Information and Analytical Support for Adapting Financial Management in E-Commerce Enterprises to Market Changes
Анотація
The research focuses on the problem of forming an effective information and analytical support system for adapting financial management in e-commerce enterprises to dynamic market changes. The research methodology includes the use of hybrid forecasting methods based on ARIMA-GARCH and neural networks, digital twin modeling of business processes, real-time stream analytics, approaches to AI-based threat analytics in cybersecurity, and dynamic AI pricing with elements of hyper-personalization. The theoretical significance of the study lies in the development of an integrated model that ensures interconnection between analytical, automated, protective, and adaptive components of financial management. The applied value lies in the creation of practical tools for improving forecast accuracy, optimizing costs, reducing financial risks, and enhancing customer interaction in the digital economy. The research results demonstrate that the proposed information and analytical support system enables e-commerce enterprises to adapt financial management to market changes more precisely, rapidly, and securely, while ensuring increased profitability and competitiveness.
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