With the development of the mobile Internet, a large amount of structured and unstructured data is generated every day. Taking the banking industry as an example, China UnionPay involves 4.3 billion bank cards, more than 900 million cardholders, more than 10 million merchants, and nearly 70 million transaction data per day, with core transaction data exceeding terabytes. However, in the face of massive amounts of data, traditional financial data information processing systems face the following challenges:

• Traditional centralized architecture with low concurrency, unable to cope with transaction surges • Risk control only supports simple real-time control like blacklist, complex rules cannot be processed in real time, mostly post-processing.

• Mainly structured data, unable to process semi-structured data, customer behavior is used to participate in rule calculation and rule configuration, the support is not high.

• Some rules cannot be directly configured online and need to be implemented through development programs.

      Based on a unified platform for enterprise-level big data storage, query, and analysis, it quickly builds a massive data information processing system for financial institutions. Through real-time and non-real-time analysis and mining of various types of massive data information, it helps banks obtain information from massive data Real value, timely insight and decision-making on new opportunities and risks.

Realize the big data strategy of financial institutions and provide solutions covering risk control, anti-money laundering, data governance and data storage.