A Systematic Literature Review on the Integration of Blockchain and Artificial Intelligence for Fraud Detection in Auditing

Abstract

The deliberate integration of advanced technologies has significantly transformed modern organizations, particularly in fraud detection and audit enhancement. This systematic review examines 30 scholarly articles published between 2020 and 2025, focusing on the integration of blockchain distributed ledger technology and artificial intelligence (AI) in audit frameworks. The findings reveal that blockchain ensures immutable audit records and data authenticity, while AI enables rapid anomaly detection and complex pattern recognition. When combined, these technologies greatly improve corporate governance, reduce fraud detection time from weeks to real-time, and increase detection accuracy by up to 95%. Using the UTAUT framework, the study identifies key factors influencing the adoption of blockchain-AI systems, including performance expectations, implementation effort, social influence, and organizational infrastructure. Organizations adopting these integrated solutions experience enhanced audit productivity, improved fraud detection precision, and increased stakeholder trust. However, significant challenges remain, such as complex implementation processes, high financial costs, regulatory uncertainties, and the necessity for organizational change management. Overall, this research provides a comprehensive framework for understanding how emerging technologies are revolutionizing fraud detection in auditing. It offers valuable insights and practical guidance for auditors, corporate leaders, and policymakers on effectively implementing blockchain-AI integrated systems. By addressing deployment challenges and leveraging their combined strengths, organizations can significantly strengthen fraud prevention strategies and elevate audit quality, marking a transformative step toward more transparent, accurate, and efficient auditing practices.