OPTIMIZING EXTERNAL AUDITORS’ REPORTING IN NIGERIA THROUGH ARTIFICIAL INTELLIGENCE: CONSTRAINTS AND A STRATEGIC ADOPTION FRAMEWORK

Abstract

The quality of external auditors’ reports is central to capital-market confidence, corporate governance, and stakeholder decision-making. In Nigeria, persistent concerns about audit quality manifested in audit failures, late or qualified reports, and restatements have sparked regulatory reforms and renewed interest in technological solutions. Artificial intelligence (AI) offers auditors powerful tools (machine learning, anomaly detection, natural language processing, and robotic process automation) capable of improving evidence collection, fraud risk assessment, sampling, disclosure review, and report drafting. This non-empirical paper synthesizes recent literature and policy signals on AI in auditing, diagnoses Nigeria-specific barriers to AI-enabled audit-report quality improvement, and proposes a practical, phased framework for responsible adoption by audit firms, regulators, and standards-setters. Key challenges identified include data fragmentation and access, legal and professional-ethics considerations, skills and human-capital gaps, vendor governance and model explainability, and cybersecurity. The paper concludes with governance, capacity, and regulatory recommendations designed to preserve auditor independence and professional scepticism while harnessing AI to raise audit-report quality in Nigeria.