How AI search is changing prospecting for community lenders.
By Ariel Trybuch, Co-founder, LenderSquared
Ask a commercial lender at a community bank or credit union how they find new business and you’ll hear some version of the same answer: referrals, relationships, and a spreadsheet someone built three years ago. Those relationships are the whole advantage of community lending. But building the list eats hours that should go to conversations, and it rarely tells you which of the 400 names on it to call first.
That’s the problem we set out to solve with AI Prospect Search. Here’s what AI-powered search can and can’t do for a community lending team.
The prospecting problem nobody budgets for
Most lenders already know the borrower they want. Something like:
“Owner-operated HVAC and plumbing contractors in our three counties, $2 million to $10 million in revenue, growing, with room on the balance sheet for equipment financing.”
Turning that sentence into a real list usually means stitching together a contact database, an industry report, and some guesswork. Traditional filters make you think like the database: pick a code, pick a revenue band, pick a radius, repeat. Anything nuanced, like “growing” or “underleveraged,” gets lost because it isn’t a checkbox.
The result is a list that’s too broad to act on or too narrow to be useful, with no clear place to start.
What AI search actually does
AI search flips the order. You describe the target profile in plain English, the way you’d explain it to a new hire. The system translates that description into filters across the underlying data: industry, geography, size, and financial characteristics. You see exactly which filters it applied, adjust anything that’s off, and generate the list.
Two things matter here.
Transparency
A lender should never have to trust a black box to decide who’s worth a call. Seeing the filters means you can check the logic, tighten a revenue band, or drop a county, the same way you’d mark up a colleague’s first draft.
Ranking
Instead of an alphabetical list, prospects come back stack-ranked by how closely they match the profile you described. The top 20 are this week’s calls. The rest are next quarter’s pipeline.
17M+ U.S. small and mid-sized businesses, with income statement and balance sheet depth down to the zip code.
That depth is what lets a phrase like “room for more debt” mean something concrete.
Why the credit team should care too
This is the part that tends to surprise people. Prospecting and credit usually run on different data. Business development pulls a name from one tool, credit benchmarks it against peer data from another, and the numbers don’t always agree. That gap shows up at exactly the wrong moment, right when a relationship manager is trying to move a deal forward.
When prospect search and credit benchmarking sit on the same data, business development and credit see the same numbers on the same company. The lender who flagged a contractor as a strong fit and the analyst who benchmarks that contractor against its industry peers are working from one set of figures. Fewer surprises, faster first conversations, cleaner handoffs.
What stays human
AI search doesn’t replace the relationship, and it shouldn’t try. It won’t know that the owner of the landscaping company just bought out a partner, or that the family has sponsored the local Little League for 20 years. Your lenders know that.
What it removes is the grunt work between “I know who we want” and “here’s who to call.” The judgment on whether to pursue someone, how to open the conversation, and what to offer stays with the people who know the market.
Why compliance and IT say yes
It’s all our data
Every figure comes from our own proprietary dataset, not scraped web data or a patchwork of third-party feeds. Nothing an institution enters or searches is added to that data or visible to another institution. Your compliance officer will love us.
No implementation
Nothing to install, nothing for IT to stand up before your lenders start searching. Your IT department will love us too.
Three ways to put it to work
- Start with your best relationships. Describe your five favorite commercial clients in a sentence each, then search for businesses that look like them.
- Go deep on one market. Pick a county or corridor where you want to grow and pull the top matches in two or three industries you already understand.
- Bring credit in early. Share the ranked list with your analysts before outreach, so everyone agrees on what a good fit looks like.
Let’s compare notes
We’d love to hear from lenders across the COCC community:
- How does your team build prospect lists today, and how long does it take?
- What’s one borrower profile you’d love to find more of but can’t easily search for?
- Where do business development and credit disagree most often at your institution?
