Strategic AI-Oriented Compliance Optimization Models for FinTechs Operating Across Multi-Jurisdictional Financial Ecosystems

摘要

The expansion of FinTech enterprises across international financial ecosystems has introduced complex compliance challenges stemming from heterogeneous regulatory environments. This review investigates the strategic deployment of Artificial Intelligence (AI) to enhance regulatory compliance across jurisdictions, focusing on automation, adaptability, and real-time responsiveness. It explores AI-driven frameworks—including Natural Language Processing (NLP), Machine Learning (ML), and Robotic Process Automation (RPA)—to optimize regulatory interpretation, anti-money laundering (AML) procedures, and Know Your Customer (KYC) requirements. The study further examines how AI models facilitate risk-based compliance strategies and agile adaptation to evolving regulations such as GDPR, PSD2, and Basel III. Emphasis is placed on strategic alignment, explainability, and scalability in AI deployments that allow FinTechs to proactively manage compliance across diverse legal systems. Through a synthesis of recent advancements and implementation case studies, the paper identifies core opportunities, operational limitations, and future directions for AI-integrated compliance infrastructures in global FinTech operations.