AI is reshaping MCA underwriting by doing the bank statement work in seconds: it extracts deposits, flags NSFs and negative days, calculates revenue and daily balance, and scores the file against lender guidelines before a person opens it. For a broker, a pre-qualification that took hours now takes about a minute, and files reach lenders that can fund them.
A good AI underwriting engine reads every transaction line, not just the summary page, and rebuilds the numbers an underwriter would calculate by hand. That is what makes the verdict trustworthy.
The output is the same worksheet a senior underwriter would produce, delivered before the broker finishes reading the application.
It moves the broker from data entry to decision making. The hours that used to go into spreadsheets go into talking to merchants and lenders instead, and the files that reach lenders are cleaner, so they get answered faster.
| Step | Manual underwriting | AI underwriting |
|---|---|---|
| Statement review | Read each PDF, key figures into a spreadsheet | Upload PDFs, figures extracted automatically |
| Time per file | One to three hours | About 60 seconds |
| Lender selection | Memory plus a guideline sheet | Rules engine scores every lender at once |
| Error risk | Missed transfers, miscounted NSFs | Consistent rules applied to every line |
| Declines | Discovered after submission | Predicted before submission |
| Volume per underwriter per day | A handful of files | Dozens of files |
Speed is only half the story. Because the analysis is finished on the first call, the broker can tell the merchant what is realistic, what lender tier they fall into and what paperwork is still missing, which is the conversation that wins the deal. Our guide to how long MCA underwriting takes shows where the remaining time goes once the statement work is automated.
General-purpose financial AI makes MCA-specific mistakes. Tools built for the MCA industry are designed around those cases. The rules that matter most are the ones a generic tool gets wrong.
The Zeneth UW Suite was built for MCA brokers with these rules in place from the start, rather than adapted from an accounting product. That is the difference between a verdict a broker can send to a lender and a summary they still have to check.
The engine holds each lender's published guidelines, such as minimum monthly revenue, maximum NSFs, maximum negative days, time in business, restricted industries, position limits and state exclusions, and scores the merchant against every one of them at once. Lenders that clear every rule are ranked by fit; lenders that fail a rule are listed with the reason.
This replaces the guideline binder and the guesswork. A broker with access to a large lender network can see immediately that a merchant with three existing positions and eight NSFs will only fit a fourth-position funder, and can price the deal honestly on the first call. It also stops the shotgun submission, where the same file goes to fifteen lenders and burns the broker's reputation with twelve of them. The MCA requirements guide lists the criteria most lenders apply.
Lenders like them. A submission that arrives with a clean revenue schedule, an NSF count, a position summary and a note on why this lender was chosen is faster for their underwriter to approve, and it signals a broker who does not waste their time. The lender still runs its own underwriting; the AI work simply makes the file easier to say yes to.
Far less than the underwriter hours it replaces. The Zeneth UW Suite charges $1 per month of bank statements analyzed, so a standard three-month file costs $3, and new accounts get five free credits to test it on real deals. Verdicts come back in about 60 seconds, and matches draw on a network of more than 100 lenders.
The cost comparison a broker should actually run is against lost deals: every file that sits in a queue overnight is a file the merchant may have signed elsewhere by morning. Speed to verdict is speed to contract.
Run your next ten real files through the software alongside your current process and compare the results line by line. Look for three things: whether the revenue figure matches yours, whether the NSF and negative-day counts match, and whether the lender matches agree with where you would have sent the deal.
Brokers who do this usually find the AI caught a transfer or a hidden position they missed. From there, the change is procedural: every new file goes through the engine before anyone opens a spreadsheet. Details on the platform are on the Zeneth UW Suite page, and the app runs at zeneth-uw.com.
Pricing is usually per file or per statement rather than a large monthly license. The Zeneth UW Suite charges $1 per month of bank statements analyzed, so a three-month file costs $3, and new accounts receive five free credits. Compare that against the underwriter hours a manual review of the same file takes.
About 60 seconds from upload to verdict for a standard file of three to six months of statements. The time goes into extracting every transaction line, rebuilding revenue, counting NSFs and negative days, detecting existing positions, and scoring the merchant against each lender's guidelines. Larger or scanned files can take a little longer.
Yes. The engine detects recurring daily and weekly funder debits, counts the positions, and estimates the remittance load on the merchant's cash flow. It then matches only lenders whose position limits allow the deal, which is how brokers avoid submitting a fourth-position file to a first-position funder and getting declined.
Yes, because per-file pricing means a small shop pays only for the files it runs, and the time saved on each file goes directly into selling. A two-person ISO gains the most from faster verdicts on the first call, where a merchant who hears a realistic answer immediately is far less likely to sign elsewhere.