Evaluation of Artificial Intelligence and Efficacy of Audit Practice in Nigeria

Owonifari, Victor Olufemi and Igbekoyi, Olusola Esther and Awotomilusi, Niyi Solomon and Dagunduro, Muyiwa Emmanuel (2023) Evaluation of Artificial Intelligence and Efficacy of Audit Practice in Nigeria. Asian Journal of Economics, Business and Accounting, 23 (16). pp. 1-14. ISSN 2456-639X

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Abstract

Artificial Intelligence (AI) has become increasingly popular globally as a crucial tool for auditing financial statements, but in Nigeria, the adoption and use of AI tools by auditors is still in its early stages. Attention has been primarily focused on the Big 4 accounting firms, with little attention given to small-scale audit practitioners in Nigeria. This study seeks to examine the impact of AI on audit practice in Nigeria by employing a survey research design. The population of this study comprises 89 accounting firms operating in the Ikeja Local Government area of Lagos State, with a sample size of 62 firms selected using purposive sampling. Data was collected through a well-structured questionnaire, and the reliability of the research instrument was confirmed with a Cronbach Alpha test result of an average of 70%. Descriptive analysis and regression analysis were used to analyze the data, and the results indicated that data mining, machine learning, and image recognition exhibited a significant positive relationship with audit practice in Nigeria. The study concluded that the use of AI will enable auditors to predict future trends and make more informed decisions that focus on improving audit practice. The study recommended constant training of accountants and audit personnel on the use of data mining techniques to improve audit practice, investment in machine learning tools by audit firms in Nigeria, and increased use of image recognition to assist in object classification.

Item Type: Article
Subjects: GO for STM > Social Sciences and Humanities
Depositing User: Unnamed user with email support@goforstm.com
Date Deposited: 12 Jun 2023 04:54
Last Modified: 14 Sep 2023 07:54
URI: http://archive.article4submit.com/id/eprint/1073

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