Thanks to ChatGPT, 2023 is the year of the chatbot in banking



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 listening to pundits and futurists tell you how disruptive AI is going to be in banking, 2023 will finally be the year bank executives do something about it.”

It wasn’t a welcome prediction, but I stand by it (not literally, of course).

Three big 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 horrible!”

Consumer research refutes that opinion. According to a study by Cornerstone Advisors, consumer ratings of their mobile banking experience are higher for banks with a digital assistant than for those without.

But not all chatbots are created equal.

Chatbots are “evolving” to become Intelligent Digital Assistants (IDAs).

Although the terms chatbots and IDA are often used interchangeably, according to a Cornerstone Advisors report titled The Chatbot Journey: Making Intelligent Digital Assistants Integral Team MembersThere are differences:

“Chatbots are usually rule-based systems that can perform routine tasks with general FAQs. IDAs are fully equipped with natural language understanding that helps understand and retain context for polished conversations while performing a variety of tasks to meet user requirements.”

Intelligent digital assistants provide superior service by:

  • Be conversational. Basic (meaning non-evolved) chatbots are drawn from a limited library of scripts and FAQs. This approach offers only a simple path to a default answer. IDAs, by contrast, are pre-trained with knowledge of clients’ financial histories and behavior patterns, giving them a more complete conversational base of experiences, languages, and terms to draw on to address the specific needs of clients. the clients.
  • Advise versus solve. The main function of banking chatbots is to quickly solve basic transactional questions from consumers, or pass them on to human intervention. This limitation often leads to incomplete problem resolution and a high customer churn rate. IDAs, on the other hand, act like expert bankers who can walk alongside a client and recommend the most informed next step in their specific financial journeys.

Chatbots fill support gaps without much ability to retain and grow relationships. Using conversational skills, a deep data library, and AI-powered usage pattern analysis, 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 is true that many consumers (today) will resist using chatbots and will prefer to deal with another human being. But have you been to a bank branch or called a bank contact center recently?

Eight out of 10 banks are having difficulty hiring new employees, according to Cornerstone Advisors. When those banks find someone to come on board, getting them up to speed on products and processes takes a long time.

The new reality: chatbots are for employees, and they are new employees.

Employees often turn to other employees for help 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 turning a chatbot into a “team member.”

Making a chatbot or intelligent digital assistant a team member is similar to bringing a new human employee onto the team. If you were hiring someone (a real person) in your organization, what would you do to make sure that person was successful?

You would create an onboarding plan, assign that person to report to one of your top managers, and create a career development plan with a multi-year time frame to identify the types of roles and positions you would like that person to fill at your company. 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 don’t instruct their CIOs and CTOs to report back to the executive team with ideas on how to use ChatGPT are leaving their roles.

OpenAI’s recently announced conversational AI tool is great for composing Post Malone-style poems, 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 for ChatGPT in banking. My favorites included:

  • Create code. ChatGPT can analyze all open source code and synthesize code libraries to help create code capsules. SouthState programmers have asked ChatGPT to: 1 Write python code to create a graph of the current month’s expenses; 2) Write C+ code that will match an email address to the registered one; and 3) Write Java code to create a survey for the bank’s website.
  • Product design. Nichols notes that one of ChatGPT’s capabilities is to take on the persona of a specific customer, such as a doctor, retiree, CEO or engineer. ChatGPT can tell a bank: 1) how to present treasury management services to a controller in a municipality, and 2) how you would like a lawyer to be notified that the bank has placed a hold on your checking account.
  • Legal contracts. Chat GPT may not be ready to write and analyze legal contracts, Nichols says it’s “almost ready” and says his bank is using the tool to “insert missing clauses about return of information, place, non-automatic renewal, requests regulations and other items in draft contracts” saving the legal team significant time.

Conversational AI is a fundamental technology in banking

Conversational AI has become a competitive necessity, that is, a fundamental technology, not only for supporting customers and employees, but also for the need to collect data.

Attempts to encode and store “data” collected through human interactions, and even clickstream data, are incomplete, generally inaccessible to other applications that could benefit from the data, and difficult to analyze.

Data obtained from chatbot interactions can overcome these shortcomings. Financial institutions need to make digital assistants a part of their data management strategies, not just their sales and service strategies.



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