An Interdisciplinary Framework for the Development of Intelligent Accounting, Automation Systems Integrating: Predictive Risk Analytics and Dynamic Internal Control Mechanisms to Enhance Regulatory Compliance and Fraud Mitigation in High-Risk Economic Sectors

The rapid digitalization of financial processes, coupled with increasing regulatory complexity, cyber risks, and economic volatility, has exposed the limitations of traditional accounting automation systems that rely on static rules and retrospective analysis. In response, this paper proposes an interdisciplinary framework for the development of intelligent accounting automation systems that integrate predictive risk analytics (PRA) and dynamic internal control mechanisms (DICM). Drawing on advances in artificial intelligence, machine learning, data analytics, and governance theory, the study synthesizes existing literature to illustrate how accounting systems can transition from reactive compliance tools to proactive, adaptive decision-support infrastructures. The framework emphasizes real-time risk prediction, continuous learning, automated control adaptation, and ethical governance as core design principles. Through sectoral illustrations from finance, healthcare, and technology-driven supply chains, the paper demonstrates how intelligent accounting systems enhance fraud detection, regulatory compliance, and operational resilience in high-risk economic environments. The study contributes to accounting and information systems research by providing a structured conceptual foundation for next-generation accounting automation and highlighting practical integration strategies that align technological innovation with transparency, accountability, and sustainable value creation.