Abstract

Financial crime, encompassing fraud, money laundering, terrorist financing, and insider trading, presents persistent challenges to financial institutions, regulators, and law enforcement agencies. Traditional investigative approaches, relying heavily on manual analysis and rule-based systems, are increasingly insufficient to cope with the complexity, velocity, and volume of financial data in contemporary markets. Recent advancements in artificial intelligence (AI), machine learning, and data analytics provide opportunities to enhance the investigative process, enabling more accurate detection, prioritisation, and interpretation of suspicious activities. This paper proposes an AI-driven financial crime investigation framework designed to support analyst decision-making by integrating anomaly detection, pattern recognition, risk scoring, and workflow management. The framework leverages hybrid AI models to handle structured and unstructured data, incorporates feedback loops for continuous learning, and facilitates explainable recommendations to investigators. A comprehensive review of existing methods is presented, highlighting their strengths, limitations, and gaps. The proposed framework aims to reduce false positives, prioritise high-risk cases, and enhance the efficiency and effectiveness of financial crime investigations. Potential implications for regulatory compliance, operational efficiency, and investigative accuracy are discussed, providing a foundation for future implementation and evaluation in real-world financial contexts.