Blockchain and AI in Combating Financial Corruption: A Systematic Literature Review - Rimal Mahdani; Dara A. Soufyan; Rimal Mahdani; Dara A. Soufyan (Journal of Central Banking Law and Institutions, 2026)
Corruption in the financial sector threatens economic stability, resource allocation, and public trust. This study explores how blockchain and artificial intelligence (AI) can combat this corruption. Using a systematic literature review (SLR) guided by the PRISMA methodology, we analysed articles from 2020 to 2024. Findings show that blockchain enhances transparency through immutable, decentralised ledgers, while AI improves fraud detection through realtime anomaly detection and predictive analytics. Case studies reveal successful applications, such as greater accountability in public procurement and enhanced fraud detection in banking. However, adoption of these technologies faces challenges, including scalability, regulatory hurdles, and data privacy concerns. Integrating blockchain and AI into financial institutions’ operations can strengthen existing anti-corruption measures, boosting transparency and accountability. Yet, this study is limited by the technologies’ early development stage and the shifting regulatory environment. Future research should address barriers to unlocking the full potential of AI and blockchain to build a more equitable financial system.