AI-Powered Regulatory Compliance and Fraud Detection in Automated Payment Systems: A Review of Current Approaches and Future Directions
DOI:
https://doi.org/10.32628/CSEIT251117128Keywords:
Regulatory Compliance, Fraud Detection, Artificial Intelligence, Machine Learning, Natural Language Processing, FinTech, RegTechAbstract
The rapid digitalization of financial transactions demands robust, adaptive, and intelligent systems capable of ensuring regulatory compliance and preventing fraudulent activity in automated payment environments. This paper presents a comprehensive review of existing approaches to AI-driven compliance monitoring and fraud detection, analyzing methods across rule-based, machine learning (ML), natural language processing (NLP), and hybrid frameworks. Through a synthesis of literature and industry practices, we identify major gaps related to scalability, explainability, regulatory language interpretation, and real-time adaptability. Building upon these insights, we propose a conceptual AI-powered framework integrating ML-based anomaly detection with NLP-based compliance automation. The proposed model highlights future directions for real-time compliance assurance and proactive fraud mitigation in next-generation payment ecosystems.
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Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
