大数据驱动的金融服务模式优化研究
Research on Optimization of Financial Service Model Driven by Big Data

本研究聚焦大数据技术对金融服务模式的优化路径，系统分析大数据驱动的金融服务创新逻辑与实践挑战。首先梳理大数据技术的核心特征及其在金融领域的应用价值，对比传统金融服务模式的局限性，揭示数据驱动模式在风险评估、客户服务、产品创新等场景中的优势。研究发现，当前金融机构在数据质量管控、技术应用深度、组织架构适配性等方面仍存在显著短板，制约了大数据效能的充分释放。通过构建数据治理体系、强化技术研发投入、推动业务流程再造等优化策略，提出应建立“数据–技术–组织”三维协同机制，以实现金融服务的精准化、智能化升级。研究结果为金融机构数字化转型提供理论支撑，对提升行业服务效率与风险管理水平具有重要参考价值。未来随着人工智能、区块链等技术的融合发展，大数据驱动的金融服务模式将向更高阶形态演进。
This study focuses on the optimization path of big data technology for financial service models, systematically analyzing the logic and practical challenges of big data-driven financial service innovation. Firstly, summarize the core characteristics of big data technology and its application value in the financial field, compare the limitations of traditional financial service models, and reveal the advantages of data-driven models in risk assessment, customer service, product innovation, and other scenarios. Research has found that financial institutions still have significant shortcomings in data quality control, technology application depth, and organizational structure adaptability, which restrict the full release of big data efficiency. By constructing a data governance system, strengthening technology research and development investment, and promoting business process reengineering and other optimization strategies, it is proposed to establish a three-dimensional collaborative mechanism of “data technology organization” to achieve precise and intelligent upgrading of financial services. The research results provide theoretical support for the digital transformation of financial institutions and have important reference value for improving industry service efficiency and risk management level. In the future, with the integration and development of technologies such as artificial intelligence and blockchain, the big data-driven financial service model will evolve towards higher levels.