Artificial Intelligence and the Transformation of Accounting Information Systems: A Systematic Literature Review on Enhancing Audit Quality and Fraud Detection

Abstract

The rapid integration of artificial intelligence (AI) into accounting information systems (AIS) has fundamentally altered audit practice, offering new capabilities for enhancing audit quality and detecting fraud. This study presents a systematic literature review to synthesize empirical and conceptual evidence on how AI-driven AIS transformation influences audit quality and fraud detection. Following PRISMA 2020 guidelines, 58 peer-reviewed articles published between 2015 and 2025 were analyzed from Scopus, Web of Science, and Google Scholar. Thematic synthesis revealed three dominant AI technology clusters: machine learning, natural language processing, and robotic process automation. Findings indicate AI improves audit quality through continuous auditing, broader data coverage, reduced sampling risk, and enhanced professional skepticism support. For fraud detection, AI algorithms—especially ensemble classifiers and deep learning—achieve higher precision and recall than traditional rule-based red flags, although data quality and algorithmic bias remain constraints. The study proposes an integrative conceptual model linking AIS automation maturity to audit quality and fraud detection effectiveness. Practical implications for regulators, accounting educators, and audit firms in emerging economies such as Indonesia are discussed. Future research should focus on longitudinal designs, algorithmic transparency, and human-AI collaboration