Impact of Artificial Intelligence Adoption on Audit Quality and Earnings Management in Emerging Markets

Abstract

The researchers examined the impact of artificial intelligence (AI) adoption on audit quality and earnings management in emerging markets, with specific reference to Pakistan. Artificial intelligence transformed many professional domains in recent years. Auditing was not an exception to this transformation. Audit firms increasingly adopted AI-based tools for data analytics, risk assessment, and fraud detection. These tools promised greater accuracy and efficiency in the audit process. However, empirical evidence on their actual contribution to audit quality remained limited in developing economies. Earnings management practices continued to undermine the credibility of financial reporting in such contexts. The study adopted a quantitative research design to investigate these relationships. Data were collected online through a structured questionnaire distributed among professionals in Lahore, Multan, and Rawalpindi. A sample of 300 respondents was selected through purposive sampling. External auditors, internal auditors, and financial reporting professionals formed the target population. Structural Equation Modeling was applied to test the relationships among AI adoption, audit quality, and earnings management. The results indicated that AI adoption significantly improved audit quality. Audit quality, in turn, reduced earnings management practices among the sampled firms. Audit quality also mediated the relationship between AI adoption and earnings management. These findings carried practical implications for regulators, audit firms, and policymakers in Pakistan. The study extended the literature on technology-driven auditing within emerging market contexts.

Keywords: Artificial Intelligence, Audit Quality, Earnings Management, Emerging Markets, Pakistan, Structural Equation Modeling, Financial Reporting