Study of the possibilities of using deep artificial intelligence in forecasting the green paper market in Kazakhstan

Alma Bekbolsynova (), Yesset Zhussupov (), Aida Zhagyparova (), Saule Spatayeva () and Zhanat Bulakbay ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 4, 843-852

Abstract: This study introduces a novel approach to predicting the success of small businesses in Kazakhstan, leveraging Graph Neural Networks (GNNs) to analyze a comprehensive set of business parameters. Recognizing the critical role of small businesses in the national economy, this research aims to provide stakeholders with a predictive tool that utilizes advanced machine learning techniques to evaluate business outcomes. By integrating data on revenue, number of employees, market dynamics, and other key operational metrics, the model captures the complex interactions within the business ecosystem. The methodology involves constructing a graph-based representation of the business landscape, where nodes represent individual businesses and edges denote relationships and influences among them. Through this framework, the GNN model learns to identify patterns and predictors of success, offering insights that traditional linear models might overlook. Preliminary results indicate a strong correlation between specific business parameters and their likelihood of success, highlighting the potential of GNNs in strategic decision-making. This paper not only contributes to the academic discourse on predictive analytics in business but also proposes a practical tool for entrepreneurs, investors, and policymakers in Kazakhstan to foster a thriving small business sector. Future work will focus on refining the model, incorporating real-time data, and expanding its applicability to other regions and sectors.

Keywords: Business parameters; Data-driven business insights; Ecosystem; Graph neural networks; Model. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:4:p:843-852:id:7964

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