Retail Big Data Solutions

-Retail big data applications rely on the technical system of the big data platform, based on retail credit wind control, customer group management and other business areas, the data processing method from batch processing services gradually to real-time, service-oriented, intelligent evolution, improve the timeliness and accuracy of retail business operations.

business pain points
-Inefficient data sharing within banks makes it difficult to support rapid business iteration
· Can not effectively understand the customer, can not provide personalized service according to customer needs
-Low accuracy of risk identification, which can easily lead to an increase in non-performing loan ratios

Business Objectives
Improve customer experience: dig deeper into retail customer needs and behavior data to provide customers with more personalized and convenient financial services.
Optimize risk management: to achieve comprehensive identification, accurate assessment and effective control of risks.
Strengthen data governance: ensure data quality, security and compliance through a sound data governance system.
· Realize intelligent operation: through the application of retail big data, realize the intelligence and automation of business, and improve the operation efficiency and service quality.

technological innovation
Retail big data applications process risk data based on big data components to shorten batch run time, introduce and explore real-time engines, provide real-time indicator display, and expand system delivery capabilities, deliver intelligent marketing requirements, and provide data empowerment for customer, activity, and customer manager marketing.

Customer Value

retail big data application is a new risk data support system that is application-oriented, data-centric, and based on a big data platform:
-Promoting the effectiveness of bank credit decision-making
Improve the bank's risk data management capabilities.
· Improve the overall level of intelligent marketing of banks