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Application of Artificial Intelligence and Audit Quality in Nigeria

Domaine:

socioeconomic

Type de record:

paper
Créateur:
123E-molaMuy
Éditeur:
Cre
Hôte:
This study examined the effect of artificial intelligence on the quality of audit practice in Nigeria. Specifically, examined how expert systems, machine learning, and intelligent agents affect audit quality in Nigeria. The study employed a survey research design. The population of this study comprised 178 practicing accounting firms in Nigeria with the application of artificial intelligence. A sample size of 125 was selected using the purposive sampling technique. Data was obtained from primary sources using a well-structured questionnaire. Data were analysed using descriptive statistics and OLS regression analysis. The result of the study indicated that expert systems, machine learning, and intelligent agents exhibited a significant positive relationship to audit quality in Nigeria. Thus, the study concluded that the application of artificial intelligence positively influences the quality of auditors’ reports. With the positive impact made by artificial intelligence, in the coming decades, intelligent systems will take over more and more decision-making tasks from humans. The study recommended constant training of accountants and audit personnel on the use of AI techniques for the improvement of audit quality. Audit firms in Nigeria should invest in machine learning tools to further improve audit quality in the country. Also, the use of intelligent agents that assist in the classification of detecting objects into different categories should be increased among audit firms in Nigeria. Keywords: Artificial Intelligence, Expert Systems, Machine Learning, Intelligent Agents, Audit Quality. JEL Classification: M42, M49, C451. Journal Reference Format: Dagunduro, M.E., Falana, G.A., Adewara, Y.M. & Busayo, T.O. (2023): Application of Artificial Intelligence and Audit Quality in Nigeria. Humanities, Management, Arts, Education & the Social Sciences Journal. Vol. 11. No. 1, Pp 39-56. isteams.net . dx.doi.org

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