Artificial Intelligence-Assisted Anti-Money Laundering Compliance for Small and Medium-Sized Businesses: A Risk-Based Governance Framework

Abstract

Artificial intelligence (AI) is increasingly presented as a solution to persistent weaknesses in anti-money-laundering (AML) compliance, including high false-positive rates, fragmented customer data, delayed investigations, and limited compliance capacity. Yet most discussions focus on banks and large financial institutions, leaving small and medium-sized businesses (SMEs), fintech firms, money-service businesses, professional service providers, and other regulated or exposed enterprises without a proportionate implementation model. This conceptual article examines how AI can support business-facing AML functions while preserving legal accountability, explainability, privacy, and human judgment. The article identifies five high-value use cases: customer due diligence, transaction monitoring, adverse-media screening, network analysis, and investigation support. It does this by using international AML standards, regulatory guidance, AI-risk governance frameworks, and recent financial-crime technology literature. It also evaluates major risks, including biased data, opaque models, automation bias, model drift, cyber manipulation, privacy violations, and the use of generative AI by criminals. The article proposes the Responsible AI-AML Governance Framework, organized around six pillars: risk scoping, data governance, model validation, human oversight, documentation and auditability, and continuous monitoring. The framework is designed as a proportionate roadmap for businesses with limited resources. The article concludes that AI should augment, rather than replace, accountable compliance professionals and that effective adoption depends more on governance quality than algorithmic sophistication.