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A small yet increasing number of banks are developing "decision hubs" driven by a predictive model portfolio and fed by readily accessible customer data to create the next-best customer, background, and channel-aware conversations.
Fremont, CA: Artificial intelligence (AI) has gradually moved through many sectors, including retail and commercial banking, from science fiction to the mainstream. In back- middle- and front-office applications, AI shows itself to be beneficial in many ways. Not all of these technologies influence consumer experience tangibly (CX). Among organizations of all sizes around the globe, those who do are of significant and increasing importance.
Here are two ways AI is influencing CX:
Chatbots, Conversational AI, and Virtual Assistants
Chatbots usually refers to natural text language interfaces used to execute such tasks as an alternative to accessing an app or browser. Celent refers to chatbots and AI-powered bots as virtual assistants for all forms of automated conversational interfaces. Chatbots of the first generation were designed using rules that facilitated linear interactions navigated by pre-defined flows. Chatbots were created for a particular and strictly defined number of tasks.
To allow complex dialogs between a customer and a computer, conversational AI uses natural language processing (NLP) and natural language understanding (NLU), that is, one with multiple turns instead of a single-turn static conversation. The AI model interprets meaning and emotion and goes beyond finding and providing a response. The dialogue can take place by voice or by text. Text is more advanced than voice apps. When the discussion exceeds the technology's capacity to provide a satisfactory result, both involve a smooth transition to a human. To make the decision, of course, AI is used.
Data Insight and Advice Generation
Many banks have a general understanding of their clients and/or groups of customers. At the individual consumer level, fewer people have a deep understanding. Some have developed the skills to recognize the profitability, lifetime value, and/or wallet share of individual clients. These are valuable insights, but they are rarely used to educate individual customers on how banks communicate. It is unusual to operationalize the use of customer data to shape actionable insights that inform real-time customer conversation at all touchpoints. But it will not be for long, thanks to AI.
A small yet increasing number of banks are developing "decision hubs" driven by a predictive model portfolio and fed by readily accessible customer data to create the next-best customer, background, and channel-aware conversations. Initially developed to optimize sales efficiency in many banks, these hubs are increasingly designed to support diverse business objectives. However, they are narrowly prioritized against each other to ensure that banks engage customers at each point of interaction, from branch to contact center, with the most appropriate next-best-conversation possible.