INFORMATION TECHNOLOGY OF FINANCIAL ANALYSIS - BERKO, ANDRII; ODREKHIVSKYI, MYKOLA; YAROMYCH, MAKSYM (Herald of Khmelnytskyi National University. Technical sciences, 2024)
This work contains research on the most common methods of utilizing information technology for financial analysis automation based on a review of relevant literature. It demonstrates that this topic is highly relevant, as information technologies provide the opportunity to significantly optimize the process of financial analysis and to make efficient, optimal decisions in correspondence with market realities. The study provides an analysis of prominent literature on this subject, showing a wide variety of applications of information technology in this area, as well as the fact that the topic is actively studied by scientists worldwide. It examines promising areas such as big data analysis and the application of artificial intelligence, focusing on the use of these technologies in financial analysis, as well as the potential of utilizing the Python programming language and features of Microsoft Excel and MATLAB environments. It was discovered, in particular, that big data analysis is convenient for tracking market trends and anomalies, while artificial intelligence tools allow for the automation of repetitive tasks based on identified patterns. Microsoft Excel is convenient for working with small amounts of data, whereas Python and MATLAB are suitable for tasks involving the development of complex mathematical models that require extensive numerical calculations. Possible risks associated with the application of these technologies were also considered. For example, the high complexity of artificial intelligence algorithms and the presence of "noise" in the data can lead to inaccurate results, while Microsoft Excel may lack specialized data processing capabilities. For each automation technique, examples of their suitable use have been provided, and a comparative table with the advantages and disadvantages of each technology has been created. Conclusions have been made regarding the efficient use of analyzed technologies in different cases and the prospects for further research on this topic.