Business. Vol. 5. Princeton, NJ: Markus Wiener Publishers.
  20. Vasarhelyi, M., E. Bonson, and R. Hoitash. 2005. Artificial Intelligence in Accounting and Auditing: International Perspectives. Vol. 6. Princeton, NJ: Markus Wiener Publishers.

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Issa, H., Sun, T., & Vasarhelyi, M. A.
Affiliations
Rutgers, The State University of New Jersey (US)
ORCID
Venue
Journal of Emerging Technologies in Accounting
Publisher
American Accounting Association
Published
2016-09-01
Volume
13
Issue
2
Pages
1-20
ISSN
  • 1554-1908 (ISSN-L)
  • 1558-7940
DOI
10.2308/jeta-10511
OpenAlex
W2591793331
Cited by
491
References
58
Citations by year
2026: 103 · 2025: 80 · 2024: 77 · 2023: 56 · 2022: 40 · 2021: 47 · 2020: 44 · 2019: 24 · 2018: 14 · 2017: 6
FWCI
19.89
Type
journal article
Language
en
Open access
closed
Locations
Journal of Emerging Technologies in Accounting (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Robotic Process Automation Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Keywords
artificial intelligence in auditing, audit automation, deep learning, assurance, research agenda

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review