APPLICATIONS OF DATA MINING AND ARTIFICIAL INTELLIGENCE IN BANKING RISK MANAGEMENT AND ANTI-MONEY LAUNDERING (AML): A GLOBAL REVIEW AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.55439/EIT/vol13_iss3/709Kalit so‘zlar:
artificial intelligence, data mining, machine learning, deep learning, anti-money laundering (aml), financial crime detection, banking risk management, graph neural networks, federated learning, explainable ai, regtech, blockchain, financial compliance, transaction monitoring, anomaly detection, privacy-preserving analytics, fraud prevention, global financial systems.Abstrak
The increasing digitalization and globalization of financial systems have significantly heightened risks related to money laundering, fraud, and financial crimes. Traditional rule-based Anti-Money Laundering (AML) mechanisms often struggle to detect complex and adaptive criminal schemes, producing high false-positive rates and operational inefficiencies. This study provides a comprehensive global review of data mining and artificial intelligence (AI) applications in banking risk management and AML systems. It examines machine learning, deep learning, and hybrid models that enhance transaction monitoring, anomaly detection, and customer risk profiling. The research also explores advanced data mining techniques—such as clustering, association rule mining, and graph analytics—that reveal hidden patterns and networked financial behaviors. Furthermore, the paper highlights key challenges including data privacy, class imbalance, model explainability, and integration into legacy infrastructures. Emerging solutions such as federated learning, explainable AI (XAI), and blockchain-based compliance frameworks are discussed as pathways toward transparent, collaborative, and scalable AML ecosystems. The findings emphasize that AI-driven risk management can significantly improve detection accuracy, operational efficiency, and regulatory compliance, fostering more resilient and trustworthy financial systems in the global digital economy.
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