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Рішення для оброблення великих обсягів даних у фінансової галузі

Huawei’s Financial Big Data Solution, the FusionInsight unified platform, is based on enterprise-level Big Data storage, query, and analysis. The solution rapidly constructs massive data processing systems, allowing financial institutions to obtain real value from information through mass data mining and both real-time and non-real-time analysis. This provides enterprises with timely insight, aiding in their informed decision-making.

Customer Challenges

• Traditional centralized architectures, low concurrency, and an inability to handle transaction surges

• Risk control that only supports blacklist and other simple real-time control systems, an inability to handle complicated real-time rules, and the problem that issues are usually addressed after they have occurred

• Mainly structured data, an inability to process semi-structured data, customer participation in rule calculation and rule configuration, and insufficient level of support

• Inability to configure some rules online, requiring additional program development

Huawei’s financial Big Data technology provides real-time anti-fraud solutions for financial institutions, transforming traditional post-event tracking into real-time control. 

Solution Scenarios:Real-time anti-fraud based on Huawei Big Data
Huawei’s financial Big Data technology provides real-time anti-fraud and precision marketing solutions. The traditional transaction process is triggered through the transaction channel before directly entering the core system. The anti-fraud system begins tracking and analysis only after the transaction is completed. However, the transaction process based on Huawei’s Big Data platform is triggered after passing the transaction channel, before entering Huawei’s real-time anti-fraud system to be analyzed. Real-time control and processing will proceed — suspected fraud will be confirmed through a call, the transaction will be terminated, or the case will be transferred.

Huawei and partners have established data models that support omnichannel anti-fraud rules. These include models for service rules, time-effectiveness, risk scoring, basic rules, and customer behavior. 

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