AI in Banking: The Most Valuable Use Case No One Talks About
I recently attended a round table with business owners who are implementing AI initiatives using a well-known platform. Every owner had a version of the same story:
“Our AI project is stalling. It just isn’t meeting our expectations yet.”
From that point, decision makers almost always follow one of two paths:
- They abandon the project, or
- Believing they’re being innovative, they keep pouring money into “fixing” the AI.
After several owners shared their stories, I realized they all struggled with the same underlying issue.
They misunderstand what AI is actually built to do.
And in my experience, bankers fall into this trap as much as anyone.
Traditional computing is binary. Zero or one. True or false. Computers return precise answers and consistent output. If you want a count of how many customers are flagged as attrition risks, your database or SQL query will do that perfectly. In fact, most AI use cases I hear about could be solved more simply with a data analyst who knows SQL.
LLMs don’t think in a binary way. They are built for (surprise, surprise), language. They understand patterns, meaning, nuance, tone, and sentiment. They connect ideas and reveal themes humans can also do well, but mostly don’t have time to find.
Databases answer the question: “How many?” while AI answers the question: “What does it mean?”
Leaders get frustrated when they expect AI to behave like SQL. Savvy leaders unlock ROI when they use AI to interpret unstructured language.
FIs don’t have a data problem. They have a resource problem.
Bankers are drowning in dashboards. They already know every number that can be counted.
What they don’t have are the hours or the people required to sift through thousands of open-ended comments to understand why those numbers exist.
Every month, thousands of customers leave open-ended comments. Buried inside those comments are the answers to critical questions:
- Why are customers leaving?
- What is frustrating them?
- Which employees are making a difference?
- What should we fix first?
The problem is the time it takes to read them all.
That’s where LLMs shine. They do the grunt work. They read everything, group themes, understand emotion, and surface what matters most.
Introducing Smart Summaries in Avannis
Smart Summaries surfaces the most meaningful insights so you can act faster.
Here’s how it works:
- Filter to the segment you care about (wealth clients, digital users, a branch, a product line).
- Ask a natural question like:
- What are the most frustrated customers talking about?
- Which comments are the most insightful?
- What are the most frustrated customers talking about?
Smart Summaries reads the comments, extracts themes, and returns the findings along with examples of the actual comments used to generate the conclusions and the corresponding survey IDs. If you want more context or need to follow up with the customer, the path is right there.
No scrolling.
No noise.
No guessing.
And privacy is non-negotiable.
Before any comment touches the AI model, Avannis removes names, account numbers, and personal identifiers. Avannis processes comments in a secure, private environment. Your data never trains a public model and customer data never leaves our system.
How Bank Leaders Use the Insights
- Validate priorities and investments with confidence
- Drop customer quotes into board decks without manual searching
- Recognize employees praised by customers
- Spot emerging operational and process issues before they lead to attrition
Once you stop expecting AI to act like a calculator and start using it like an analyst who never gets tired, AI finally becomes useful.