AGRICULTURAL BANK OF CHINA
Introducing Galaxybase graph database to establish a crucial foundation for business innovation.
Graph databases organize data using graph structures, overcoming the pain points of deep relational data analysis that are difficult to support by other databases. They provide important technological support and technical guidance for building a banking knowledge graph, enabling deep business knowledge and value discovery.
China Agricultural Bank Research and Development Center - Luo Xiaofeng
Customer Profile
Established in 1951, the Agricultural Bank of China is a large state-owned commercial bank and a vice-ministerial-level unit. With branches across urban and rural China, it has become one of the largest modern commercial banks in China with the most extensive network and business coverage.
Business Pain Points
Acceleration of transformation, high demand for data correlation analysis
With the development of informatization, internal data analysis has become more complex and patterns change frequently. There is a growing demand for multiple correlation analysis, knowledge graph construction, deep data value exploration, etc. Traditional relational databases are unable to fully meet the requirements for analyzing and processing large-scale correlated data.
The existing system management is decentralized, leading to high operational costs.
Within the bank, there are various complex business systems that operate independently of each other. These systems have different requirements for data utilization and analysis, hence the adoption of different open-source graph databases. However, the duplicated construction has not achieved data, model, and algorithm sharing and unified management. High operational management costs have resulted in wastage of software and hardware resources.
solution
Based on the above business pain points, the Agricultural Bank plans to establish a unified, standardized, bank-wide graph computing service system to create the Agricultural Bank's graph database technology application foundation, supporting applications across various major systems and business scenarios within the bank
Galaxybase graph platform can provide Database as a Service (DbaaS) based on cloud architecture, effectively managing the unified scheduling, allocation, management, and monitoring of graph storage and computational resources for various business lines and different user permissions. It supports efficient business queries and data mining for each business line.
Graph Computing Platform Technical Architecture Diagram
Application Scenarios
Currently, Agricultural Bank's graph database applications have achieved phased results in areas such as credit risk control, intelligent marketing, internal audit, system operation and maintenance, and knowledge graphs.
Group Customer Risk Monitoring
Two models are built based on the graph database for analyzing group customer relationships and conducting risk analysis. The models explore up to 10 layers of equity penetration in group shareholder relationships, delving deep into various implicit relationships. The overall accuracy rates of the two models reach 82.94% and 81.99% respectively, enabling precise identification of hidden group customers and risky groups, effectively preventing and mitigating large-scale risks.
Application System Relationship Analysis
Based on the graph database, thousands of dependencies among internal IT systems are mapped out and visualized. In case of a failure in a single system module, timely identification and rapid analysis of potentially affected downstream systems are conducted, forming an efficient analysis of system failure impacts. This significantly enhances operational efficiency and elevates the level of system maintenance.
Knowledge Graph Application
Utilizing a graph database to build a knowledge graph engine, maximizing the value of the bank-wide graph assets. In the scenario of identifying suspicious customers in anti-money laundering operations, Agricultural Bank relies on the knowledge graph engine. By constructing a graph containing 160 million entities and 370 million relationships based on three months of transaction data, the bank successfully identified over 2,000 suspicious users with an accuracy rate exceeding 94%.
Customer Benefits
Based on the Galaxybase graph platform, Agricultural Bank's graph data storage has expanded from millions of nodes and edges during the pilot phase to a scale of tens of billions of nodes and hundreds of billions of edges. Applications have been successfully implemented in multiple OLAP and OLTP scenarios, enriching business practices.
By efficiently utilizing the Galaxybase graph platform, Agricultural Bank has achieved full-lifecycle operational management and scheduling of storage and computational resources, enhancing resource utilization efficiency and reducing operational management costs.
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