AI-Enabled Risk Detection and Compliance Governance in Fintech Portfolio Operations

Abstract

The rapid growth of the fintech sector has introduced unprecedented complexity, speed, and risk to portfolio operations, challenging traditional models of risk management and regulatory compliance. This paper investigates how artificial intelligence (AI), machine learning (ML), and predictive analytics (PA) support strengthen fraud detection, enhance compliance oversight, and proactively manage risk across fintech portfolios.

Using publicly available data from a leading global payments provider’s tokenization framework and compliance initiatives, the study demonstrates how AI-driven tools can automate risk identification, detect anomalies in real-time, and focus mitigation strategies based on risk forecasting (Owen, 2022). A maturity model is introduced to assess operational readiness for AI adoption in risk operations, providing a structured path to improved agility, transparency, and governance.

By positioning AI as a critical enabler of operational resilience, this research offers practical strategies for fintech entities to align compliance initiatives (e.g., PCI-DSS, PSD2) with AI-enabled decision support systems. The findings highlight the development of scalable, future-proof governance frameworks that reduce fraud exposure, enhance auditability, and ensure stakeholder trust in increasingly complex digital financial ecosystems.