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Machine Learning Applied in Government Audit with Focus on Financial Statement: A Systematic Literature Review

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Highlights in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection (PAAMS 2024)

Abstract

Government audit, characterized by systematic examinations and evaluations of public sector organizations, is pivotal in ensuring the integrity, transparency, and accountability of public finances. Given this scenario, this paper presents a comprehensive exploration of the integration of Machine Learning (ML) within Government Auditing, with a specific focus on financial statements, and to study how ML has influenced the analysis and decision-making of the auditors. Following the PRISMA methodology, this literature review explored publications from the past five years using keywords in both English and Portuguese to ensure comprehensive coverage. The review revealed the multifaceted applications of ML in various contexts of financial auditing within governments worldwide, elucidating the diverse approaches employed in these scenarios and their success rates, providing valuable insights into emerging trends and best practices. Overall, this paper contributes to advancing knowledge and understanding of ML in Government Auditing, providing valuable insights for practitioners, policymakers, and researchers in the field.

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Acknowledgement

This research was supported by the Federal Institute of Maranhão (IFMA), the Foundation for the Support of Research and Scientific and Technological Development of Maranhão (FAPEMA), and the Polytechnic of Porto (ISEP). Their support is gratefully acknowledged.

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Correspondence to Heloisa Guimarães Coelho .

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Coelho, H.G., Marreiros, G., Maia, L.F. (2025). Machine Learning Applied in Government Audit with Focus on Financial Statement: A Systematic Literature Review. In: González-Briones, A., et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection. PAAMS 2024. Communications in Computer and Information Science, vol 2149. Springer, Cham. https://doi.org/10.1007/978-3-031-73058-0_18

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  • DOI: https://doi.org/10.1007/978-3-031-73058-0_18

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