The Role of Data Mining in Enhancing the Accuracy of Financial Forecasting

Abstract

The purpose of the article is to study the role of data mining in increasing the accuracy of financial forecasting. The relevance of the work is due to the rapid growth of financial data volumes and the need to use modern methods for their processing, analysis and forecasting in conditions of dynamic changes in the economic environment. The work focuses on the analysis of the basic principles, methods and tools of Data Mining, revealing its impact on forecasting financial trends, as well as identifying the main problems associated with the integration of these technologies into the financial sphere. To achieve this goal, the methods of theoretical analysis of literary sources, systematization of approaches to the classification of data mining methods, as well as a comparative analysis of the practical results of the use of Data Mining technologies in the financial sphere were used. The review was conducted with an emphasis on the practical significance of processing big data, identifying hidden patterns and forecasting market trends. As a result of the study, it was found that data mining provides new opportunities for processing significant amounts of information, increasing the accuracy of forecasts and adapting to dynamic market conditions. Methods such as artificial neural networks, decision trees and clustering have demonstrated high efficiency in detecting patterns and predicting changes in financial indicators. At the same time, the study identified key problems, including the difficulty of accessing qualitative data, the cost of implementing technologies and interpreting models that function as a “black box”. The conclusions emphasize that data mining has significant potential to improve the accuracy of financial forecasts and create competitive advantages. However, for its effective integration, it is necessary to address the issue of availability of qualitative data, reduce implementation costs and develop methods for simplifying the interpretation of models. The results obtained contribute to the further expansion of the practical application of Data Mining in the financial sector and can be the basis for further research in this area.