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Identifying Going Concern Audit Opinions Using Supervised Machine Learning
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DOI：10.1002/isaf.70020.png)
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Abstract
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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.
Keywords:
audit reports
going concern opinions
information retrieval
supervised machine learning
text classification
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