Financial Industry

Due to the specific nature of its operations, the financial industry faces challenges such as decentralized data construction across headquarters and branches, difficulties in cross-departmental communication, and inconsistent data quality. With urgent needs for third-party administrator (TA) elimination and localization replacement in business development, the industry requires a mature and stable data infrastructure, as well as a leading data capability system in the financial field. It aims to establish a comprehensive data value chain, realizing financial data centralization, standardization, assetization, valorization, and service orientation, to fully ensure data security. Furthermore, continuous data asset operation, management, and innovative business applications are necessary to maintain a leading position in the digital transformation.

Industry Pain Points

Traditional Data Warehouses Are Unsustainable
Traditional data warehouses built in earlier times struggle to support the surge in data, encountering bottlenecks in data processing performance.
Widespread Data Distribution
Data is produced from numerous sources, and integrating a large volume of heterogeneous data is challenging, leading to issues of data silos.
Heightened Security Requirements
Due to the financial nature of industry data, there is a need for better security mechanisms to protect sensitive data such as user information and transaction details.
Insufficient Data Timeliness
The demand side, including marketing, investment research, operations, and investment advisory, has increased requirements for data timeliness. Offline data processing cannot meet these scenario needs.
Intelligent Transformation Is Urgent
In the transition from traditional to intelligent applications, traditional technologies struggle to deeply extract data value to support the intelligent transformation of the fund industry.

Solution Architecture

Customer Benefits

Massive Data Support
The big data platform built on distributed systems can support computation and exchange of data volumes in the petabyte (PB) range and beyond.
Heterogeneous Data Integration
A batch-streaming integrated data synchronization system supports the connection, integration, and accumulation of massive heterogeneous data.
Data Security Assurance
Unified data management, resource management, permission systems, and data audits isolate risks of data breaches and protect sensitive data.
Real-Time Scenario Support
Utilizing streaming synchronization systems and real-time computing platforms, real-time application analysis is conducted on data such as real-time trading behaviors generated by fund transactions.
Intelligent Innovation
Through the algorithm service capabilities of visual modeling and model publishing on scientific platforms, innovative support for new business models and scenarios is enabled.