ARTIFICIAL INTELLIGENCE, BIG DATA, AND BLOCKCHAIN TECHNOLOGIES IN FINANCIAL FRAUD DETECTION: A SYSTEMATIC LITERATURE REVIEW - Nelly Reinalda Sidabutar; Sambas Ade Kesuma; Fahmi Natigor Nasution; Keulana Erwin (Journal of Economic, Bussines and Accounting (COSTING), 2025)
Financial fraud has become one of the most critical challenges in the modern digital economy, particularly with the rapid expansion of e-commerce, mobile payments, and online financial transactions. Artificial Intelligence (AI), Big Data Analytics (BDA), and Blockchain technology have emerged as transformative tools for enhancing fraud detection, prevention, and mitigation. This systematic literature review (SLR) aims to synthesize the state-of-the-art academic research on how these technologies contribute to identifying, predicting, and controlling fraudulent activities in financial systems. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, twenty-three peer-reviewed studies published between 2019 and 2025 were analyst based on their theoretical frameworks, methodological designs, and empirical findings. The results reveal three main technological convergence trends: (1) the integration of AI and BDA for pattern recognition and anomaly detection; (2) the use of Blockchain for decentralized data security and auditability; and (3) the hybridization of AI–Blockchain–Big Data for real-time fraud prevention. The review also identifies current challenges, such as data privacy concerns, model interpretability, and the scalability of analytical frameworks. This study contributes to the literature by providing a holistic view of technological evolution in financial fraud detection, highlighting key gaps, and proposing a future research agenda for more transparent, adaptive, and intelligent financial ecosystems.