Artificial Intelligence Dimensions and Audit Quality: A Study of Selected Audit Firms in Nigeria

This study examined the effect of various Artificial Intelligence (AI) dimensions on audit quality within major audit firms in Nigeria, specifically focusing on Machine Learning, Natural Language Processing (NLP), Robotic Process Automation (RPA), and Data Analytics. A survey research design was utilized, allowing data collection from 249 respondents across four prominent audit firms (PwC, KPMG, Deloitte, and EY) through structured questionnaires. Using regression analysis, the study found that all AI dimensions positively influence audit quality, with Machine Learning having the most significant effect, followed by Data Analytics, NLP, and RPA. The findings indicate that Machine Learning enhances audit quality by enabling robust anomaly detection and improving risk assessment, while Data Analytics contributes to trend analysis and informed decision-making. NLP and RPA further support audit quality by analyzing unstructured data for compliance and automating repetitive tasks to minimize errors and increase efficiency. It concludes with recommendations for adopting Machine Learning and Data Analytics as priorities and emphasizes continuous assessments to evaluate AI’s impact on audit quality. Future research should investigate the longitudinal effects of AI integration and consider additional AI technologies, such as Cognitive Computing, to further validate the findings across different auditing contexts. These results highlight the transformative potential of AI in enhancing audit quality, compliance, and fraud detection in Nigeria’s auditing sector.