Collaborative decision engine application framework

Abstract

The invention discloses a collaborative decision engine application framework, and relates to the technical field of computers. Comprising a data base layer, a decision engine layer, an artificial intelligence layer and a service access layer. The data base layer is used for storing customer information and index labels of the customer information; the decision engine layer is used for receiving the event acquired by the service access layer and performing index operation according to the event to obtain a rule decision; the artificial intelligence layer is used for storing a model library, performing model operation, training and algorithm fitting, and providing real-time estimation and batch estimation modes for the decision engine layer to call; and the service access layer is used for providing a closed-loop analysis function, a system management function and a channel access management function. According to the invention, analysis can be carried out according to customer information to obtain rule decisions, and customer classification is rapidly realized.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a collaborative decision engine application framework. Background technique [0002] In the context of the big data era, the retail marketing line of banks needs to identify customer needs and resolve the risk of information asymmetry in order to win business development opportunities. The technical pain points and problems currently encountered are, first, the amount of marketing customer data is not enough to support the precise identification of customer needs. In terms of customer marketing, the grouping of customers is not detailed enough, and there is a lack of comprehensive, multi-dimensional, and dynamic customer portraits for customers, and a deeper insight into customer needs. Second, batch marketing decisions lead to marketing timeliness that fails to cover all customer scenarios. The marketing plan focuses on selling products, does not incorporate scenario-based an...

AI Technical Summary

Problems solved by technology

In terms of customer marketing, the grouping of customers is not detailed enough, there is a lack of comprehensive, multi-dimensional and dynamic customer portraits for customers, and a deeper insight into customer needs; second, batch marketing decisions lead to marketing timeliness that fails to cover all customer scenarios

The marketing plan focuses on selling products, does not incorporate scenario-based and event-based thinking, and cannot capture customer marketing events and scenarios in real time, resulting in low marketing efficiency; third, in the marketing execution process, fragmentation is serious, mainly relying on Manual intervention and expert experience, lack of personalized product recommendations for customers, unable to capture long-tail users

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