ARTIFICIAL INTELLIGENCE GOVERNANCE IN FINANCIAL SERVICES: INTERNATIONAL REGULATORY APPROACHES AND FUTURE CHALLENGES

Artificial intelligence (AI) is moving from peripheral analytics into core financial functions, including credit assessment, fraud detection, insurance pricing, customer service, trading, compliance, and operational risk management. This transition creates a governance problem because AI combines conventional model risk with new concerns involving data provenance, opacity, autonomy, third-party concentration, cyber threats, and potentially system-wide feedback effects. This article compares major regulatory approaches to AI governance in financial services and evaluates their implications for future supervision. Using qualitative comparative document analysis, the study examines ten official legal, regulatory, and supervisory sources from the European Union, the United Kingdom, the United States, and international standard-setting bodies. The results identify three regulatory models: a horizontal risk-based statutory model in the European Union; an outcomes-focused, sector-led model in the United Kingdom; and a distributed, technology-neutral model in the United States supported by existing financial laws and risk-management frameworks. Despite institutional differences, the approaches converge around accountability, data governance, validation, human oversight, transparency, operational resilience, and continuous monitoring. The article argues that future policy should combine technology-neutral financial regulation with AI-specific controls for high-impact use cases, while strengthening third-party oversight, cross-border interoperability, and supervisory capacity for generative and agentic AI.