Artificial Intelligence and Audit Quality: Evidence from Emerging Economies

Abstract

This study investigates the impact of artificial intelligence (AI) adoption on audit quality in emerging economies, with an emphasis on fraud detection, risk assessment, implementation challenges, and the roles of auditor competence and technological readiness. A quantitative research design was employed, using secondary panel data from 25 firms observed over a decade, yielding 250 firm-year observations. Data were drawn from annual reports, audit reports, governance disclosures, and publicly available financial statements. Analytical techniques included descriptive statistics, correlation analysis, fixed- and random-effects regression models, and Hausman specification tests to ensure methodological robustness. Findings reveal that AI adoption significantly enhances audit quality by improving fraud detection and risk assessment, enabling auditors to identify irregularities and unusual transactions more effectively. Nonetheless, weak technological infrastructure, high implementation costs, cybersecurity risks, and limited expertise constrain optimal adoption. Auditor competence and technological readiness emerged as decisive factors influencing AI’s effectiveness in improving audit outcomes. This study contributes to the literature by providing empirical evidence on AI’s transformative role in auditing within emerging economies, highlighting both opportunities and constraints. The findings underscore the need for investments in digital infrastructure, auditor training, and cybersecurity frameworks to maximize AI’s potential in audit practice.