TY - JOUR
T1 - Exploration of Audit Technologies in Public Security Agencies
T2 - Empirical Research from Portugal
AU - Leocádio, Diogo
AU - Malheiro, Luís
AU - Reis, João
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/2
Y1 - 2025/2
N2 - 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.
AB - 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.
KW - Portugal
KW - artificial intelligence
KW - auditing
KW - public sector
KW - python
UR - http://www.scopus.com/inward/record.url?scp=85218893388&partnerID=8YFLogxK
U2 - 10.3390/jrfm18020051
DO - 10.3390/jrfm18020051
M3 - Article
AN - SCOPUS:85218893388
SN - 1911-8066
VL - 18
JO - Journal of Risk and Financial Management
JF - Journal of Risk and Financial Management
IS - 2
M1 - 51
ER -