<![CDATA[Information is very important in the economic world because it affects how industries make choices in today's fast-growing economy. It's essential to learn how to find valuable knowledge for the company's operations quickly. The goal of this work is to create a database of socioeconomic intelligence, operationalize it as a system, and use the study's results to make better decisions. Building economic understanding processes requires research into economic analysis algorithms and the development of computational representations for financial systems. This information is used to construct knowledge item architecture for financial reasoning systems. This study employs data mining methods to assess and extract relationships among dataset elements. Association rules and forecasting techniques are used to quickly and accurately retrieve relevant data for the financial intelligence sector. The research examines the application of financial intelligence mechanisms via data mining methodologies. The article discusses the dataset and reveals that the suggested algorithm's classification accuracy surpasses that of the Logistic Regression (LR) technique by 2.76%. This illustrates the efficacy of the devised system in obtaining and analyzing economic intelligence data. Research on sophisticated algorithms and their use in financial intelligence platforms could improve the precision and effectiveness of data collection and analysis. The results of this study provide a basis for enhancing financial decision-making and underscore the potential for further innovation in this field.]]>
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