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Cashflow Analytics

Price on what they actually owe
Uncover the positions nobody declared — matching every advance coming in to the payments going out, surfacing unnamed funders, and showing holdback by funder and in total against your revenue definition.

Declared positions

1

Positions found

3

Same merchant

Three new pulls

Trusted by 200+ funders, brokers, and fintechs.
Powering underwriting for MCA, revenue-based financing, term loans, lines of credit, and SBA deals.
Every number you price on
Revenue, positions, holdback, risk flags, and how good the data behind them is. No bureau, no consortium, just the statements you already collect. Windowed so you see direction and not just level, and synced into your systems on your rules.

True revenue

Inflows minus internal transfers, refunds, chargebacks, NSF returns and financing. A transaction counts as revenue unless it can be proven otherwise, and you decide the edge cases like Zelle and Venmo.
Card settlement
+12,400
revenue
Internal transfer
+8,000
excluded
Customer deposit
+3,250
revenue
Chargeback
−900
excluded
Funding inflow
+75,000
excluded
Customer deposit
+5,100
revenue

Positions

Each funding inflow tied to the repayment going out by pattern, not by matching a name against a list. Mark one Heron missed and the correction holds on every future submission.
3
SCRUB FOUND
7
ACTUALLY THERE

Holdback

The share of true revenue already committed to repayments, per funder and in total. Expected and actual sit side by side, and the gap between them is its own signal.
Expected
62%
Actual
44%

Risk flags

NSF days, negative balance days, direct debit reversals and debt collection. Missed payments caught the day they happen, plus repayment modifications and stop payments.
NSF days
4
review
Negative balance days
7
review
Direct debit reversals
2
flag
Debt collection
none
clear
Missed payments
1
flag
Stop payments
none
clear

Data quality

How fresh the data is, how complete, how confident, and what share of the money moves to an account you have not seen.
Data coverage
94%
Data freshness
2 days
Confidence
high
Unconnected accounts
12%
“
We’re more consolidated and organised in the underwriting space now. And candidly —
 we’re funding more deals
.”

Doug Christison

Chief Executive Officer, Olympus Lending

A deal priced on debt you never saw
Holdback computed on a position set with holes in it is not a number. It is a guess with a decimal point.
WHO CARRIES IT
A shopkeeper working behind the counter of a small store

Your underwriter

They priced on three positions. There were seven. The file read clean because the other four never carried a funder name.

The merchant

They took a position they could not carry on top of four they were already paying. The default was arithmetic.

The broker

They sent it in good faith and watched it go bad. The next one goes to a funder whose read they trust more.

You, the funder

The loss lands on your book, and the post-mortem finds the debt was sitting in the statements the whole time.
The file only tells you half of it
Bank statements and an application tell you what moved through the account and what somebody typed. They don't tell you whether the business is real, who stands behind it, or what else is happening around it. Heron runs both halves from the same intake, before anyone opens the file.
What the file shows you

Application fields

State, time in business, revenue and entity type, lifted off the form itself.

Bank statements

True revenue, existing positions, NSFs and balances, read line by line.

What it does not

SOS filings

Whether the entity is real, active, and registered where it claims to be.

TIN / EIN validation

The tax ID and the legal name belong to the same business.

Court records

Judgments, liens and open cases against the owner and the business.

DataMerch

What other funders already found out about this merchant the hard way.

Web presence

Evidence the business exists somewhere other than its own paperwork.

Industry

The NAICS code taken off the transactions rather than off what was typed.

Nobody sees more of small business finance

Purpose-built AI and trained LLMs, tuned on the submissions that come through Heron every day, built for the people who have to make a real call on a real business.

60K

submissions analyzed daily

15%

of US businesses seeking capital

3x

more volume per underwriter

03 · The Results
Send better offers. Fund more deals. Spend less time.
Heron mark rendered as a field of coloured blocks

25-person data entry team → zero

“Two weeks after turning on Heron, our 25-person offshore data entry team went to zero. We moved 7 of our 10 in-house folks into underwriting roles.”

Brian Mullins

Chief Technology Officer, Vox Funding

98% faster deal flow

3–4x more volume per underwriter

Deals are won and lost in minutes.
Don’t waste another one.