Artificial Intelligence for Market Risk and Opportunity Forecasting in Small and Medium-Sized Businesses: A Critical Review and Future Research Agenda
1 Department of Marketing, Faculty of Management Sciences, Imo State University, PMB 2000.
2 Department of Business Administration, Faculty of Management Sciences, Nnamdi Azikiwe University, PMB 5025, Awka, Anambra State, Nigeria.
Review Article
World Journal of Advanced Research and Reviews, 2019, 03(03), 171-180
Publication history:
Received on 11 July 2019; revised on 23 October 2019; accepted on 29 October 2019
Abstract:
Small and medium-sized businesses (SMEs) operate in increasingly volatile and competitive markets characterized by demand uncertainty, price fluctuations, supply chain disruptions, and rapid technological change. In recent years, artificial intelligence (AI) has emerged as a powerful tool for market risk assessment and opportunity forecasting, enabling data-driven decision-making that was previously accessible mainly to large corporations. This review critically examines the state of the art in artificial intelligence–driven market risk and opportunity forecasting models applied to SMEs, to synthesize existing knowledge, identifying methodological trends, and outlining future research directions. Drawing on peer-reviewed literature across business analytics, information systems, and applied machine learning, the review evaluates commonly used AI techniques, including traditional machine learning algorithms, deep learning models, ensemble methods, and hybrid analytical frameworks. Particular attention is paid to how these models are used to predict market demand volatility, revenue risk, customer churn, pricing dynamics, and emerging growth opportunities in SME contexts. The review also assesses the types of data employed, such as transactional records, financial statements, social media data, and macroeconomic indicators, and discusses the implications of data limitations typical of SMEs. The findings reveal that while AI-based forecasting models often outperform traditional statistical approaches in predictive accuracy, their adoption among SMEs is constrained by challenges related to data quality, computational resources, model interpretability, and organizational readiness. Furthermore, the literature shows a strong bias toward accuracy-focused evaluations, with limited emphasis on explainability, managerial usability, and real-world economic impact. This review contributes by developing a structured conceptual framework linking AI capabilities, data sources, and forecasting objectives to SME strategic outcomes. It concludes by proposing a future research agenda emphasizing explainable AI, integration of alternative data sources, ethical and governance considerations, and scalable AI solutions tailored to the unique constraints of small and medium-sized businesses.
Keywords:
Small and Medium Enterprise (SME); Artificial Intelligence (AI); Market Risk Forecasting; Opportunity Forecasting; Business Analytics; Machine Learning; Data-Driven Decision-Making
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Copyright © 2019 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0