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Identifying Going Concern Audit Opinions Using Supervised Machine Learning

Dennis Hedback

Intelligent Systems in Accounting, Finance and Management2025https://doi.org/10.1002/isaf.70020article
AJG 1
Weight
0.37

What the paper says

This paper evaluates the use of supervised machine learning to automatically identify going concern–modified audit reports. Models based on two different classifiers—logistic regression and extreme gradient boosting—achieve strong classification performance for this task. The same classifiers, along with naïve Bayes, also demonstrate strong performance in the ancillary task of identifying audit report pages in financial reports. These results have practical implications, including the application of the presented methods for timely accounting information retrieval for users, automated peer comparison for auditors, or as a data extraction method for researchers, particularly in settings with limited audit data availability.

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https://doi.org/https://doi.org/10.1002/isaf.70020

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@article{dennis2025,
  title        = {{Identifying Going Concern Audit Opinions Using Supervised Machine Learning}},
  author       = {Dennis Hedback},
  journal      = {Intelligent Systems in Accounting, Finance and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1002/isaf.70020},
}

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Identifying Going Concern Audit Opinions Using Supervised Machine Learning

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.