Impact of Artificial Intelligence Adoption on External Auditing Efficiency in Deposit Money Banks in Nigeria: Evidence from Taraba State
TL;DR
The study concludes that integrating AI tools into audit processes enhances efficiency, reduces manual workload, and improves audit accuracy, and recommends investing in AI tools, building auditors’ digital competence, strengthening regulatory guidance, and improving IT infrastructure.
Abstract
This research examined the impact of artificial intelligence adoption on external auditing efficiency in Deposit Money Banks in Taraba State, Nigeria, covering the period 2021 to 2025. Specifically, it assessed the effects of AI-based audit automation, machine learning analytics, and AI fraud detection systems on audit timeliness, accuracy, workload reduction, and fraud detection. Primary data were collected from external auditors, internal control officers, compliance officers, finance officers, and other audit-related personnel using structured questionnaires. Descriptive statistics, correlation analysis, and multiple regression analysis were employed to analyze the data. The findings reveal that all three AI adoption variables positively and significantly influence external auditing efficiency, with AI-based audit automation showing the strongest effect. The study concludes that integrating AI tools into audit processes enhances efficiency, reduces manual workload, and improves audit accuracy. Recommendations include investing in AI tools, building auditors’ digital competence, strengthening regulatory guidance, and improving IT infrastructure. These findings provide practical insights for auditors, bank management, and regulators on the effective use of AI to improve audit performance.