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The object of this study is the business processes of making an investment decision based on determining the state of the investment attractiveness of the enterprise. To support the adoption of investment decisions under the conditions of a fast-moving and dynamic environment, information-analytical support to the algorithm using intelligent information systems has been developed. The relevance of the study is justified by the continuous development of digitization processes, in particular in the financial realm. The traditional approach to the reproduction of management decision-making technology is complemented by the tools and methods of intelligent information systems. In particular, the modeling of the target subject area using UML made it possible to determine the main requirements for the projected information-analytical support (user roles, available options, types of connections and the logic of interaction between them). SQL queries to the information database speed up the process of processing and obtaining the necessary data samples. Business intelligence (BI) tools are used to create interactive reports that provide access to operational financial data. At the stage of making investment decisions, these tools make it possible to study a wide range of analytical data based on the results of the assessment of the investment attractiveness of the enterprise obtained at the previous stage of the developed algorithm. Monitoring of the main indicators of the enterprise's investment attractiveness is carried out on the basis of a dashboard, an information panel (display) with graphs, tables, and figures that clearly reflect the dynamics and rates of change of the investigated indicators. The results of the use of algorithmic information-analytical support make it possible to quickly prepare and make investment decisions. A visual description of the projected information-analytical support, visual content of the results of investment analysis, the validity of decisions due to the use of reliable retrospective information from an aggregated database
Vnukova et al. (Fri,) studied this question.