Challenges and Opportunities of Implementing Artificial Intelligence in Auditing Practices: A Case Study of Nigerian Accounting Firms

The ever-evolving nature of the worldwide business arena is undergoing a significant change propelled by technological progress, where artificial intelligence (AI) is emerging as a central force redefining conventional methodologies. This research intends to identify and dissect the challenges encountered by Nigerian accounting firms in incorporating AI into their auditing processes, and to illuminate the opportunities that arise from leveraging AI capabilities in the auditing domain. This research utilized a survey research design. A well-structured questionnaire was employed to gather data from statutory auditors familiar with the use of artificial intelligence within their accounting firms situated in Lagos, the commercial hub of the Nigerian economy. The study encompassed all 35 registered accounting firms in Lagos State, Nigeria, employing a census sampling technique to determine the sample size, representing 100% of the population. Given the relative size of the population, five respondents were chosen from each accounting firm, totaling 175 respondents. The study received 153 responses, constituting 87% of the sample size. Descriptive and inferential statistics were applied to analyze the collected data. The outcomes reveal that AI, encompassing machine learning, natural language processing, and expert systems, significantly contributes to identifying challenges and highlighting opportunities in this context. The results underscore the positive role of AI in addressing challenges and revealing potential advancements in auditing practices within Nigerian accounting firms. The study concludes that AI, particularly machine learning, holds promise for addressing challenges and fostering advancements in auditing practices for Nigerian accounting firms. Given the significant positive impact of machine learning, accounting firms in Nigeria should consider prioritizing the integration of machine learning technologies into their auditing practices.