Adoption of AI-Based Accounting Systems and Audit Efficiency

This study investigates the effect of AI-based accounting system adoption on audit efficiency among Nigerian deposit money banks (DMBs) from 2019 to 2023. Employing a quantitative ex post facto panel research design, the study uses panel data from 15 banks and a fixed effects regression model to estimate the efficiency of audits, which is measured by audit cycle time, error detection rate, and audit cost ratio, and AI adoption, which is measured by a composite index derived from annual reports and regulatory filings. The results show that AI adoption significantly improves overall audit efficiency (β = 0.412, p < 0.01), enhances error detection (β = 0.623, p < 0.01), reduces audit costs (β = 0.318, p < 0.05), and accelerates audit completion (β = 0.187, p < 0.05). Firm size, regulatory environment, and technological readiness are also significant determinants. The results are consistent with the Technology Acceptance Model and Diffusion of Innovation Theory, and suggest AI plays a pivotal role in audit transformation in emerging banking systems. This research adds to the body of knowledge by offering panel evidence from Nigeria and suggests the need for increased investment in AI infrastructure and regulatory support for successful audit digitisation.