Artificial Intelligence-Driven Audit Technology and Auditor Performance: Evidence from Listed Deposit Money Banks in Nigeria

Abstract

The increasing use of artificial intelligence in audit practice has begun to reshape how audit tasks are performed, yet evidence on its implications for auditor performance remains limited, particularly in developing economies. This study investigates the relationship between artificial intelligence-driven audit technology and auditor performance in listed deposit money banks in Nigeria. Drawing on Task-Technology Fit Theory, AI-driven audit technology is examined through three functional dimensions, namely AI-based audit analytics, machine learning-enabled fraud detection systems, and robotic process automation in audit tasks, while auditor experience is incorporated as a control variable. The study employs a quantitative cross-sectional survey approach using data obtained from 410 audit professionals. Data were analyzed using Partial Least Squares Structural Equation Modeling with SmartPLS 4, following a two-stage procedure involving measurement and structural model assessment. The findings show that AI-based audit analytics, machine learning-enabled fraud detection systems, and robotic process automation each have a positive and statistically significant relationship with auditor performance. Auditor experience also demonstrates a significant positive influence, indicating that professional expertise complements, rather than replaces, the use of advanced audit technologies. Overall, the results provide empirical evidence that the effective alignment of technological capabilities with audit task requirements enhances auditor performance within the banking sector. The study contributes to auditing literature by offering evidence on the performance effects of disaggregated AI-driven audit technologies in a developing economy and highlights the practical importance of investing in both advanced audit tools and continuous professional development.

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