AI-driven risk management in online financial transactions: Enhancing cybersecurity in the fintech ERA

Abstract

This study investigates the evolving role of artificial intelligence (AI) in enhancing cybersecurity risk management within fintech platforms. It focuses on how AI-driven systems impact threat detection capabilities, regulatory compliance, and algorithmic transparency in online financial transactions. A quantitative research design was employed, analyzing cybersecurity and privacy compliance data from 15 fintech platforms across North America, Europe, and Southeast Asia. Data sources included public audits, white papers, and platform-level documentation. The study tested three hypotheses concerning AI's impact on detection accuracy, the trade-off between model complexity and explainability, and the effectiveness of privacy-by-design in achieving GDPR compliance. The results show that AI-driven systems enhance threat detection accuracy by an average of 10% over traditional rule-based methods. However, increased model complexity significantly reduces explainability, posing challenges for regulatory accountability. Platforms adopting privacy-by-design principles consistently demonstrate stronger GDPR compliance and fewer security breaches. AI significantly strengthens fintech cybersecurity performance, but it introduces critical governance challenges related to transparency and data privacy. A balanced approach integrating explainable AI and privacy engineering is essential for sustainable innovation in the sector. The findings underscore the need for platform-specific risk management models that prioritize both technical performance and ethical design. Developers and compliance teams should embed governance protocols and privacy protections into AI system architecture from inception to ensure operational resilience and regulatory readiness.