Quality Chatbots Find an Enthusiastic Audience in Finance
Despite the increasing number of successful chatbot deployments, bots still carry a somewhat negative connotation in many industries.
Finance and banking are not among these.
A 2018 report found that 43% of digital banking customers actually prefer to solve issues using chatbots or live chat – and that was before the runaway success stories of Bank of America’s Erica, or Capital One’s Eno. Banking chatbots have wide appeal, from millennials as well as seniors.
Many other industries are riding the chatbot wave – it’s estimated that by 2020 85% of consumer interactions will be handled without a human agent. Chatbots can save up to 30% on customer support costs, and customers increasingly expect businesses to be “open” 24/7 – one of the most compelling benefits of bots. Finance was initially slow to hop in the bandwagon (as late as 2017, 67% of finance firms reporting that chatbots weren’t even on their radar), but the tide has turned.
As bots multiply in finance, banking execs want to know whether intelligent chatbots are the future of financial customer care – or another digital trend. Although it’s still very early days, the answer seems to hinge on the question of how bots will scale. How will they grow to navigate more complex requests? How much will they be able to take on without handing off to a human? In the past, with chatbots designed around rigid decision trees, bot limitations seemed pretty steep. Customers may ask the same question a hundred different ways, and there was simply no decision tree that could handle the complexities of human language in the same way a live rep can.
Image via Chatbots Life
With the smash success of banking bots like Erica (which has already handled 50 million+ interactions with an 82-83% customer satisfaction rating), the viability of chatbots as a CX disruptor has been proven, turning the question of “if” into a question of “how”. New technology like AI-driven NLS opens up a whole world of new possibilities here.
AI-driven NLS is a powerful tool for processing unstructured data like natural language. Imagine a chatbot that can understand “Reset my password” and “locked out of my account” immediately as account access issues – without ever coming near a decision tree. In finance, even more complex customer interactions like service charge disputes or fraud identification may be undertaken without escalating immediately to a rep. Customers expect 24/7 access, they expect correct answers, and quick resolution to their problems. With the right technology backing them, chatbots are perfectly poised to take this on.
For more information on how to implement next-generation chatbots through AI-driven NLS, you can check out our Roadmap for Digitalized Customer Service. Or, you can explore an in-depth case study of Coseer’s NLS at work at a technology company. Coseer saved the company tens of millions by increasing help ticket deflection from 11% to 64%; or $400 per ticket.