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Exploration of Audit Technologies in Public Security Agencies: Empirical Research from Portugal

Author

Listed:
  • Diogo Leocádio

    (Department of Military Sciences, Portuguese Military Academy and CINAMIL, Avenida Conde Castro Guimarães, 2720-113 Lisbon, Portugal)

  • Luís Malheiro

    (Department of Military Sciences, Portuguese Military Academy and CINAMIL, Avenida Conde Castro Guimarães, 2720-113 Lisbon, Portugal)

  • João Reis

    (Industrial Engineering and Management, Faculty of Engineering, Lusófona University, Campo Grande, 1749-024 Lisbon, Portugal
    RCM2 + Research Centre for Asset Management and Systems Engineering, Lusófona University, Campo Grande, 376, 1749-024 Lisbon, Portugal
    Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Aveiro University, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal)

Abstract

The integration of artificial intelligence (AI) in the public sector is driving significant advancements in governance and management, changing the way public organizations operate. In particular, AI technologies have a profound impact on auditing practices, enhancing efficiency and accountability. This article aims to explore how AI can improve audit processes in a Portuguese public security agency, focusing on its transformative potential in streamlining tasks such as data extraction, analysis, and verification. Using a qualitative research approach, the study employs custom Python algorithms to examine the integration of key indicators into the audit process, specifically through the analysis of economic classification and expenditure limits. The findings demonstrate that personalized algorithms can reduce manual workloads, improve accuracy, and strengthen compliance with financial regulations, providing valuable contributions for decision-making. However, challenges such as data privacy and infrastructure investment remain, emphasizing the need for further research. Future studies should focus on adapting AI-based auditing models to various public administration contexts, addressing organizational changes, and advancing public governance.

Suggested Citation

  • Diogo Leocádio & Luís Malheiro & João Reis, 2025. "Exploration of Audit Technologies in Public Security Agencies: Empirical Research from Portugal," JRFM, MDPI, vol. 18(2), pages 1-18, January.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:2:p:51-:d:1574117
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    References listed on IDEAS

    as
    1. Goto, Masashi, 2023. "Anticipatory innovation of professional services: The case of auditing and artificial intelligence," Research Policy, Elsevier, vol. 52(8).
    2. Wood, David A., 2024. "More transparent and explainable machine learning algorithms are required to provide enhanced and sustainable dataset understanding," Ecological Modelling, Elsevier, vol. 498(C).
    3. Ines Kateb & Ines Belgacem, 2024. "Navigating governance and accounting reforms in Saudi Arabia's emerging market: impact of audit quality, board characteristics, and IFRS adoption on financial performance," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 21(2), pages 290-312, June.
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    Cited by:

    1. Dolores Genaro-Moya & Antonio Manuel López-Hernández & Mariia Godz, 2025. "Artificial Intelligence and Public Sector Auditing: Challenges and Opportunities for Supreme Audit Institutions," World, MDPI, vol. 6(2), pages 1-19, June.

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