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Personalized Digital Customer Services for Consumer Banking Call Centre using Neural Networks

机译:使用神经网络的个人银行呼叫中心的个性化数字客户服务

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Many banks and financial service companies have been transforming the way to run their businesses and serve customers. In this paper, we present a use case for digitizing customer journeys in the area of consumer banking call centre. The main objective is to provide personalized customer service experience through an integrated solution for call centre, including the Interactive Voice Response (IVR) system, SMS system, Internet Banking platform and chatbot. Topic modeling was performed on the dialogue transcript between the customers and Customer Service Officers (CSOs) to identify the customers’ reason for calling. Using the customer-level profile, transaction and servicing log data, a multi-task neural network was trained to predict if a customer is going to call the bank for any customer service request in the next 10 days. In the IVR system, a personalized voice prompt will recommend relevant digital services based on the model prediction and redirect the customer to digital services through a SMS with a URL to chatbot. Through this, the number of calls reaching the CSOs has reduced and the bank can achieve considerable operational cost savings and provide a more efficient customer service experience.
机译:许多银行和金融服务公司已经在改变经营方式和服务客户​​的方式。在本文中,我们提供了一个用例,用于数字化消费者银行呼叫中心区域中的客户旅程。主要目标是通过呼叫中心的集成解决方案提供个性化的客户服务体验,包括交互式语音响应(IVR)系统,SMS系统,网上银行平台和聊天机器人。在客户与客户服务官(CSO)之间的对话记录上进行主题建模,以识别客户致电的原因。使用客户级别的配置文件,交易和服务日志数据,对多任务神经网络进行了培训,以预测客户在接下来的10天中是否打算致电银行以应对任何客户服务请求。在IVR系统中,个性化语音提示将基于模型预测来推荐相关的数字服务,并通过带有URL的SMS将客户重定向到数字服务,并将其重定向到聊天机器人。这样,减少了到达CSO的呼叫数量,银行可以节省大量的运营成本并提供更有效的客户服务体验。

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