Authors : Mark Sekinobe; Kevin Mukasa; Faith Isabella Nayebale; Jimmy Kato

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/mvejwjk6

Scribd : https://tinyurl.com/4xwdzbjs

DOI : https://doi.org/10.38124/ijisrt/25dec1138

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : 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.

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