(untitled) (Journal of Soft Computing Exploration, 2026)
The rapid growth of digital financial services has significantly increased fraud risks, threatening the security of global financial systems. This study addresses the limitations of traditional fraud detection by analyzing the role of Artificial Intelligence (AI) as a real-time prevention mechanism. Using a Systematic Literature Review (SLR) of 24 scientific articles published between 2019 and 2025, this research evaluates AI’s effectiveness, implementation challenges, and its synergy with Big Data, Blockchain, and AutoML. The findings demonstrate that AI models, particularly Deep Learning and Machine Learning algorithms, provide superior accuracy in anomaly detection compared to conventional rule-based systems. However, implementation is often hindered by data scarcity, high false-positive rates, and infrastructure costs. The study concludes that a collaborative framework—integrating AI for predictive analysis, Blockchain for data integrity, and Big Data for scalable processing—creates a more robust and adaptive defense against sophisticated financial crimes. These insights provide a conceptual foundation for developing more comprehensive digital security ecosystems.