Huawei FusionInsight Helps CMB Explore the Value of Big Data

Established in 1987, China Merchants Bank (CMB) is the first joint-stock commercial bank that enjoys full legal entity status in China. Sticking to the core values including “Service, Innovation, and Stability”, CMB has been titled the Best Retail Bank in China for years.

Relying on emerging IT technologies such as cloud computing and big data, Internet financial services develops rapidly, providing more innovative services and better user experience. The traditional financial service mode of commercial banks is facing strong challenges.

The traditional financial industry lacks of decision-making and service systems based on big data analysis. Currently, the traditional financial industry still holds a favorable position against customers. Banks design financial products and then customers purchase them. This mode lacks of the rewards and innovation mechanisms based on big data, resulting in low innovation strength and customer satisfaction.

The traditional financial industry calls for an urgent reform in terms of product innovation, prediction and risk control by reconstructing a decision-making and service system based on big data analysis, to improve competitiveness and customer satisfaction.

Traditional data processing platforms cannot meet the requirements of the big data era.

A large amount of structured, semi-structured, and non-structured data (such as orders, contracts, and logs) from the production system and operating system as well as data obtained from the Internet need to be processed. As the amount of financial data increases, the existing first data platform with a core of transactions cannot meet requirements due to the high cost, low scalability, and applicability only to structured data. In the big data era, the second data platform must be constructed to process data of more dimensions and larger amount.

Based on the standards of production systems, the Huawei FusionInsight big data solution strengthens the performance of the big data platform in terms of reliability, security, and ease of use, and it is the first big data platform in the industry that supports classified protection for financial data and over 1000 km remote disaster recovery. To improve the ease of use and complex query capability, the solution provides table-clustering and multi-level indexing schemes, implementing the seamless integration with the existing databases and data warehouses of the bank. With the Huawei FusionInsight big data solution, CMB implements real-time query, credit investigation, and event marketing for the historical transaction details of the past seven years and the customer-oriented precision marketing.

Huawei FusionInsight provides an enterprise-level big data platform featuring improved performance in terms of reliability, security, and ease of use:

Security: FusionInsight supports the role-based user access control (RBAC) to eliminate risks in the Hadoop distributed file system (HDFS) plain-text data storage. It is also the first big data platform in the industry that supports classified protection for financial data.

Reliability: FusionInsight is the first big data platform in the industry that supports remote disaster recovery at a distance of over 1000 km and provides high-availability (HA) design for all its components to ensure data reliability and consistency.

Ease of use: The unique table cluster and multi-level index features are specially designed to address the large number of association tables in the databases and data warehouses prevalently used in data-intensive industries. Those features allow FusionInsight to seamlessly integrate with the databases and data warehouses and facilitate deployment to a large degree. The SQL on Hbase, smooth application migration, automated online O&M, user-defined dashboard, and automated application development assistant features further ease enterprises’ burden in managing big data systems.

Huawei FusionInsight covers the entire life cycle of big data collection, storage, processing, insight, and service, and provides matched solutions for typical financial big data application scenarios, such as historical transaction detail query, real-time credit investigation, real-time event marketing, and customer behavior analysis. The big data insight platform FusionInsight Miner provides the feature extraction, management, and modeling of million-dimension big data to help customers with services such as potential customer forecast for small and micro credit and contingent financial asset forecast. As the platform for historical transaction detail query, real-time credit investigation, and real-time event marketing, the big data service platform FusionInsight Farmer enables customers to focus on the big data service development, and facilitates the use of big data platform.

The Huawei FusionInsight team have strong service capability for localization projects. They can help customers locate kernel-level big data problems and provide consulting and service capabilities, facilitating the use of big data and generating value.

With the Huawei FusionInsight big data solution, CMB develops a variety of innovative services, and improves its service accuracy, real-time performance, and customer satisfaction.

Unified solution covering the entire life cycle of big data: Huawei FusionInsight provides the entire life cycle solution for big data collection, storage, processing, insight and service, which meets the requirements for big data and helps commercial banks implement the data-driven operating and decision-making.

Diversified innovative services: With Huawei FusionInsight, CMB quickly develops a variety of innovative financial services, such as the online historical detail query, real-time credit investigation, real-time event marketing, potential customer forecast for small and micro credit, contingent financial asset forecast, and precision recommendation for financial products.

Improved service efficiency: The online historical detail querying function supports the query for historical details of seven years, compared with the one-year data amount that can be queried in the past. The potential customer forecast for small and micro credit improves the conversion rate by 40 times. The error rate of contingent financial asset forecast is reduced by half. Effective purchase customers can be covered by less than 20% of the original amount of recommendation texts. The credit investigation duration for credit cards is reduced from two weeks to less than 10 minutes.

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