Implementing Artificial Intelligence in Auditing: A Systematic Literature Review of Trends, Challenges, and Adoption

Abstract

This study aims to systematically review and synthesize the application of Artificial Intelligence (AI) in the field of auditing, with a focus on development trends, benefits, challenges, and differences in adoption levels between the global context and Indonesia during the 2020–2025 period. The research population consists of reputable scientific journal articles that discuss the use of AI in financial auditing. This study employs a Systematic Literature Review (SLR) method following the PRISMA 2020 protocol. Article selection was conducted using the Scopus (Q1–Q4) and Sinta (1–2) databases based on predefined inclusion and exclusion criteria, resulting in a final sample of 15 articles. Thematic analysis was applied to identify key patterns and dominant themes within the selected literature. The findings indicate that global auditing practices increasingly utilize machine learning, natural language processing, and robotic process automation to support risk-based auditing, fraud detection, and continuous auditing. AI implementation has been shown to significantly enhance audit efficiency, accuracy, and overall audit quality. Nevertheless, several challenges persist, including high technology investment costs, ethical and data privacy concerns, limitations in auditor competencies, and infrastructure constraints, particularly in developing countries. Comparative analysis reveals that global audit firms are positioned at the innovators and early adopters’ stage, while Indonesia remains at the early majority stage of AI adoption. This study concludes that successful AI implementation in auditing requires an integrated framework that aligns technological readiness, auditor acceptance, and innovation diffusion to sustainably improve audit quality in Indonesia.