Developing Multi-Dimensional Control Risk Assessment Models for Intelligent Financial Systems

Abstract

This review paper explores the development of multi-dimensional control risk assessment models tailored for intelligent financial systems. The integration of artificial intelligence (AI) and machine learning (ML) technologies into financial operations has introduced new dimensions of risks beyond traditional models. This paper provides a comprehensive overview of existing control risk assessment methodologies, evaluating their applicability and limitations in the context of intelligent financial systems. The core themes addressed include data-driven risk assessment, algorithmic bias and fairness, and the challenges of model interpretability and explainability. Furthermore, this review examines the implications of regulatory compliance and ethical considerations within AI-driven financial environments. Critical analysis reveals the need for adaptive and dynamic risk assessment models capable of addressing emerging threats and vulnerabilities. The paper culminates by proposing future research directions focused on enhancing model robustness, improving real-time risk monitoring, and establishing standardized frameworks for control risk assessment in intelligent financial systems. The study synthesizes insights from diverse disciplines, including finance, computer science, and regulatory studies, to foster a holistic understanding of control risk management in the evolving landscape of intelligent finance. This review is essential for researchers, practitioners, and policymakers seeking to navigate the complexities of risk assessment in the era of AI-driven financial innovation.