ANALYSIS OF ARTIFICIAL INTELLIGENCE IN FINANCIAL REGULATION IN MALAYSIA, INDONESIA, AND THE UNITED STATES

Abstract

This study examines the regulatory frameworks governing artificial intelligence (AI) in financial markets in the United States, Malaysia, and Indonesia using a doctrinal method and a structured questionnaire with 403 respondents. Grounded in institutional theory, this study examines the regulatory, normative, and cultural-cognitive pillars that influence AI adoption, systemic risk management, and consumer trust. The findings revealed significant disparities across jurisdictions. In the United States, robust doctrines, such as the 2010 Dodd-Frank Wall Street Reform and Consumer Protection Act, and FTC Guidance on Algorithms and AI (2020), ensure transparency, accountability, and systemic risk mitigation, fostering higher operational efficiency and consumer trust. In contrast, Malaysia’s 2020 Risk Management in Technology (RMiT) Guidelines and 2021 Capital Markets Master Plan 3 demonstrate partial effectiveness due to limited enforcement. Indonesia’s framework, including the 2108 Digital Finance Innovation Roadmap and the 2020 National AI Strategy, remains underdeveloped to address AI-related biases and systemic risks. The results from the structured questionnaire highlight a strong relationship among transparency, accountability, and consumer trust in the U.S., while Malaysia and Indonesia exhibit weaker impacts due to regulatory ambiguity. This study advocates harmonised AI governance that integrates doctrinal principles with enforceable mechanisms to ensure ethical AI deployment, systemic resilience, and investor confidence.