Impact of Artificial Intelligence in Auting on Audit Quality

This study examined the impact of Artificial Intelligence in auditing on audit quality among audit firms operating in Edo State, Nigeria. Specifically, the study investigated the effects of Automated Data Analytics, Machine Learning Algorithms, AI-based Fraud Detection Tools, and Continuous Auditing Systems on audit quality. A survey research design was adopted, and primary data were collected through structured questionnaires administered to audit professionals, including partners, managers, senior auditors, and associates in selected audit firms. The data were analyzed using descriptive statistics, correlation analysis, and Ordinary Least Squares (OLS) regression techniques. The findings revealed that all the Artificial Intelligence components examined have positive and statistically significant effects on audit quality. Automated Data Analytics was found to enhance audit coverage and anomaly detection. Machine Learning Algorithms significantly improved risk assessment and predictive accuracy. AI-based Fraud Detection Tools showed a strong positive influence on audit effectiveness, while Continuous Auditing Systems significantly enhanced real-time monitoring and reduced detection lag. The overall regression model was statistically significant, indicating that Artificial Intelligence tools jointly explain a substantial proportion of the variation in audit quality. The study concludes that the integration of Artificial Intelligence technologies significantly enhances audit quality in Nigeria. It recommends that audit firms invest in AI infrastructure, strengthen training in data analytics and machine learning, and establish robust governance frameworks to ensure effective implementation. The study contributes to existing literature by providing empirical evidence from a developing economy context on the relationship between Artificial Intelligence adoption and audit quality