FinTech Innovation and Digital Security: AI Applications for Fraud Mitigation and Regulatory Compliance in US Financial Markets

The rapid expansion of financial technology (FinTech) in the United States has transformed digital finance through innovations in payment systems, blockchain integration, and automated lending. However, this transformation has also intensified risks related to cyber fraud, data breaches, and regulatory non-compliance. Artificial Intelligence (AI) has emerged as a crucial enabler of security and compliance within FinTech ecosystems by offering predictive analytics, behavioral pattern recognition, and real-time fraud detection capabilities. This study explores the applications of AI in mitigating financial fraud and enhancing adherence to regulatory frameworks such as the Bank Secrecy Act (BSA), Anti-Money Laundering (AML) regulations, and the Dodd-Frank Act. Using a mixed-methods approach that includes data-driven model evaluation and policy analysis, the research examines how machine learning, natural language processing, and deep learning algorithms strengthen fraud detection and ensure transparency in digital transactions. The findings highlight AI’s potential to reduce false positives, improve regulatory reporting accuracy, and establish trust between financial institutions and consumers. Moreover, the study underscores the importance of ethical AI governance to prevent algorithmic bias and ensure compliance with evolving US financial standards. Overall, AI-driven innovation presents a dual opportunity to advance operational efficiency while fortifying digital resilience in the FinTech sector.