AI-driven business analytics for financial forecasting: a systematic review of decision support models in smes
Abstract
The accelerating convergence of artificial intelligence (AI), business analytics, and financial management has redefined how small and medium-sized enterprises (SMEs) forecast cash flows, allocate resources, and navigate volatile market conditions. Yet, research on the breadth and depth of AI‐driven decision support models for SME financial forecasting remains fragmented. Addressing this gap, the present systematic review and meta-analysis synthesizes findings from 78 peer-reviewed studies published between 2015 and 2025, each investigating the deployment of machine-learning, deep-learning, or hybrid-intelligence systems in SME forecasting and budgeting contexts. Guided by PRISMA protocols, we searched five major databases—Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar—followed by rigorous title, abstract, and full-text screening. Eligibility criteria required empirical, quantitative evidence, explicit focus on SMEs, and sufficient statistical detail to calculate standardized effect sizes. Ultimately, 67 studies met all inclusion standards and were subjected to meta-analytic pooling using a random-effects model. The aggregated results reveal a robust, statistically significant improvement in financial-forecast accuracy, decision speed, and overall financial performance among AI-adopting SMEs. Hybrid frameworks—those combining human expertise or traditional statistical methods with machine learning—produced the largest gains, underscoring AI’s role as…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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