For Community Banks, Quality Data is Essential for Effective AI Use
The rise of artificial intelligence (AI) in the banking sector has been a game-changer, with institutions of all sizes adopting this technology for a variety of functions. However, as community banking experts have highlighted, the success and usefulness of AI are heavily reliant on the quality of data it is fed. Without accurate, clean data, AI’s potential benefits can be significantly undermined, leading to a scenario of “garbage in, garbage out.”
At a recent webinar hosted by American Banker, executives from three community banks discussed their experiences with incorporating AI into their operations. The consensus was clear: good data is crucial. “If you want AI to work at all, it’s going to rely on good, clean, solid data,” said Shawn Main, chief business architect at Vantage Bank in San Antonio, Texas.
Larger Banks Hold Advantage in Data Collection
Today, banks use generative AI for a variety of purposes, including fraud detection, customer service, and streamlining back-office tasks involving large amounts of paperwork. Fundamental to the effective execution of these functions is a rich and accurate pool of data, or a “data lake” as Main referred to it.
Large banks, with their extensive customer bases, naturally have more data at their disposal than smaller, community-based lenders. This advantage allows them to leverage AI more effectively. However, this doesn’t mean smaller banks are out of the running. As Main emphasizes, community banks need to focus on building robust data-collection skills to stay competitive.
AI’s Potential in Community Banking
According to a 2025 survey by American Banker, over half of community bankers anticipate that AI will significantly bolster their fraud detection capabilities. Additionally, 44% believed AI would be highly useful for advising customers and for regulatory compliance.
Nevertheless, the effective use of AI hinges on the quality of input data. As Aleda De Maria, chief operating officer at PeoplesBank in Holyoke, Massachusetts, pointed out, data strategy and oversight should be a key interest for all bank employees, not just those behind the scenes.
The Benefits of Data Cleansing
Jim Kisch, CEO of Passumpsic Bank in Saint Johnsbury, Vermont, highlighted the importance of “data cleansing” in leveraging AI effectively. A well-informed AI tool can automate operational tasks, freeing up employees for more specialized roles and direct community interaction. These new roles could potentially include a “fraud grief counselor” or an advisor for new businesses.
“This is all about driving more efficiency in your back office so they have more time to spend with your customers,” Main echoed.
AI is Not a One-Size-Fits-All Solution
While AI holds potential for many functions, it is not a panacea for every problem, especially when the data is of poor quality. De Maria stressed the importance of having quality data ready for AI deployment. “It doesn’t make sense to deploy it if you have garbage data,” she said.
In conclusion, for smaller banks wanting to use AI, the key lies in developing a robust data strategy and collection skills. This is a critical step in ensuring the technology’s potential benefits are fully realized, thereby maintaining their competitiveness in an increasingly AI-driven industry.
Source: American Banker
