Using AI To Help Businesses Borrow Money
A fintech founder reframes lending as a workflow problem and raises a bigger question for builders. Daniel & Sharmeen Aqeel chat about trust, human-centered design, and how AI changes accessibility.
đ Hey friends, Arianne here, editor and producer of Artificial Insights. Welcome! I hope youâre keeping warm.
This is TL;DL where I write about what stood out to me in each episode, share some food for thought, and share whatâs next⊠for those of us who prefer to read.
đ€ Letâs talk AI and money.
Thereâs something oddly reassuring about hearing a fintech founder say that lenders actually do want to lend money.
Most of the time, the process feels overwhelming and stressful. Sometimes, it feels a little unfairâlike the system is built with overly restrictive rules and onerous requirements. But, what if all that is just because the information borrowers need is scattered and disorganized?
This episode with Sharmeen Aqeel sat at the intersection of two things I particularly care about: trust and design. And how the two are very intertwined. As a designer, I often think about (and evaluate) how many systems feel outright hostile when, really, theyâre just poorly designed.
Yes, Iâm a great person to have coffee with. â
đïž Just Interviewed: Sharmeen Aqeel on Using AI To Help Businesses Borrow Money
Sharmeen is the founder and CEO of Lyyvora, a Lending-as-a-Service platform helping healthcare and medical aesthetics clinics access capital more clearly and efficiently.
Her background is in product design, shaped in France and Canada. And she speaks like a designer, too. You can hear it in the way she talks about the lending gap in a user experienced-centered way: lenders are there, the information is there, but borrowers feel confused and stop trying after one rejectionâeven if, maybe, all they needed was a different document.
Her UX-focus allowed her to see lending as a workflow problem. Rather than think of it like a huge, insurmountable finance problem (which would have been my first instinct), she figured making the user experience easier would solve the problem she saw.
After all, lenders want qualified borrowers. So, whatâs actually broken?
đĄ One Core Insight: Accessibility, Not Obstruction
What Sharmeen describes isnât a system built to exclude borrowers, per se, but a system thatâs just become difficult to navigate for the people who most need to use it. The criteria for lending are generally well defined. Lenders care about revenue levels, time in business, documentation, and other indicators that help them understand risk. None of that is unusual or surprising within financial services.
Where the difficulty arises is in how those requirements are communicated and experienced. Information about lending criteria is often scattered across websites, buried inside lengthy explanations, or delivered through formats that require time and interpretation to understand. For someone running a healthcare clinic, that level of research quickly becomes untenable. Theyâre managing staff, patients, and operationsâspending hours comparing lenders and trying to decode eligibility requirements is a difficult thing to find time for.
The result is a pattern Sharmeen encountered repeatedly while chatting with people in the space. A clinic receives a rejection from its primary bank, assumes that decision reflects the entire lending market, and stops applying. In reality, other lenders with slightly different risk models mightâve been willing to consider the same borrower.
Lyyvora doesnât try to rewrite the financial rules that lenders use to evaluate borrowers. Instead, it tries to improve the experience of borrowing through human-centered design. The platform consolidates the application process into a single intake flow, then uses AI to assess borrower readiness and compare that profile against the criteria of multiple lenders.
From the borrowerâs perspective, this changes the experience from a series of opaque applications into something closer to guided navigation. Instead of guessing which lenders might say yes, they receive a clearer picture of where they stand and which options are realistically available.
Danielâs conversation with Sharmeen highlighted how AI fits into this structure. The technology doesnât replace lender judgment or remove risk evaluation. But, because it helps interpret criteria at scale and presents the results in a form that borrowers can actually use, it opens the doors for borrowers who previously wouldnât have knocked.
For founders building in regulated or high-stakes industries, this is a useful reminder that many problems labeled as âdifficultâ are actually problems of accessibility. The underlying system may already work reasonably well. The missing piece is often the design layer that translates complexity into something understandable.
What AI makes possible is a different interface to that complexity.
Instead of asking borrowers to climb the entire mountain of information themselves, systems can now interpret those rules and present them as smaller, usable decisions along the way. What documents are missing? Which lenders might realistically consider this application? What step should come next?
When that translation layer works well, the experience changes dramatically. Borrowers no longer need insider knowledge of the financial system to navigate it (what small business owner signs up for that?)âjust the ability to respond to the next clear step.
đ One Key Clip: What If ChatGPT Makes You Obsolete?
âOne thing that I donât see AI doing is the network that Iâm creating.â
In the bonus clip, Daniel asks a forward-looking question. What happens if matching borrowers to lenders becomes trivial? What if, in five years, a single GPT can gather criteria and return offers instantly?
Sharmeen didnât flinch. Yes, the core workflow may get easier. And yes, ChatGPT may one day start doing what Lyyvora does out of the box. But, the network sheâs building right now? That will still matter.
Sheâs cultivating relationships with lenders, building a close community of borrowers, staying hands-on in early deals to protect credibilityâŠ
AI may automate the intake, but it canât automate trust.
If youâre thinking about defensibility in an AI-heavy world, this short clip is precise and honest. Give it a listen.
đ„Ą Food For Thought: What Becomes Your Moat?
Consider this:
If AI makes the core work easier, what becomes the moat that sets you apart?
Itâs easy to think about this question in the context of companiesâif the technical capability that once defined your product suddenly becomes easier to build, easier to automate, or even embedded inside general AI tools, then the source of your defensibility has to shift somewhere else.
Maybe that âsomewhere elseâ is distribution, or brand, or even a network of relationships that took years to build. Or, maybe, itâs the trust people place in the humans behind the system.
But, the same question applies to individuals, too.
Many of us built careers around skills that required time, training, and specialized knowledge. If AI begins to reduce the effort required to perform those tasks, the question naturally becomes: what makes someone stand out when many people can now perform similar tasks?
Is it experience interpreting messy situations? The relationships you have built? Your ability to see patterns and ask better questions? The way people trust you to guide decisions when the stakes are high?
I donât think this question has a single answer, but I do think itâs becoming one of the most important questions for anyone in the workforce right nowâbusiness owner or not.
I would genuinely love to hear how you are thinking about this. What becomes the moat when the work itself becomes easier?
Share your thoughts in the comments or reply and tell us how you are approaching this in your own work!
đ„ Up Next: Patrick Belliveau Returns
âWhat happens when an AI prototype works once, and then has to work every day on a million real inputs?â
Patrick Belliveau is coming back to the show just over a year after his first appearance, and the evolution is real.
When Daniel first spoke with him, Gambit was building chat-based AI personalities. Now the focus is agentic workflows with audit trails, fallbacks, and human-in-the-loop checks.
He also shares concrete patterns they use to keep systems honest, including multi-model validation when extracting from documents. And, youâll hear about âBoganâ, an AI personality paired with a research layer that wrote a cold email and got a reply in 35 minutes during a client retreat.
If youâre curious about what it looks like when an AI company moves from prototype to operational discipline, youâll want to hear this one. The episode drops tomorrow!
đ The Part AI Still Canât Automate
AI can reduce the cost of analysis, accelerate communication, and help systems surface patterns more quickly than a human team could on its own.
But, at the same time, relationships, credibility, and community still develop through slower and more human channels.
Listening to Sharmeen describe the network she is building around Lyyvora made me think about how many successful systems ultimately rely on both at once: automation where speed helps and human presence where trust must accumulate over time.
At least for now, it seems trust isnât something that can be automated.
As always, thanks for listening. đ
P.S. Artificial Insights is a podcast on how AI is changing work, lifeâand us. Every other Friday, Daniel Manary sits down with leaders, thinkers, and builders in AI to have candid conversations on what theyâre doing right now and how they think the world will change. If youâre a podcast listener, weâd love for you to check us out!
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