AI-powered financial crime prevention with cybersecurity, IT, and data science in modern banking

Financial crime in modern banking has evolved significantly with the digital transformation of financial services, presenting unprecedented challenges to traditional prevention methods. This comprehensive review examines the integration of artificial intelligence (AI), cybersecurity frameworks, and data science methodologies in combating financial crime within the banking sector. We analyze the current state of AI-powered solutions, including machine learning models, real-time detection systems, and advanced analytics frameworks that have transformed financial crime prevention. The review synthesizes findings from recent studies and industry implementations, highlighting the synergistic relationship between AI technologies and cybersecurity measures in creating robust defense mechanisms. Our analysis reveals that while AI-powered solutions demonstrate superior detection rates and reduced false positives compared to traditional methods, significant challenges remain in areas of data privacy, regulatory compliance, and system integration. The paper concludes by identifying critical research gaps and proposing future directions for enhancing the effectiveness of AI-based financial crime prevention systems. This review provides valuable insights for researchers, banking professionals, and policymakers working at the intersection of AI, cybersecurity, and financial crime prevention.