The bank needs to build the middle office capability of the intelligent customer service module to drive the upgrade of the whole scene intelligent customer service

Analysis of Yi Guan ：

Although customer service has nearly 20 Years of development history , Compared with manual customer service , The customer experience is still poor . When banks apply intelligent customer service system , The secondary development cost is high 、 The manufacturer's solution can not be well adapted to the bank scenario and other problems . The bank needs to form a midrange capability related to intelligent customer service that can be reused across departments .

Background of the event

With the development of artificial intelligence technology , The application of intelligent customer service in various industries has increased rapidly .

Although customer service has nearly 20 Years of development history , Compared with manual customer service , The customer experience is still poor . When banks apply intelligent customer service system , The secondary development cost is high 、 The manufacturer's solution can not be well adapted to the bank scenario and other problems .

The scenes of intelligent customer service in the banking industry are relatively scattered , The whole scene intelligent upgrade has not been realized

According to Analysys , The construction of banking customer service includes four development stages ： Enterprise self built call center 、 Managed call center 、 Multi channel cloud customer service and full scene intelligent customer service .

Based on the above development stages , And the current development status of customer service in the banking industry , At present, the bank is in the development stage from multi-channel cloud customer service to full scene intelligent customer service , Pay attention to the intelligent upgrading of customer service system , But there are problems left over by history, such as technical debt , The scene is relatively scattered , The whole scene intelligence has not been realized .

The specific analysis is as follows ：

firstly , In Banking , Due to the business content of each business department 、 Relevant data system 、 There are huge differences in customer service channels , Each business department has the authority to purchase intelligent customer service products independently . Intelligent customer service manufacturers provide customized solutions according to the banking department , Make the customer service systems of different departments independent of each other , Form a data island .

second , In the context of data islands , There are still difficulties in getting through customer service data in the banking industry , Provide sufficient materials for in-depth learning . Even big banks , In some channels with less customer service , For example, applets 、NPS Evaluation or certain specific marketing channels , The monthly service volume is only hundreds to thousands , Deep learning cannot achieve effective training based on this order of data , So as to form a personalized service for thousands of people .

third , Full scene intelligent customer service requires the integration of multiple technology stacks , At the same time, it has a solid customer service knowledge base system , There are also shortcomings in this aspect . Intelligent customer service is not only answering customer questions 、 Voice robots that provide self-service . In a broad sense, intelligent customer service also includes agent service 、 operation management 、 Seat management and other contents , As shown in the following table ：

Diversified application scenarios and capability support lead to the construction of intelligent customer service needs to integrate a large number of products 、 technology 、 Solution . This includes not only audio and video conversations 、 natural language processing 、 Deep learning 、 Knowledge base and other related capabilities , Also include BI、OA、 Cooperative office system construction and other agent management 、 Ability in customer service operation .

in addition , voice & The accuracy of semantic recognition is only 70-90% Between , Dialect adaptation only supports dozens of regions 、 The data volume and classification dimension of the existing user behavior of the bank do not meet the requirements of in-depth learning 、 The knowledge base does not meet the personalized needs of current customers . This leads to multiple rounds of intelligent customer service 、 High complexity customer service interaction , Unable to understand the conversation 、 There is no appropriate knowledge base answer .

Analysys analysis suggests ：

According to Analysys , Banks need to build on the enterprise level architecture , Form the middle office ability of intelligent customer service module , Make a unified plan for reusable technical capabilities 、 Development and Application .

The formation of mid-range capabilities can realize the autonomy and controllability of core capabilities , At the same time, reduce technology development 、 Investment in capital investment , It can also ensure that relevant capabilities are more in line with the bank's own business needs .

The formation of middle office capability can also realize the cross business of customers 、 Cross channel service information synchronization , Enhance the customer experience .

Notice of declaration ：

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