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Small and medium enterprises (SMEs) increasingly adopt AI-based forecasting tools (machine learning, predictive analytics, and generative AI) to support budgeting, cash-flow planning, demand forecasting, and credit decisions. This paper examines how AI-driven financial forecasting affects SME decision-making efficiency — defined here as the combination of forecasting accuracy, decision latency (speed), and decision quality under resource constraints. Drawing on recent empirical studies and industry reports, the paper develops a conceptual framework linking AI adoption to improvements in forecast accuracy, real-time insight availability, automation of routine tasks, and enhanced scenario analysis capabilities. We then propose an empirical study design (mixed methods: quasi-experimental field study + surveys + interviews) to measure changes in efficiency and to identify mediating factors such as data quality, human oversight, and implementation strategy. Practical implications and policy considerations for SME managers and fintech providers are discussed.
"Impact of AI-Driven Financial Forecasting on SME Decision-Making Efficiency", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.b27-b29, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511104.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator