AI-Augmented Compliance Adaptive Risk Intelligence for Detecting Emerging Financial Crime Patterns in Multinational Corporations

Abstract

Advancing growth and internationalisation in the financial system increased the contribution of multinational corporations (MNCs) to becoming the most vulnerable target of advanced financial crimes. Rule-based compliance environments cannot keep up with new threats, which usually results in violations of regulations, loss of finances, and reputational harm. This conceptual research paper will examine how Artificial Intelligence (AI) could offer an effective compliance process and highlight emerging trends of financial crimes through the combination of Artificial Intelligence (AI) and adaptive risk intelligence. Based on the existing theories of Fraud Triangle, Enterprise Risk Management (ERM), and Adaptive Systems Theory, the paper suggests an AI-augmented compliance framework integrating such technologies as machine learning, natural language processing and anomaly detection. It also looks at some critical challenges, such as data privacy, regulatory limitations, a lack of standardization, and AI systems' explain ability. The report shows the advantages MNCs could enjoy, such as active search of risks, enhanced worldwide compliance administration, and smart utilization of resources. The paper has established the importance of dynamic and self-learning compliance architectures that can balance technology developments and regulatory requirements through a thorough literature review and theoretical underpinnings. It ends with strategic propositions of ethical and scalable application of AI in cross-border compliance functions. Nevertheless, with the supplement of AI-powered adaptive systems, there is a chance to redesign how financial crimes are detected and raise corporate resilience in the fast-changing, highly risky market environment.