Digital audit talent, audit digitization and detection risk: A Systematic literature review and conceptual framework

Abstract

This paper reviews audit literature focuses on digital talent, studies audit technologies, examines detection risk. Develops conceptual framework, integrates RBV theory, applies TAM model, uses audit competency theories. Explains digital capabilities, links technology tools, enhances audit quality. Uses systematic review method, follows PRISMA guidelines, analyzes academic databases, covers 2021–2026 period. Initial sample includes 214 papers, applies inclusion criteria, focuses on digital audit topics, and selects 30 studies. Conducts thematic synthesis, identifies key constructs, examines relationships, highlights research gaps. Digital audit talent includes analytics skills, requires AI literacy, involves IT knowledge, depends on ethical judgment. Audit digitization uses AI tools, applies RPA systems, analyzes big data, and integrates blockchain technology. Reduces detection risk, enables full data testing, supports continuous monitoring, and improves anomaly detection. Technology benefits depend on skills, require trained auditors, need data governance, rely on organizational support. Skills still exist, affect audit education, limit professional practice, reduce technology effectiveness. Review shows limited evidence, lacks empirical testing, misses theoretical validation, needs longitudinal studies. Future research should expand methods, apply mixed approaches, examine mediation effects, study talent roles. Audit firms must invest, improve technology systems, develop human capital, enhance training programs.