Effect of artificial intelligence on audit practices: empirical evidence from auditors in Southern Nigeria

Abstract

Purpose: This study examines the effect of artificial intelligence (AI) on audit practices. This study aims to determine whether AI will affect auditing practices in any way. This investigation was supported by the Task–Technology Fit (TTF) theory.

Methodology: Using a structured questionnaire and a research survey design, data was gathered from primary sources. The study's sample size consisted of 316 auditors from audit firms in southern Nigeria. Multiple regression analysis was used to determine the kind and strength of correlations between the study variables.

Results and conclusion: Findings show that the p-value of 0.0391 was less than 0.05, the results demonstrated that machine learning (ML) significantly affects auditing practices (AUP). A p-value of less than 0.05 indicates that expert systems (ES) have a substantial impact on auditing practices (AUP), and a p-value of less than 0.05 indicates that natural language processing (NLP) has a significant impact on auditing practices (AUP). The results of the study demonstrated that artificial intelligence (ML, ES, and NLP) significantly and favorably impacted auditing practices.

Implication of findings: To improve efficiency, accuracy, and fraud detection skills, audit firms and regulatory agencies should proactively incorporate Artificial Intelligence (AI) techniques into audit practices. To automate regular audit processes, analyze vast amounts of financial data in real time, and more successfully discover abnormalities or dangers than using traditional approaches, technologies like machine learning, expert system, and natural language processing should be implemented.