Toward a Digital Audit Future: Integrating AI and Predictive Analytics in Financial-Performance Review

Abstract

This study investigates the integration of artificial intelligence (AI) and predictive analytics in financial-performance review, proposing a hybrid audit model that merges algorithmic precision with human professional judgment. Building upon empirical evidence from AI-driven audit practices in Nigerian and United Kingdom firms, the paper demonstrates that automation enhances audit efficiency, data coverage, and risk prediction while simultaneously redefining the epistemology of assurance. The conceptual–empirical design draws on secondary scholarship, including Beloucif et al. (2024), Krieger, Drews and Velte (2021), and Rodrigues et al. (2023), to situate AI adoption within broader debates on audit ethics, governance, and professional competence. Findings reveal that digital audit transformation yields measurable operational benefits but introduces new dependencies on data integrity, explainability, and governance frameworks. The study argues that professional scepticism and ethical reasoning must evolve into algorithmic scepticism, a continuous oversight function over machine logic and predictive outputs. Policy implications highlight the need for continuous education, AI literacy, and harmonised international governance to maintain public trust. Ultimately, digital transformation in auditing is not a displacement of human auditors but an expansion of their role as interpreters and governors of intelligent systems.