The Role of AI-Enabled Digital Twins in Managing Financial Data Risks for Small-Scale Business Projects in the United States

Abstract

The rise of financial data risks in small-scale business projects poses significant challenges, particularly in ensuring data accuracy, security, and resilience against evolving cyber threats. This review examines the transformative role of AI-enabled digital twins as an innovative solution for managing these risks in the United States. Digital twins, virtual replicas of financial systems, use AI to simulate, monitor, and predict potential vulnerabilities in real-time, enabling proactive risk mitigation and enhanced decision-making. The paper explores the integration of AI technologies such as machine learning and natural language processing within digital twins, emphasizing their capabilities in anomaly detection, data validation, and predictive analytics. Furthermore, it highlights case studies demonstrating the practical implementation of AI-enabled digital twins in financial risk management for small businesses. By addressing regulatory compliance and scalability concerns, this paper outlines a pathway for adopting digital twin technology to foster robust financial data governance in small-scale business environments.