OBSERVATIONS FROM THE FINTECH SNARK TANK
Among my 5 Predictions For Banking And Fintech In 2023, I wrote:
“2023 will be the ‘year of the chatbot’ in banking. After years of hearing pundits and futurists tell them how disruptive AI is going to be in banking, 2023 will finally be the year that bank executives do something about it.”
It wasn’t a well-received prediction, but I’m sticking to my guns (not literally, of course).
Three important reasons why 2023 will be the year of the chatbot—or conversational AI, more broadly—include: 1) The need to improve digital service; 2) The need to improve the employee experience; and 3) Banks will experiment with ChatBPT.
Digital Service Needs (Evolved) Chatbots
I know what you’re thinking: “Dude, what are you smoking? Haven’t you used chatbots? The experience is awful!”
Consumer research refutes that view. According to a study by Cornerstone Advisors, consumers’ ratings of their mobile banking experience are higher for banks with a digital assistant than for those without one.
But not all “chatbots” are created equally.
Chatbots are “evolving” to become intelligent digital assistants (IDAs).
Although the terms chatbots and IDAs are often used interchangeably, according to a report from Cornerstone Advisors titled The Chatbot Journey: Making Intelligent Digital Assistants Integral Members of the Team, there are differences:
“Chatbots are typically rule-based systems which can perform routine tasks with general FAQs. IDAs are fully equipped with natural language understanding which aids in understanding and retaining context for polished conversations while carrying out a variety of tasks to fulfill a user’s requirements.”
Intelligent digital assistants provide superior service by:
- Being conversational. Basic (i.e., un-evolved) chatbots pull from a limited library of scripts and FAQs. This approach delivers only a simple path to a predetermined response. IDAs, in contrast, are pre-trained with knowledge from customers’ financial history and behavior patterns, which gives them a more comprehensive conversational foundation of experiences, languages, and terms to draw upon to address specific customer needs.
- Advising versus resolving. The primary role of banking chatbots is to swiftly resolve consumers’ basic, transactional questions, or move them on to human intervention. This limitation often leads to incomplete problem resolution and a high customer abandonment rate. IDAs, on the other hand, act as knowledgeable bankers who can walk alongside a customer and recommend the most informed nextstep in their specific financial journeys.
Chatbots fill support gaps without much capacity to retain and grow relationships. Using conversational skills, a deep data library, and AI-driven analysis of usage patterns, IDAs understand what customers are asking and can direct them to what they want while encouraging them to explore other engagement options.
Conversational AI Improves The Employee Experience
It’s true that many consumers (today) will resist using chatbots, preferring to deal with another human. But have you been in a bank branch or called a bank’s contact center recently?
Eight in ten banks are struggling to recruit new staff members, according to Cornerstone Advisors. When those banks do find someone to come onboard, getting them up to speed on products and processes takes a long time.
The new reality: Chatbots are for employees—and are the new employees.
Employees often turn to other employees for help in figuring out how to respond to customer requests, but what do they do when their colleagues don’t have the answers?
Banks are increasingly deploying conversational AI technology to support employees directly—in effect, making a chatbot a “member of the team.”
Making a chatbot or intelligent digital assistant a member of the team is akin to bringing a new human employee onto the team. If you were hiring someone (a real person) into your organization—what would you do to ensure that person succeeded?
You would create an onboarding plan, assign that person to report to one of your best managers, and create a professional development plan with a multi-year timeframe to identify the types of roles and positions you’d want that person to fill on his or her way to the management level.
It’s no different for a chatbot on its journey to becoming an intelligent digital assistant.
Banks Will Experiment With ChatGPT
Bank and credit union CEOs who aren’t instructing their CIOs and CTOs to report back to the executive team with ideas for how to ChatGPT are derelict in their duties.
The recently announced conversational AI tool from OpenAI is great at composing poems in the style of Post Malone, but there are more mundane uses for the tool in banking. In a recent LinkedIn post, Chris Nichols, Director of Capital Markets at SouthState Bank, identified 15 use cases ChatGPT in banking. My favorites included:
- Create code. ChatGPT can analyze all open-source code and synthesize code libraries to help create code capsules. Programmers at SouthState have asked ChatGPT to: 1 Write python code to create a graph of the current month’s spending; 2) Write C+ code that will match an email address to the one on file; and 3) Write Java code to create a poll for the bank’s website.
- Product design. Nichols points out that one of ChatGPT’s abilities is to take on a specific customer persona, e.g., a doctor, retiree, CEO, or engineer. ChatGPT can tell a bank: 1) How to pitch treasury management services to a Controller at a municipality, and 2) How a lawyer would like to be notified that the bank has placed a hold on their checking account.
- Legal contracts. Chat GPT may not be ready to write and analyze legal contracts, Nichols says “it is almost there,” and says his bank is using the tool to “insert missing clauses about the return of information, venue, non-auto renewal, regulatory requests, and other items in draft contracts” saving the legal team significant time.
Conversational AI is a Foundational Technology in Banking
Conversational AI, has become a competitive necessity—i.e., a foundational technology—not just to provide customer and employee support but because of the need to gather data.
Attempts to codify and store “data” collected through human interactions—and even from clickstream data—is incomplete, generally inaccessible to other applications that could benefit from the data, and hard to analyze.
Data gleaned from chatbot interactions can overcome these shortcomings. Financial institutions need to make digital assistants part of their data management strategies—not just their sales and service strategies.
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