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A inteligência artificial nas instituições financeiras: Desafios para a auditoria externa
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Abstract(s)
A rápida evolução da Inteligência Artificial (IA) tem impulsionado profundas transformações nas Instituições Financeiras (IF), otimizando processos, reforçando a deteção de fraudes e melhorando a experiência do cliente. No entanto, a sua adoção levanta desafios significativos para a Auditoria Externa (AE), nomeadamente na avaliação de riscos, na resposta aos riscos identificados e na necessidade de adaptação dos auditores a novas metodologias e tecnologias. Este estudo tem como objetivo analisar os principais desafios que a adoção da IA pelas IF coloca à AE em Portugal, avaliando o impacto da tecnologia nos processos de auditoria, nos riscos de distorção material das demonstrações financeiras e nas competências exigidas aos auditores. A metodologia adotada combina uma revisão de literatura com a realização de entrevistas semiestruturadas a auditores experientes, garantindo uma abordagem qualitativa aprofundada. Os resultados evidenciam que a IA melhora a eficiência da auditoria e a capacidade de deteção de padrões e anomalias, mas também introduz desafios, como a opacidade dos algoritmos, a necessidade de novas competências técnicas e a adaptação dos processos de auditoria. Além disso, destaca-se a importância da formação contínua dos auditores para garantir que estes acompanham a evolução tecnológica e conseguem mitigar os riscos inerentes à IA no setor financeiro.
The rapid evolution of Artificial Intelligence has driven profound transformations in Financial Institutions, optimizing processes, enhancing fraud detection, and improving customer experience. However, its adoption poses significant challenges for External Audit, particularly in risk assessment, response to identified risks, and the need for auditors to adapt to new methodologies and technologies. This study aims to analyze the main challenges that AI adoption by FI presents to EA in Portugal, assessing the impact of technology on audit processes, the risks of material misstatement in financial statements, and the skills required of auditors. The adopted methodology combines a literature review with semi-structured interviews with experienced auditors, ensuring a thorough qualitative approach. The results highlight that AI enhances audit efficiency and the ability to detect patterns and anomalies, but also introduces challenges such as algorithm opacity, the need for new technical skills, and the adaptation of audit processes. Additionally, the importance of continuous auditor training is emphasized to ensure they keep up with technological advancements and can mitigate the risks inherent to AI in the financial sector.
The rapid evolution of Artificial Intelligence has driven profound transformations in Financial Institutions, optimizing processes, enhancing fraud detection, and improving customer experience. However, its adoption poses significant challenges for External Audit, particularly in risk assessment, response to identified risks, and the need for auditors to adapt to new methodologies and technologies. This study aims to analyze the main challenges that AI adoption by FI presents to EA in Portugal, assessing the impact of technology on audit processes, the risks of material misstatement in financial statements, and the skills required of auditors. The adopted methodology combines a literature review with semi-structured interviews with experienced auditors, ensuring a thorough qualitative approach. The results highlight that AI enhances audit efficiency and the ability to detect patterns and anomalies, but also introduces challenges such as algorithm opacity, the need for new technical skills, and the adaptation of audit processes. Additionally, the importance of continuous auditor training is emphasized to ensure they keep up with technological advancements and can mitigate the risks inherent to AI in the financial sector.
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Keywords
Auditoria externa Inteligência artificial Instituições financeiras Riscos de distorção material External auditing Artificial Intelligence Financial institutions Risks of material misstatement