The Current Status and Prospects of Data-Driven Analysis in Personal Financial Decision-Making

Abstract

At present, financial technology is deeply integrated into the personal wealth management market, and data-driven analysis based on big data and artificial intelligence is becoming a key support for optimizing wealth management decision-making efficiency and improving decision-making accuracy. Based on the actual situation of the industry's transformation towards net asset value after the implementation of the new asset management regulations, this article summarizes the basic conditions and core scenarios for applying data-driven analysis to personal financial decision-making, analyzes the prominent problems of data silos, security risks, and uneven regional development faced by the industry, puts forward targeted optimization suggestions, and looks forward to the future development direction of the industry. Research has shown that data-driven analysis, supported by both technological iteration and policy regulation, has been effectively applied in key areas such as customer profiling and asset allocation. However, it requires innovative mechanisms to overcome application bottlenecks. In the future, driven by continuous technological upgrades and policy system improvements, it will advance in depth towards personalization and inclusiveness, providing strong support for the preservation and appreciation of residents' assets and the high-quality development of the personal wealth management market.