In the transformation of retail business to wealth management, the personalized and comprehensive service requirements for customers generate demand for big data technology application; in deepening institutional business, demands related to product creation, sales, and investment management from institutional clients will also be closely linked to big data technology; moreover, multi-dimensional data sources provide an important analysis foundation. Brokerages can not only obtain unstructured text data such as internet news, but also quickly accumulate external transaction data and internal customer behavior data. Finally, breakthroughs in big data-based deep learning algorithms in natural language processing, speech semantic recognition, and image recognition, as well as the release of open-source algorithm platforms such as TensorFlow, provide strong algorithm guarantees for big data technology.

In general, big data technology empowers the transformation and development of the securities industry in three aspects: First, promote data-driven operations in the brokerage's daily business activities, using big data to improve the operational efficiency of each business line and middle/back-office function; second, use big data technology to promote intelligent applications of brokerage business and mine new business forms and opportunities; third, establish a more effective and scientific control tool based on big data and related technologies to ensure controllable business operation risks and uphold compliance bottom lines.