LENDER.NEWS

AI, blockchain and community powered digital loan visibility and marketing.

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Inspiration

I have had the opportunity to work with thousands of entrepreneurs, hundreds of investors, and dozens of funds over the years. One of the biggest challenges for each of these groups is finding talent that complements a founder or CEO’s strengths. This is particularly true in the areas of marketing, operations, and finance.

But even the strongest teams can fail if they’re flying on bad instruments.

Like a flight crew navigating through clouds at 38,000 feet and cruising at over 500 miles per hour, a capable team moving fast to meet customer needs depends entirely on the accuracy of the signals it receives. When those signals about cost, cash flow, and consumption are wrong, experience and talent alone cannot prevent failure.

The largest source of erroneous capital signals I have seen in small businesses is merchant cash advances.

MCAs routinely tell the people running these companies that they have three to five times more fuel than they actually do. They often suggest that per mile cost and consumption are a fraction of reality. Decisions get made, commitments get locked in, and growth plans accelerate based on information that simply is not true.

Improving the reliability of these signals can literally save companies. When business owners and lenders can clearly see the true cost of capital and its impact on cash flow, they regain the ability to allocate resources rationally. They can grow real assets instead of compounding hidden risk.

As assets grow sustainably, the value of both debt and equity grows with them.

That is the motivation behind Lender.News and its companion MCA.NEWS. It exists to replace distorted capital signals with reliable ones so teams can move faster with confidence, allocate capital intelligently, and build businesses that compound resilience rather than risk. These improvements directly impact loan portfolio visibility, digital marketing of loans, yields and value.

What it does

Merchant cash advances operate in near total opacity. There's no audited standardized or regulatory mandated visibility into true cost to capital, cashflow drag, stacking behavior or failure dynamics. Most importantly, many MCA transactions carry high double digit and triple digit effective APRs. In practice, this means MCAs often reduce the value of traditional loans and equity within a week. By imposing a prior claim on gross cash flow, MCA providers can realize cash on cash returns in months. Traditional lenders and owners remain exposed to risk for years. What appears as liquidity is economically accelerated value extraction and destruction. Lender.News addresses this by allowing any lender, borrower, or AI agent to upload an MCA agreement manually or programmatically using one of our APIs and immediately see its cash flow and valuation impact across all stakeholders, lenders, owners, capital providers, and the firm's ability to fund growth. This makes a true cost of these instruments legible in ways even experienced CFOs fail to fully comprehend.

If Lender.News stopped there, it would already be sufficient to materially improve loan intelligence. Better loan documentation for lenders, clear decision making for borrowers and more effective marketing and servicing of digital loans using underwriting data that has historically been difficult to access. With entity-specific targeting and retargeting, lenders can keep loans on track, making small business decision makers aware of what they are sacrificing before they renew an MCA. Something many businesses repeatedly do. These enhancements create transparency across the whole portfolio, replacing paperwork with digitally native documentation systems that lenders and platforms can act on in the near real time. All of this at negligible costs. This alone can increase portfolio yields by a full percentage point, but the true power lies elsewhere. For the first time, small business owners get the ability to deliberately repurpose the cash flow they were previously forced to hand over to MCAs.

Cash extracted in a last ditch attempt to avoid collapse. Instead, that capital can be redirected towards the expertise, systems, and talent they previously couldn't afford or source, breaking the cycle that put them in that position in the first place.

How we built it

We built MCA.NEWS as a modular TypeScript system composed of:

  • A parser layer that extracts structured signals from loan documents, MCA agreements (Via MCA.NEWS) , SEC filings, Pitch Decks and exhibits
  • A valuation and decay engine that models tangible and intangible capital loss over time
  • A network topology layer that represents entities as nodes within an ICE FIELD™
  • A visualization and inspection interface for live exploration and comparison

All inputs are auditable, reproducible, and derived from mandatory disclosures — no scraped rumors, no unverifiable estimates.

Challenges we ran into

The biggest challenge was resisting the temptation to “fill in the gaps.” Private credit data is messy and incomplete by nature, but adding inferred or synthetic assumptions would undermine credibility. We chose rigor over convenience.

Another challenge was normalization: disclosures are inconsistent across issuers and years. Building a system that could generalize across hundreds of filings required careful schema design and conservative confidence scoring.

Accomplishments that we're proud of

  • Seeing the look on the face of founders and operators of companies with millions in revenue when we explained how exactly how much their MCA loans were costing them just by uploading a document to MCA.NEWS
  • Having them see a vision of how loans, equity and asset returns impact how fast they can growth and how efficiently they can serve their customers just by uploading their deck to Lender.NEWS
  • We were a little shocked that literally no CEO, founder or CFO was within one standard deviation of what their actual effective rate was on MCA loans.
  • Building a large, defensible dataset of real listing-distress events
  • Demonstrating that intangible capital decay can be observed, not guessed
  • Creating a reusable framework that bridges public market signals to private credit insight
  • Delivering a system that favors exploitability over black-box scoring

What we learned

Financial stress leaves fingerprints long before failure — but only if you know where to look. Public markets, through regulation, unintentionally provide one of the best laboratories for studying economic fragility.

We also learned that transparency is a design choice. By anchoring everything to audited disclosures, we were able to build trust into the system itself.

What's next for MCA.NEWS

Next, we plan to expand coverage, refine decay calibration, and integrate additional forced-disclosure signals. Longer term, MCA.NEWS will serve as a translation layer between opaque private credit markets and the transparency standards of public finance — helping lenders, regulators, and operators see risk earlier and act smarter.

I also built a handful of agents with wallets to spend on user targeting businesses that have had MCA loans but also have outsized growth potential. The three minute demo could not accommodate those experiments, but I learned that giving AI agents with different skills (marketing, sales, finance operations) incentive to spend on ads, data, other agents and even humans can actually lever ROI in amazing ways.

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