A delegação e uso de inteligências artificiais no contexto da auditoria interna
Resumen
Objetivo: Esta pesquisa teve como objetivo compreender os fatores que levam à delegação ou não de
tarefas para inteligência artificial no contexto da auditoria interna a luz da teoria da dominância tecnológica.
Método: Foram efetuadas entrevistas semiestruturadas com 13 profissionais de auditoria interna. Para
análise de dados foi empregada a análise de conteúdo.
Originalidade/Relevância: Ainda há uma lacuna para entender como os tomadores de decisões podem se
adaptar para o uso eficaz de técnicas de IA e como incorporaram estas técnicas nas análises. Existe um
chamado para estudos qualitativos que expliquem os motivos que levam a decisão para automação de
tarefas, buscando compreender um contexto específico.
Resultados: Tarefas que exigem julgamento profissional são preferíveis para não serem delegadas,
principalmente quando envolvem a detecção e prevenção de fraude. Os modelos precisam estar
parametrizados a fim de diferenciar uma fraude de um erro não intencional. Necessidade de novas
competências do profissional de auditoria, identificando a falta de formação em programação e uso de
inteligências artificiais.
Contribuições teóricas/metodológicas/práticas: Como contribuições teóricas, este estudo complementa
a literatura de delegação de tarefas ao identificar fatores que podem contribuir com adoção. Este estudo
também avança no campo teórico, pois identificou-se que a idade pode possuir uma característica
moderadora na experiência. Outra contribuição teórica é a proposição de um framework de delegação que
pode ser explorado em futuras pesquisas com o método de Design Science Research (DSR). Como
contribuição prática, ao delegar tarefas repetitivas e rotineiras para a IA, os auditores internos podem focar
em atividades mais complexas e estratégicas, aumentando a eficiência do departamento de auditoria
interna.
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Arnold, V. (2018). The changing technological environment and the future of behavioural research in accounting. Accounting & Finance, 58(2), 315–339. https://doi.org/10.1111/acfi.12218
Arnold, V., Collier, P. A., Leech, S. A., & Sutton, S. G. (2004). Impact of intelligent decision aids on expert and novice decision-makers’ judgments. Accounting and Finance, 44(1), 1–26. https://doi.org/10.1111/j.1467-629x.2004.00099.x
Arnold, V., & Sutton, S. G. (1998). The theory of technology dominance: Understanding the impact of intelligent decision aids on decision maker’s judgment. Advances in Accounting Behavioral Research, 1, 175–194.
Baird, A., & Maruping, L. M. (2021). The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts. MIS Quarterly, 45(1), 315–341. https://doi.org/10.25300/MISQ/2021/15882
Benbya, H., Pachidi, S., & Jarvenpaa, S. L. (2021). Special Issue Editorial: Artificial Intelligence in Organizations: Implications for Information Systems Research. Journal of the Association for Information Systems, 23.
Bertomeu, J. (2020). Machine learning improves accounting: Discussion, implementation and research opportunities. Review of Accounting Studies, 25(3), 1135–1155. https://doi.org/10.1007/s11142-020-09554-9
Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468–519. https://doi.org/10.1007/s11142-020-09563-8
Betti, N., & Sarens, G. (2021). Understanding the internal audit function in a digitalised business environment. Journal of Accounting & Organizational Change, 17(2), 197–216. https://doi.org/10.1108/JAOC-11-2019-0114
Bierstaker, J., Janvrin, D., & Lowe, D. J. (2014). What factors influence auditors' use of computer-assisted audit techniques?. Advances in Accounting, 30(1), 67-74. https://doi.org/10.1016/j.adiac.2013.12.005.
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Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What Are You Saying? Using topic to Detect Financial Misreporting. Journal of Accounting Research, 58(1), 237–291. https://doi.org/10.1111/1475-679X.12294
Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral Implications of Big Data’s Impact on Audit Judgment and Decision Making and Future Research Directions. Accounting Horizons, 29(2), 451–468. https://doi.org/10.2308/acch-51023
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Ding, K., Lev, B., Peng, X., Sun, T., & Vasarhelyi, M. A. (2020). Machine learning improves accounting estimates: Evidence from insurance payments. Review of Accounting Studies, 25(3), 1098–1134. https://doi.org/10.1007/s11142-020-09546-9
Dyball, M. C., & Seethamraju, R. (2021). The impact of client use of blockchain technology on audit risk and audit approach—An exploratory study. International Journal of Auditing, 25(2), 602–615. https://doi.org/10.1111/ijau.12238
Evangelista, J. (2020). Estudo sobre a Teoria da Dominância Tecnológica no uso da inteligência artificial aplicada a Gestão Tributária no Brasil [Dissertação de Mestrado, Centro Universitário FECAP]. http://tede.fecap.br:8080/handle/123456789/849
Eilifsen, A., Kinserdal, F., Messier, W. F, & McKee, T. E. (2020). An Exploratory Study into the Use of Audit Data Analytics on Audit Engagements. Accounting Horizons, 34 (4): 75–103. https://doi.org/10.2308/HORIZONS-19-121
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Fisher, I. E., Garnsey, M. R., & Hughes, M. E. (2016). Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research: NLP in Accounting, Auditing and Finance. Intelligent Systems in Accounting, Finance and Management, 23(3), 157–214. https://doi.org/10.1002/isaf.1386
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Garven, S., & Scarlata, A. (2020). An examination of factors associated with investment in internal auditing technology. Managerial Auditing Journal, 35(7), 955–978. https://doi.org/10.1108/MAJ-06-2019-2321
Gray, G. L., Chiu, V., Liu, Q., & Li, P. (2014). The expert systems life cycle in AIS research: What does it mean for future AIS research? International Journal of Accounting Information Systems, 15(4), 423–451. https://doi.org/10.1016/j.accinf.2014.06.001
Hampton, C. (2005). Determinants of reliance: An empirical test of the theory of technology dominance. International Journal of Accounting Information Systems, 6(4), 217–240. https://doi.org/10.1016/j.accinf.2005.10.001
Hass, S., Abdolmohammadi, M. J., & Burnaby, P. (2006). The Americas literature review on internal auditing. Managerial Auditing Journal, 21(8), 835–844. https://doi.org/10.1108/02686900610703778
Huang, F., & Vasarhelyi, M. A. (2019). Applying robotic process automation (RPA) in auditing: A framework. International Journal of Accounting Information Systems, 35, 100433. https://doi.org/10.1016/j.accinf.2019.100433
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Institute of Internal Auditors [IIA]. (2021). The IIA's Internal Audit Competency Framework. https://www.theiia.org/en/content/guidance/mandatory/standards/ia-competency-framework/
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
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Arnold, Clark, Collier, Leech, & Sutton. (2006). The Differential Use and Effect of Knowledge-Based System Explanations in Novice and Expert Judgment Decisions. MIS Quarterly, 30(1), 79. https://doi.org/10.2307/25148718
Arnold, V. (2018). The changing technological environment and the future of behavioural research in accounting. Accounting & Finance, 58(2), 315–339. https://doi.org/10.1111/acfi.12218
Arnold, V., Collier, P. A., Leech, S. A., & Sutton, S. G. (2004). Impact of intelligent decision aids on expert and novice decision-makers’ judgments. Accounting and Finance, 44(1), 1–26. https://doi.org/10.1111/j.1467-629x.2004.00099.x
Arnold, V., & Sutton, S. G. (1998). The theory of technology dominance: Understanding the impact of intelligent decision aids on decision maker’s judgment. Advances in Accounting Behavioral Research, 1, 175–194.
Baird, A., & Maruping, L. M. (2021). The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts. MIS Quarterly, 45(1), 315–341. https://doi.org/10.25300/MISQ/2021/15882
Benbya, H., Pachidi, S., & Jarvenpaa, S. L. (2021). Special Issue Editorial: Artificial Intelligence in Organizations: Implications for Information Systems Research. Journal of the Association for Information Systems, 23.
Bertomeu, J. (2020). Machine learning improves accounting: Discussion, implementation and research opportunities. Review of Accounting Studies, 25(3), 1135–1155. https://doi.org/10.1007/s11142-020-09554-9
Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468–519. https://doi.org/10.1007/s11142-020-09563-8
Betti, N., & Sarens, G. (2021). Understanding the internal audit function in a digitalised business environment. Journal of Accounting & Organizational Change, 17(2), 197–216. https://doi.org/10.1108/JAOC-11-2019-0114
Bierstaker, J., Janvrin, D., & Lowe, D. J. (2014). What factors influence auditors' use of computer-assisted audit techniques?. Advances in Accounting, 30(1), 67-74. https://doi.org/10.1016/j.adiac.2013.12.005.
Borges, W. G., Leroy, R. S. D., Carvalho, L. F., Lima, N. C., & Oliveira, J. M. (2020). Implicações da Inteligência Artificial na Auditoria Interna no Brasil: Análise sob a Percepção de Profissionais. Sociedade, Contabilidade e Gestão, 15(1), 23–40. https://doi.org/10.21446/scg_ufrj.v0i0.25284
Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What Are You Saying? Using topic to Detect Financial Misreporting. Journal of Accounting Research, 58(1), 237–291. https://doi.org/10.1111/1475-679X.12294
Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral Implications of Big Data’s Impact on Audit Judgment and Decision Making and Future Research Directions. Accounting Horizons, 29(2), 451–468. https://doi.org/10.2308/acch-51023
CFC (2016a). NBC TA 200 (R1) – Objetivos gerais do auditor independente e a condução da auditoria em conformidade com normas de auditoria. https://www1.cfc.org.br/sisweb/SRE/docs/NBCTA200(R1).pdf
CFC (2016b). NBC TA 240 (R1) – Responsabilidade do auditor em relação a fraude, no contexto da auditoria de demonstrações contábeis. https://www2.cfc.org.br/sisweb/sre/detalhes_sre.aspx?Codigo=2016/NBCTA240(R1)
Ding, K., Lev, B., Peng, X., Sun, T., & Vasarhelyi, M. A. (2020). Machine learning improves accounting estimates: Evidence from insurance payments. Review of Accounting Studies, 25(3), 1098–1134. https://doi.org/10.1007/s11142-020-09546-9
Dyball, M. C., & Seethamraju, R. (2021). The impact of client use of blockchain technology on audit risk and audit approach—An exploratory study. International Journal of Auditing, 25(2), 602–615. https://doi.org/10.1111/ijau.12238
Evangelista, J. (2020). Estudo sobre a Teoria da Dominância Tecnológica no uso da inteligência artificial aplicada a Gestão Tributária no Brasil [Dissertação de Mestrado, Centro Universitário FECAP]. http://tede.fecap.br:8080/handle/123456789/849
Eilifsen, A., Kinserdal, F., Messier, W. F, & McKee, T. E. (2020). An Exploratory Study into the Use of Audit Data Analytics on Audit Engagements. Accounting Horizons, 34 (4): 75–103. https://doi.org/10.2308/HORIZONS-19-121
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Fisher, I. E., Garnsey, M. R., & Hughes, M. E. (2016). Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research: NLP in Accounting, Auditing and Finance. Intelligent Systems in Accounting, Finance and Management, 23(3), 157–214. https://doi.org/10.1002/isaf.1386
Flick, U. (2012). Introdução à Metodologia de Pesquisa: um guia para iniciantes. Penso.
Garven, S., & Scarlata, A. (2020). An examination of factors associated with investment in internal auditing technology. Managerial Auditing Journal, 35(7), 955–978. https://doi.org/10.1108/MAJ-06-2019-2321
Gray, G. L., Chiu, V., Liu, Q., & Li, P. (2014). The expert systems life cycle in AIS research: What does it mean for future AIS research? International Journal of Accounting Information Systems, 15(4), 423–451. https://doi.org/10.1016/j.accinf.2014.06.001
Hampton, C. (2005). Determinants of reliance: An empirical test of the theory of technology dominance. International Journal of Accounting Information Systems, 6(4), 217–240. https://doi.org/10.1016/j.accinf.2005.10.001
Hass, S., Abdolmohammadi, M. J., & Burnaby, P. (2006). The Americas literature review on internal auditing. Managerial Auditing Journal, 21(8), 835–844. https://doi.org/10.1108/02686900610703778
Huang, F., & Vasarhelyi, M. A. (2019). Applying robotic process automation (RPA) in auditing: A framework. International Journal of Accounting Information Systems, 35, 100433. https://doi.org/10.1016/j.accinf.2019.100433
International Auditing and Assurance Standards Board [IAASB] (2009). International Standard on Auditing 200: Overall objectives of the independent auditor and the conduct of an audit in accordance with international standards on auditing. https://www.ifac.org/system/files/publications/files/A009%202012%20IAASB%20Handbook%20ISA%20200.pdf
Institute of Internal Auditors [IIA]. (2021). The IIA's Internal Audit Competency Framework. https://www.theiia.org/en/content/guidance/mandatory/standards/ia-competency-framework/
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Jones, K. K., Baskerville, R. L., Sriram, R. S., & Ramesh, B. (2017). The impact of legislation on the internal audit function. Journal of Accounting & Organizational Change, 13(4), 450–470. https://doi.org/10.1108/JAOC-02-2015-0019
Kenno, S. A., McCracken, S. A., & Salterio, S. E. (2017). Financial Reporting Interview-Based Research: A Field Research Primer with an Illustrative Example. Behavioral Research in Accounting, 29(1), 77–102. https://doi.org/10.2308/bria-51648
Kokina, J., & Blanchette, S. (2019). Early evidence of digital labor in accounting: Innovation with Robotic Process Automation. International Journal of Accounting Information Systems, 35, 100431. https://doi.org/10.1016/j.accinf.2019.100431
Kokina, J., & Davenport, T. H. (2017). The Emergence of Artificial Intelligence: How Automation is Changing Auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://doi.org/10.2308/jeta-51730
Koreff, J (2022). Are Auditors' Reliance on Conclusions from Data Analytics Impacted by Different Data Analytic Inputs?. Journal of Information Systems, 36 (1), 19–37. https://doi.org/10.2308/ISYS-19-051
Korhonen, T., Selos, E., Laine, T., & Suomala, P. (2020). Exploring the programmability of management accounting work for increasing automation: An interventionist case study. Accounting, Auditing & Accountability Journal, 34(2), 253–280. https://doi.org/10.1108/AAAJ-12-2016-2809
Krieger, F., Drews, P., & Velte, P. (2021). Explaining the (non-) adoption of advanced data analytics in auditing: A process theory. International Journal of Accounting Information Systems, 41, 100511. https://doi.org/10.1016/j.accinf.2021.100511
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Publicado
2026-01-02
Cómo citar
Marques Correa, W. V., Bonato Marcolin, C., & da Silva Momo, F. (2026). A delegação e uso de inteligências artificiais no contexto da auditoria interna. Enfoque: Reflexão Contábil, 45(1), 1-21. https://doi.org/10.4025/enfoque.v45i1.70818
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Original Articles
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