The Green Sheet Online Edition
August 25, 2025 • 25:08:02
From MCAs to AI-powered cash-flow intelligence: Alternative financing is growing up
Small and midsize businesses run on cash flow, not quarterly earnings calls. When revenue comes in waves access to fast, flexible working capital can be the difference between capturing an opportunity and missing payroll. For years, merchant cash advances (MCAs) filled that need-it-now gap by exchanging a lump sum today for a slice of future card sales tomorrow.
What’s changing the game is artificial intelligence. AI is transforming how financing is underwritten, priced and monitored, as well as how merchants themselves forecast cash flow, plan spending and grow more resilient. The result is a financing relationship that looks less like a one-off advance and more like an ongoing, data-rich partnership.
MCAs in brief
The classic MCA exchanges an upfront advance for a fixed or variable percentage of future card receipts, withdrawn automatically until the obligation is met. Because remittances scale up and down with revenue, the structure can be gentler in slow periods than a fixed amortizing loan.
Decisions are fast, and approvals hinge on business performance more than the owner’s personal credit profile, which is useful for firms that can’t clear bank loan hurdles. But costs can be high, and the factor-rate pricing used by many providers can obscure the true annualized expense. In short: quick oxygen, not always cheap oxygen.
The model’s operational backbone is payment data. Providers historically partnered with processors to see daily card settlements and sweep agreed percentages as sales occur. That deep integration with POS revenues, rather than a traditional monthly billing cycle, is what makes MCAs fast and elastic. The same tight coupling now underpins newer variants offered by embedded platforms. This processor-adjacent design is where AI can add intelligence. The rhythms of cash collections and outflows are observable in real time and ready for modeling.
AI-informed underwriting
AI does not replace the MCA concept so much as expand it. Machine-learning models can evaluate far more than trailing card sales, for example, seasonality, store-level momentum, SKU mix, invoice timing, deposit volatility, payroll cadence, marketing lift and local demand signals. Instead of a single view of sales that occurred over a specific period, risk teams get a living picture of the business’s health and near-term trajectory. In practice, that can mean smarter offer sizing, holdback percentages calibrated to actual variability and dynamic repayment plans that adjust within pre-agreed bounds.
It also means declining more applications that look fine on paper but spike certain risk indicators in pattern data. Done well, this improves access for good businesses while containing losses.
A related trend, cash-flow underwriting, is pushing beyond transaction processors to bank-account data. With borrower permission, lenders pull real-time inflows and outflows from business accounts, categorize them and evaluate repayment capacity based on actual cash dynamics rather than proxies.
Open-banking rails and account-connection tools have made this far simpler than even a few years ago. For SMBs with thin or atypical credit files, cash-flow data can be the difference between a hard no and a tailored offer.
Speed still matters. Embedded finance programs housed inside commerce platforms can pre-underwrite using platform telemetry and update risk views continually. That’s how some programs surface pre-qualified offers inside a merchant dashboard and allow the business to choose an amount, with repayments swept from a percentage of daily sales. The appeal is obvious: capital aligned to the rhythm of sales, with fewer forms and faster decisions.
#h2Beyond the advance Today’s financing platforms increasingly ship with software, not just money. The most valuable additions help owners answer three questions:
- What will my cash look like next week, next month, next quarter?
- What could break that forecast, and how would I respond?
- What investments can I responsibly make, and when?
AI-assisted forecasting models ingest accounting feeds, settlements, invoices and seasonality to produce rolling projections with confidence bands. Scenario planning moves from guesswork in a spreadsheet to a few clicks. The tools don’t replace basic financial discipline; they surface the right decisions sooner, when costs are lower and options are wider.
For underwriting teams, the benefit is two-fold: precision (more accurate estimates of loss and prepayment) and timeliness (faster detection of deteriorating conditions). Models can spot subtle shifts in deposits, ticket sizes or refund rates that often precede distress. They can also flag positive momentum such as a marketing campaign pulling through to revenue, or a new location ramping on schedule, which supports responsible upsizing of offers. For merchants, the benefit is transparency and agency. With AI tools, they gain clearly defined ranges for remittances, earlier alerts when a plan veers off track, and dashboards that explain why the system is recommending a change, not just what it recommends.
Case in point: JPMorgan’s tools combine machine-learning forecasts with real-time visibility and workflow automation, cutting manual effort and improving decision speed for clients. The important part for SMB finance is not copying the exact stack but adopting the principles of integrated data, rolling forecasts, and model-driven alerts. Those concepts are now reachable for smaller firms through embedded platforms and next-gen SMB software.
The cutting edge
Cash-flow underwriting depends on consented access to bank data. The industry has been moving away from fragile screen scraping toward secure APIs that give customers granular control over which accounts and fields can be shared and for how long. This shift reduces error rates, bolsters security and helps lenders meet compliance obligations. As open-banking practices spread, expect underwriting to feel more like linking a payroll app: authenticate, choose accounts, share selectively revoke easily.
Another fast-moving frontier is AI agents that behave like a virtual finance team that pulls bank feeds, reconciles transactions, projects cash flow and drafts recommendations. Early movers are positioning these agents as AI CFOs for SMBs that can’t afford a full finance department.
The promise is compelling: always-on financial monitoring plus human review for high-judgment calls. For lenders, that same agentic capability can standardize document collection, surface exceptions, and keep risk views current without drowning analysts in manual checks. SMB adoption of AI tools is accelerating, particularly in finance and operations. Survey data from the small-business ecosystem points to a majority of owners now using at least one AI-enabled product, with daily usage climbing. The early wins are pragmatic and include automation of repetitive workflows, faster reporting and better visibility. Hands-on programs and how-to resources are helping owners separate durable tools from hype and integrate them into everyday routines.
The need for guardrails
As AI moves deeper into underwriting and portfolio management, governance grows more important. Banking supervisors have long required robust model-risk management, including clear development and validation processes, performance monitoring, and effective oversight.Even nonbanks that rely on bank partners or sell to bank investors are feeling these expectations. Across jurisdictions, the regulatory arc points in the same direction: transparency, testing and controls commensurate with risk.
In the EU, for example, the new AI Act sets a risk-based framework that treats the assessment of creditworthiness as high-risk, triggering stricter obligations. Firms that build explainable models, document decisions and track outcomes will be better positioned as rules converge.
Practical AI playbook
Lenders need to standardize POS, bank and accounting inputs so models learn on consistent features rather than brittle one-offs. Instrument for feedback. They need to close the loop between predicted and actual cash performance to recalibrate quickly, and give risk analysts tools to interrogate drivers and override when the model’s confidence is low. Also, clear communication is a must. Merchants should see repayment logic, not just repayment results. Bundle tools, not just term sheets. The value proposition grows when forecasting and scenario modeling ride along with funding. These aren’t theoretical; they’re already embedded in programs merchants use daily.
Merchants need to treat forecasting as a weekly habit. Rolling views beat annual budgets. They should: use scenarios; model upside (a marketing win), downside (a supplier delay), and neutral paths; watch early-warning signals such as slipping deposit cadence, rising refunds and creeping expenses. With AI, merchants can make capital a tool, not a crutch. Advances and loans are accelerants that work best with a plan that turns dollars into durable earning power. And merchants should seek explainability. If a platform proposes a repayment adjustment, they deserve to know why. The same AI that evaluates risk should help business owners understand it.
Alternative financing is becoming an operating system comprising capital on demand, underwriting tied to real cash flows and AI that illuminates what’s ahead. As SMB tools mature and standards for data sharing and model governance crystallize, all parties involved can expect a financing experience that feels less transactional and more advisory, which is measurably better for merchants and safer for funders. The original promise of MCAs—speed and flexibility—remains. AI adds foresight. 
Chad Otar is CEO of Lending Valley Inc. For information about the company, please visit www.lendingvalley.com. To reach Chad, send an email to chad@lendingvalley.com.
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