Proceedings of the International scientific and practical conference ―Science in the Era of Globalization‖ (May 22-24, 2026) / Publisher website: www.naukainfo.com. - Zurich, Switzerland, 2026. - 353 p.

120 Research literature identifies several principal approaches to data extraction, including API-based integration, web scraping, and browser automation. API integration is generally considered the most structured and reliable method, as it provides standardized programmatic access to financial datasets. However, in cases where official APIs are unavailable, restricted, or incomplete, web scraping is widely adopted as an alternative mechanism for automated extraction of publicly available web data. Browser automation extends these capabilities by simulating human interaction with web interfaces, enabling access to dynamically rendered or JavaScript-dependent content [7]. In the context of financial and economic systems, web scraping is particularly important due to the fragmented nature of data sources and the limited availability of standardized APIs. As highlighted in the literature, many financial datasets are distributed across heterogeneous platforms, requiring the combination of multiple extraction strategies to ensure completeness and reliability [8]. Recent studies further emphasize that web scraping is increasingly integrated with advanced data processing pipelines, including machine learning and natural language processing techniques, to improve robustness, adaptability, and semantic understanding of extracted content [8]. This evolution reflects a broader shift from rule-based extraction toward intelligent, adaptive systems capable of handling dynamic and heterogeneous web environments. Modern approaches to data integration are evolving toward the use of next- generation interoperability protocols. In particular, the Model Context Protocol (MCP) is considered a new standard for interaction between artificial intelligence models, APIs, and external data sources, providing unified contextual integration and support for agent-based systems. Combined with API-based architectures, MCP establishes a new paradigm of interaction between data and intelligent systems, which is particularly relevant for financial analytics and automated analysis of market instruments [9,10].