The past year has felt like an avalanche of “AI.” It’s in pitch decks, product names, and even job titles. If you run a Merchant Cash Advance, your inbox probably looks the same: “proprietary models”, “GenAI”, “80% faster”, “95% straight-through.”
Here’s the uncomfortable truth: much of it won’t translate to your P&L. A lot of hype creates a lot of demos, but not necessarily fewer minutes in the queue, faster approvals, or steadier repayments.
This post is a practical guide for MCA leaders who want results without the hype. W
👉 This post is Part 1 of a two-part series. In this piece, we’ll help you separate testable value from vendor theatrics, map where AI actually helps, and walk you through a 30-day pilot plan to prove lift before you commit. In Part 2, we’ll cover how to translate AI pilots into measurable results.
Start With the Only Question That Matters
In MCA lending, every flashy AI demo or vendor pitch really distills down to one test:
“Which dashboard metric improves, by how much, and by when?”
That’s the anchor you should use in every conversation. If the response is vague, chances are the value is too. At the end of the day, lenders don’t need “AI magic”, they need outcomes.
For lenders, the four metrics that separate decoration from leverage are straightforward.
- Speed: Does it shave minutes off intake and credit decisioning for specific file types (e.g., clean renewals, top broker lanes)?
- Accuracy: Do false positive/negative decisions fall, and do early repayment signals (first-pay misses, 30-day ACH returns) hold steady or improve?
- Fraud defense: Can it catch fraud flags earlier (tampered statements, templated docs, suspicious sources) and show why?
- Cost-to-serve: Can analysts clear more files with fewer loops, reworks, and handoffs?
If a solution doesn’t move at least one of these, it’s decoration.
A Five-Minute Screen to Cut Through the Noise
Not every “AI tool” actually moves your funding metrics. Before you block off hours for vendor demos, it’s worth doing a quick sniff test.
- “Test me,” not “trust me.”
It’s easy for vendors to walk in with glossy decks claiming 95% accuracy. But numbers without proof don’t fund deals. A serious AI partner will say: “Give me last quarter’s applications. I’ll process them and show you how my system would have flagged risky applicants, caught missed approvals, or sped up clean files.”
Without testing on your own messy reality—duplicate PDFs, blurred IDs, incomplete bank statements—it’s just smoke.
- Beware one-size-fits-all.
Banks have compliance-heavy cycles. Credit unions run on member-based trust. MCAs live in the fast lane: brokers calling every hour, merchants expecting decisions in hours, not weeks. A tool built for banks (week-long SLAs, high-priced integrations) will choke in the MCA environment.
For example, an MCA workflow often tolerates higher risk in exchange for faster funding, but expects pinpoint fraud detection at the document level. A one-size-fits-all solution that doesn’t flex around your exception posture or broker funnel dynamics means you’re forcing your process around the tech, not the other way around.
- Test, then test again.
Reliable AI adoption is not a leap of faith, it’s staged. Phase one is the back-run, replaying the AI on historical data to benchmark accuracy. Phase two, shadow mode, let the AI sit quietly in the corner while your analysts run live traffic, and measure how often it agrees or disagrees. Phase three, limited automation, greenlight only the “slam dunk” cases, while nudging exceptions to humans. This crawl-walk-run model ensures you don’t wake up to production disasters.
- Spot rules engines dressed as AI.
Not every “AI” is actually AI. Checklist engines can perform decently when docs are clean, but they break immediately on non-standard formats like regional banks exporting various statement templates or scanned images at odd angles.
If the tool can’t explain why it flagged a document as fraudulent, can’t output confidence scores, or keeps failing on the first off-format file, you’re looking at a rules script with lipstick, not intelligence.
For MCA lenders who regularly receive unconventional docs, this difference is survival, not semantics.
- Fits your stack with minimal IT lift.
Many MCA lenders run lean teams with minimal IT bandwidth. The last thing you want is a six-month integration slog for a tool that only shaves minutes off underwriting. A lender-friendly AI should embed directly into your LOS or CRM, play nicely with role-based access/SSO, and keep your customer data within your security perimeter. For example, integrating an ‘email to CRM’ automation into your Salesforce with an API call is a 2-week project, not a 2-quarter one.
If a vendor says, “You’ll need custom adapters, database changes, and nightly batch jobs,” move on. The opportunity cost of lost deals during integration is higher than the tool’s promised ROI.
If a vendor can’t clear this five-minute screen, you just saved yourself a lot of work.
Where AI Helps First (Keep It Boring, Specific, Measurable)
When lenders hear “AI,” the instinct is often to think about futuristic prediction models or black-box credit scoring. In reality, the fastest wins usually show up in places that are, frankly, boring.
Start from problems, not vendor menus.
Talking to many vendors can yield 100 ideas and zero priorities. Begin with your bottlenecks.
Shadow two underwriters for a day. Pull 60-90 days of LOS timestamps. Find the one repeatable time-sink that hurts the most. Fix that first.
State the problem like an operator.
“Cut underwriting review to <5 minutes for renewals from top brokers” beats “Introduce AI efficiency in underwriting”
High-leverage contenders:
- Bank-statement intake
AI can parse transactions—even messy scans—and reconcile balances in seconds. It can calculate the number of negative balance days, flag anomalies in deposit rhythms, and standardize data across multiple banks. What used to take an underwriter 15–20 minutes now drops to under 2. That’s quantifiable time savings, plus fewer human errors sneaking through. - Fraud sanity checks
Fraud rarely looks like a Hollywood hack. More often, it’s a templated PDF or a mismatched period balance that no one notices until deeper in the process. AI can automatically check file metadata, highlight suspicious sources, or surface risky IP patterns, cutting down wasted analyst hours on files that should never have made it past intake. - Stacking visibility
Identifying stacking early is critical for avoiding mis-sized offers, yet it’s painful to detect manually. AI can surface recurring ACHs to other funders and spot inter-bank flows to flag fund overlap, giving you stacking visibility before you extend a term sheet. - Risk-report digestion
Underwriters don’t need 40 pages of raw UCCs or lien data. They need a digestible summary with explanations and citations. AI can condense the noise into lender-friendly briefs, complete with source trails, so your team can focus on decision-making instead of hunting. - Industry classification
Misclassified industries lead to mispriced risk. AI can map businesses to correct SIC/NAICS codes by pulling multi-signal clues (DBA names, stated legal entities, website content, even transaction types). That ensures your pricing and policies stay consistent. - Email to CRM automation
Merchant docs shouldn’t live in inbox purgatory. Instead of downloading, reformatting, and re-uploading attachments, AI can auto-sort incoming files into the right document categories, extract the critical fields, and feed them directly into your CRM. That means underwriters see clean, structured data the moment it arrives, no manual effort required.
Pick 1-3, then prioritize by:
- Impact (minutes or mistakes removed),
- Ease (data access and integration),
- Time-to-value (weeks, not quarters).
Launch one as a pilot. Line up the others only after the first shows value.
How to Choose What to Pilot
Not every AI project deserves your resources. The simplest framework to prioritize is:
- Impact → How many minutes or mistakes will this remove per file?
- Ease → Do you already have the data in structured formats, or will this require a nightmare of integrations?
- Time-to-Value → Can you launch in weeks, not quarters?
If you’re seeing that a “bank statement intake” use case saves 12 minutes per file across thousands of deals, it’s high impact. If it only requires your existing PDF flows and limited IT lift, that checks ease. If it can go live in weeks via an API pilot, congratulations, you’ve just found your project to test.
Launch one as a pilot. Line up the others only after the first shows value.
From Problem to Pilot: A 30-Day Plan That De-Risks Adoption
Now that you have a use case you want to test the AI on, the next step is to create a pilot plan to see how it performs and how it fits in your current ecosystem.
Here’s a simple, 30-day plan to prove value:
Week 1: Back-run Your Old Files
Pull a random, fair slice of 3-6 months of applications (renewals vs. new, broker tiers, ticket sizes). Make sure you lock targets before any results:
- Minutes saved on the selected use case
- Wrong approvals (false positives) do not rise,
- Missed good deals (false negatives) do not rise
- Month-one signals (first-pay misses, 30-day ACH returns) stay flat or better
Find the misses that don’t move your selected objectives and review them with your credit team. This will help you decide fixes that will optimise the whole process.
Weeks 2-3: Shadow Mode on Live Traffic
Run the model on 10-20% of your live traffic to gauge its performance. Let the model suggest; your underwriters still have the final say. Track two simple numbers:
- Approval precision: Of all the applications AI suggested to approve, what % did underwriters agree with?
- Capture rate: For all the applications your underwriters approved, how many did AI mark as “approve” as well?
Ensure every decision override is captured with a reason. Tune thresholds until it feels like a co-pilot, not a back-seat driver.
Week 4: Limited Automation
Turn on auto-approve only for high-confidence lanes (e.g., clean renewals from trusted brokers). Everything else depends on your underwriters’ decisions.
- Keep the Approve / Review / Decline routing
- Set a kill switch if performance dips
- Add simple drift alerts (e.g., new templates, unusual error spikes, feature shifts).
By Day 30, you should be staring at a before/after chart your team believes in.
Conclusion
The AI landscape is crowded, but MCA lenders don’t need more noise—they need provable lift. The 30‑day pilot plan we’ve outlined gives you a clear, low-risk way to test any vendor against your daily bottlenecks. Start with one high-impact use case, measure results on your own data, and prove the ROI before you commit.If you’re evaluating options now, steal this pilot template. Copy the “Week 1–4” plan, plug in your own metrics, and run it with any vendor, including one you already might have. Your team will thank you for cutting through the noise. And if you want a set of helping hands, team HyperVerge is just a call away.
