Most Lenders Don’t Have a Credit Problem, Henry Abenaim of FUNDINGO

What we talked about.

In this interview, host Sunny Ray talks with Henry Abenaim of FUNDINGO about why most lenders do not have a credit problem but a process problem. Abenaim explains that lenders already know what makes a good borrower and a good risk, and the real challenge is operationalizing that judgment consistently across a team. He describes FUNDINGO as a connected loan management platform for private and specialty lenders, built natively on Salesforce, which consolidates the 20 or more systems often needed to originate, close and service a commercial loan. He traces his unconventional path from rabbinic and Judaic studies to underwriting and Salesforce administration, including a costly wire transfer mistake that sent 25,000 instead of 2,500 to a bait shop in Montana. The conversation also covers the shift from consultancy to product after working with over a hundred lenders, the tradeoffs of building without outside capital, the limits of document automation, where AI agents genuinely help versus marketing hype, and why humans remain in the loop on the decisions that matter most.

Henry Abenaim of FUNDINGO argues lenders already know whom to trust; their real problem is turning that judgment into reliable daily operations.

The questions, and the answers.

You say most lenders don't have a credit problem. What do you mean by that?

Lenders already know what a good deal, a good borrower and a good risk look like. The judgment is there. The problem is building that judgment into operations so the team makes the right decisions time and time again. The issue is not who to give credit to, but how to operationalize it day in and day out.

What is FUNDINGO and what problem are you solving?

FUNDINGO is a loan management platform for private and specialty lenders, a lending operations platform that runs the whole operation in one connected place. People usually guess two or three systems are needed to originate, close and service a commercial loan, but it takes over 20. We consolidate that into one end-to-end, integrated platform.

Tell us the story of the wire transfer that went to a bait shop in Montana.

We were running on spreadsheets, and it was easy to add an extra zero to a cell. I sent 25,000 instead of 2,500 to a fish and bait shop in Montana, and we never got the money back. I thought I'd be fired. My boss saw the broader problem, and it showed me manual work is slow, error-prone and costly.

Did you plan to build a consultancy before the product?

The plan was consultancy, not product. While building digital lending, underwriting, origination and servicing experiences for over a hundred lenders, we noticed the same problem kept coming up. We realized the market needed a fit-for-purpose tool that would be much easier to implement and deploy than building bespoke for every lender, so we pivoted to product.

Why were people wrong that the whole loan life cycle can't live natively on Salesforce?

Many people see Salesforce as just a CRM. But since it opened its API, when I started implementing it at the lending company back in 2008, it became a business platform. You can configure it without programming, it offers point-and-click configuration and multi-tenant architecture, and it suits workflow-heavy, people-heavy, data-heavy lending operations.

You built this without raising big rounds against heavily funded competitors. What does that cost you and what does it buy you?

It cost us speed. Competitors with hundreds of millions can hire faster, market more and make bigger bets, and sometimes we turned down opportunities. But it bought us discipline and focus. We built around what customers would pay for, not what looks good on a pitch deck, and we kept control of our long-term direction.

Where do you refuse to let software decide, and what can AI agents genuinely do today?

Money movement and high-stakes decisions stay accountable to people, since a wrong call can cost millions or break compliance rules. AI is powerful at analyzing larger data sets, applying credit policies and surfacing issues. I start from the job that needs doing and remove friction one piece at a time, not agents for their own sake.

Five years out, what is fully automated and what still has a human in the chair?

Repetitive work that you train a staff member to rinse and repeat on every loan will be handled by technology, and AI will take over much more of the inefficient process. But for edge cases, reading the market and judging loans, humans will have even more information to decide with. The human in the loop will still be there.