Introduction. The use of big data in the financial sector is relevant for enhancing operational efficiency, risk management, fraud prevention, and customer experience. This study is aimed at providing a theoretical conceptualization of big data, identifying its benefits, and assessing the risks associated with its adoption by financial institutions. Materials and methods. The materials used in the study were as follows: peer-reviewed journal publications in finance, economics, and data analytics; reports from financial institutions and consulting firms on big data applications in finance. When processing information, methods of theoretical analysis, classification, and synthesis were used. Results. It has been revealed that big data allows for enhanced analytical research quality, predictive modeling of economic trends and market fluctuations, comprehensive market dynamics analysis, medical data analytics for improved diagnostics and treatment selection, predictive maintenance in manufacturing through sensor data analysis, development of socio-economic programs at governmental levels, fraud and corruption detection in financial systems, etc. The analysis substantiates both the rapid evolution of big data technologies and their strategic value for financial sector applications. Through literature review, the authors propose a novel definition of big data technology specific to financial institutions, incorporating distinctive features and advantages relevant to this sector. Examination of current big data implementations in finance reveals core benefits alongside existing limitations of these technologies. Conclusion. Big data applications in finance contribute to process optimization and cost reduction, advanced risk management capabilities, personalized service offerings, fraud detection and prevention, regulatory compliance enhancement. These technologies facilitate more accurate market trend forecasting and datadriven decision making.
Chernenkov et al. (Sun,) studied this question.