Title: Machine learning approach to identify performance audit topics for different government sectors

Authors: Alaa Aljanaby; Ahmad Abdel-Hafez; Yue Xu; Tim Rose

Addresses: University of Waikato College, University of Waikato, Hamilton 3240, New Zealand ' Department of Data Science and AI, College of Computing and Information Technology, University of Doha for Science and Technology, Doha, Qatar ' Faculty of Science, School of Computer Science, Queensland University of Technology, Brisbane QLD 4001, Australia ' Faculty of Engineering, School of Architecture and Built Environment, Queensland University of Technology, Brisbane QLD 4001, Australia

Abstract: A government performance audit is an independent evaluation of a government entity's activities and operations aimed at improving its efficiency, effectiveness, and accountability. Audit offices are frequently facing the challenge of selecting an audit topic for different government sectors that justifies the use of public money to conduct the performance audit. Text mining techniques have been rarely mentioned in association with selecting performance audit topics in the literature. In this work, we identify potential performance audit topics using topic modelling, an unsupervised machine learning approach. Topic modelling has been employed to create a demonstration system aimed at showcasing the utility of text mining tools in identifying potential audit topics. The outcome of this study suggests that incorporating text mining in the stage of identifying performance audit topics will streamline the topic selection process and decrease the amount of time required for manual information gathering at the outset.

Keywords: topic modelling; performance audit; text mining; audit office; audit topics; topic ranking; topic filtering; machine learning.

DOI: 10.1504/IJAAPE.2024.138481

International Journal of Accounting, Auditing and Performance Evaluation, 2024 Vol.20 No.3/4, pp.437 - 451

Received: 11 Mar 2023
Accepted: 03 Nov 2023
Published online: 07 May 2024 *

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