Against the backdrop of global economic expansion and diversified wealth management demands, investors are increasingly seeking diversified asset allocation beyond traditional products, promoting the integration of risk return preferences and scientific investment portfolio design with professional advisory services. Fintech and artificial intelligence technologies are reshaping the securities industry landscape, and intelligent investment advisory platforms are showing differentiated development globally due to regulatory differences. Although some markets prohibit direct agency trading, institutions still achieve asset allocation functions through standardized products such as public funds. This study focuses on the strategic transformation of the intelligent investment advisory business of a leading securities institution. By analyzing the global industry model, macro environment, and internal and external resources of the enterprise, it explores the business optimization path under technological empowerment. The research adopts literature analysis method to sort out the achievements in the field of financial technology and intelligent investment advisory, uses comparative analysis method to compare the differences in different market policy environments and typical product performance, and uses case study method to deeply analyze their operational status and internal and external challenges. The results indicate that AI and big data technologies can significantly improve the accuracy of customer profiling and the efficiency of portfolio construction, but the business is still limited by issues such as fixed scenario strategies, insufficient product innovation, and differentiated risk return performance. To overcome bottlenecks, institutions need to innovate in four areas: building hybrid AI models to support cross scenario strategy combinations; Introduce derivative and thematic investment tools; Develop dynamic performance visualization tools to enhance transparency; Improve the framework for network security and data governance. The study using the institution's "worry free investment" platform as a case further reveals that an adaptive risk control system and differentiated charging mechanism are key to ensuring sustainable development. This study provides a practical framework for technology integration and business model innovation in the intelligent investment advisory industry, and provides empirical reference for regulatory agencies to balance innovation incentives and investor protection.

Yu FD. Research on Optimization and Business Model Innovation of Financial Intelligent Investment Advisory System Based on AI and Big Data. Socio-Economic Statistics Research (2025), Vol. 6, Issue 2: 24-33. https://doi.org/10.38007/SESR.2025.060203.

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