AI-Driven Anti-Money Laundering Systems and Compliance Costs in Malaysian Fintech Smes: the Roles of Operational Efficiency and Toe Readiness
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Abstract
Anti-money laundering (AML) compliance has become more technologically-intensive and costly, creating a difficult trade-off for small and medium-sized financial technology (FinTech) firms. This conceptual paper develops a framework explaining how AI-powered AML systems could reduce regulatory compliance costs for Malaysian FinTech SMEs and how these cost effects depend on operational and organisational conditions. Informed by AI-AML research, the Technology-Organisation-Environment (TOE) framework, and the Resource-Based View (RBV), the article contends that artificial intelligence will not automatically reduce compliance expenditure. In contrast, operational efficiency can be enhanced through machine learning, natural language processing, and graph-based monitoring, which can help reduce unnecessary alert review, speed up investigation and reporting, and shift compliance capacity from low-risk to higher-risk cases. Thus, operational efficiency is identified as the primary mediating mechanism between AI-AML adoption and compliance costs. Technological preparedness, organisational preparedness, and environment-related pressures are theorised as boundary conditions that strengthen/weaken the value realised from adoption. Due to tightening AML obligations and Bank Negara Malaysia’s simultaneous advancement of policy agenda for responsible AI adoption in financial services, Malaysia is a timely setting. In the article, a parsimonious framework and testable propositions have been provided for empirical validation through PLS-SEM and explanatory interviews with Malaysian FinTech SMEs
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