Agent IQ Insights 2026 Digital - Flipbook - Page 2
CHATBOT
INSIGHTS
G O O D TO K N OW
Machine learning:
A type of artificial
intelligence in which
algorithms “learn” patterns
gleaned from large
datasets in order to make
accurate inferences from
new data.
Natural language
processing:
Chatbots are one of the most visible applications of artificial intelligence
within the banking industry and can provide efficiencies by saving staff time
and energy. However, this technology needs to be continuously monitored and
regularly updated to ensure it delivers correct information to both employees
and customers.
A type of artificial
intelligence that uses
machine learning to enable
computers to simulate
human conversation.
Source: IBM
Chatbots present an interesting opportunity and risk to the banking industry.
KEY TAKEAWAYS
When done right, chatbots can free up valuable time and energy for bank
employees, quickly tracking down information and handling simpler customer
queries. But there is also the potential to create an experience where a customer
• Chatbots rely on machine learning
to find responses to questions and
use natural language processing to
simulate human conversations.
• A bank may use chatbots either
externally to respond to simple
customer inquiries or internally to
find information for employees.
• However, a chatbot is only as good
as the data it’s fed. A chatbot that
provides a customer with incorrect
or outdated information may open
a bank up to legal and regulatory
risks. Therefore, a chatbot needs
to be continuously monitored and
regularly updated.
• When evaluating vendors, bankers
need to understand the frequency of
updates a provider will perform, as
well as the speed with which it can
resolve errors.
must type “human being” over and over into a pop-up window to reach an actual
person for help. Certainly, that’s a scenario any banker wants to avoid.
A chatbot is a type of technology enabled by artificial intelligence (AI). Chatbots
use machine learning to quickly retrieve data and natural language processing
to simulate simple conversations. Companies have been experimenting with this
technology for decades but chatbots have become more sophisticated, with more
firms adopting them, over the last few years with advancements in generative AI.
In the banking industry, chatbots are commonly associated with providing answers to simple customer questions, like queries about routing numbers. Brendan
Marston, chief operations officer at Bank First Corp., in Manitowoc, Wisconsin,
compares customer-facing chatbots to ordering a pizza: Some customers will
want to call in their order, but others will prefer to order it digitally.
“We need to meet customers where they are. A lot of customers don’t want to
make a phone call or walk into a branch,” says Marston. “It’s never about reducing headcount. It’s about scalability and bandwidth and customer experience.”
But there are opportunities to use the technology beyond answering simple
questions. Banks can also deploy chatbots internally to assist employees with
quickly finding information about company processes or provide deeper analysis. For example, a bank with a larger strategic goal of maintaining core deposits
might use an internal chatbot to identify all customers with a certificate of
deposit expiring in the next 60 days. It could then use a customer-facing chatbot
to prompt those customers to explore options for rolling over those funds when
they log into their bank accounts online.
“And that is the real challenge: Figuring out how chatbots and their AI con-
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