The Underwriting Desk

    How to Analyze Bank Statements for MCA Underwriting

    A practical guide to reading business bank statements for MCA underwriting, including deposit quality, negative days, NSFs, existing debt, cash cushion, and repayment capacity.

    9 min readMCABank statementsUnderwritingBroker operations
    Printed bank statements, spreadsheet, and calculator arranged for underwriting review

    Bank statements are the center of MCA underwriting.

    A credit report can tell you how a business or owner handled obligations in the past. A bank statement tells you what is happening right now: deposits, cash cushion, existing payments, returned items, negative days, and whether the business can carry another daily or weekly payment.

    For brokers and ISOs, good pre-underwriting matters before a file ever reaches a funder. Cleaner submissions get reviewed faster. Weak files get spotted earlier. Explainable risk gets packaged with context instead of being left for the funder to guess.

    What you are trying to answer

    When you review bank statements, you are not just looking for revenue. You are trying to answer five questions:

    1. Is the revenue real and recurring?
    2. How stable is the cash flow?
    3. How much debt pressure is already in the account?
    4. Does the business maintain enough cushion to handle another payment?
    5. Are there red flags that need explanation before submission?

    Start with the right statement set

    Most MCA reviews start with recent business bank statements. Three to six months is common, and longer history can help when the business is seasonal or recent activity looks unusual.

    CheckWhat to look for
    Account ownershipBusiness name, DBA, or related operating entity
    Statement periodFull months, not screenshots or partial exports
    Account typePrimary operating account, not a rarely used account

    Calculate gross deposits, then clean them up

    Gross deposits are the starting point. Add all deposits for each month, then separate operating revenue from noise.

    Transfers, owner contributions, tax refunds, loan proceeds, MCA advances, one-time settlements, reversals, and returned deposits should be reviewed before they are counted as revenue.

    The goal is not to punish every unusual deposit. The goal is to avoid underwriting borrowed money as operating revenue.

    Look at deposit quality

    Deposit quality matters as much as deposit volume. A healthy operating account usually shows revenue that matches the business model.

    • Number of deposit days per month
    • Average deposit size
    • Largest single deposit as a percent of monthly deposits
    • Card processor deposits vs checks, wires, ACH, cash, or transfers
    • Customer concentration and seasonality

    Count negative days, NSFs, and returned items

    Negative days are one of the simplest ways to see cash stress. Track the count and trend, not just whether it happened once.

    NSFs, overdrafts, returned ACH items, returned deposits, and stop payments can signal payment pressure. One NSF tied to a processor delay is different from repeated returned ACH payments across three months.

    Identify existing MCA and debt payments

    Existing obligations are easy to miss if you only look at deposits. Review withdrawals for daily or weekly patterns that look like financing payments.

    • Same recipient every business day
    • Daily ACH debits in similar amounts
    • Weekly debits on the same day
    • Multiple funder names
    • Payments labeled as working capital, revenue advance, remittance, or ACH debit

    Estimate repayment capacity

    MCA underwriting often comes down to whether the business can handle the proposed payment without breaking cash flow.

    Estimated true monthly revenue$160,000
    Existing daily or weekly payment burden$13,300
    Proposed monthly payment burden$9,800
    Combined burden as percent of revenue14.4%

    The acceptable burden depends on funder policy, business type, margin profile, and cash-flow stability.

    MCA bank statement checklist

    Documents

    • Three to six recent complete business bank statements
    • All pages included
    • Same operating account across months, or explanation included
    • Business name or DBA matches application
    • No cropped screenshots or partial exports

    Revenue

    • Gross deposits calculated by month
    • Non-revenue deposits identified
    • True revenue estimated
    • Deposit frequency reviewed
    • Seasonality or revenue concentration noted

    Cash stress

    • Negative days counted
    • Low-balance days reviewed
    • NSFs and overdrafts counted
    • Returned items reviewed
    • Ending balance trend reviewed

    Existing debt

    • Daily and weekly ACH debits reviewed
    • Existing MCA payments identified
    • Other recurring debt payments identified
    • Estimated monthly debt burden calculated
    • Payoff or renewal context requested if needed

    Where AI helps

    AI should not replace underwriting judgment. It should reduce the manual work that slows judgment down.

    In bank statement analysis, AI can help extract deposits and withdrawals, categorize recurring payments, flag likely MCA debits, count negative days, spot unusual deposits, summarize trends, and produce a first-pass underwriting memo.

    The human still decides what matters. The system should surface the signal, show its work, and let the underwriter override it.

    That is the Plutus view: humans decide, AI surfaces.

    Final thought

    MCA underwriting does not start with a model. It starts with the cash moving through the business. Plutus is built to make that review faster and more consistent by turning statements into structured signals and helping teams apply good judgment on every file.

    View all articles

    Market Signals

    Morgan Stanley maps $1.5 trillion AI data-center financing gap

    Morgan Stanley says AI data-center spending needs about $1.5 trillion of outside capital. The underwriting lesson is lease cash flow, power access and refinancing evidence, not AI demand alone.

    3 min readBy Plutus Editorialprivate creditAI infrastructuredata centersasset-based financesecuritizationmarket signals
    Abstract data-center financing desk with lease schedules, power card, private-credit folder and securitization folder.

    Morgan Stanley estimates that global data-center spending will reach $2.9 trillion through 2028. According to the bank's July research and an August 6 podcast, roughly $1.5 trillion of that total must come from outside capital. Private credit and asset-based finance could provide $800 billion of this gap. Another $150 billion could come via data-center asset-backed securities (ABS) and commercial mortgage-backed securities (CMBS).

    The data-center financing surge involves a complex credit structure. Morgan Stanley projects that $1.5 trillion in outside financing will be required by 2028. This shifts the focus from general growth to specific collateral, lease terms, power contracts, refinancing windows, and underwritable cash flows.

    Porter Stansberry noted these financing questions in an August 11 thread. Additional details appear in Morgan Stanley Research, Morgan Stanley's published podcast transcript, Moody's data-center credit-risk page, and L&G's securitized-credit analysis.

    Morgan Stanley breaks down the capital stack as follows: $1.4 trillion of hyperscaler capital expenditure will come from cash flow, $200 billion from investment-grade tech debt, $800 billion from private credit or asset-based financing, $150 billion from securitized credit, and $350 billion from other sources. In the podcast, Vishwas Patkar stated the $1.5 trillion gap exceeds the size of both the high-yield and leveraged-loan markets.

    Financing is moving into several different underwriting channels. Corporate bonds rely on a single large issuer. Private credit and asset-based finance rely on project cash flows, leases, collateral, construction progress, and specific lender protections. Securitization typically occurs after an asset is built, leased, and generating income.

    L&G noted this sequence in a February report on data-center securitization. The firm stated that most securitized issuance refinances existing income-generating assets instead of funding new construction. Data-center ABS and CMBS issuance exceeded $25 billion in 2025. L&G also identified a refinancing risk: many deals have a five-year repayment target. This makes the period between 2028 and 2030 a critical test if refinancing markets decline.

    Moody's analyzes this market through credit risk. Its data-center page states that developers and landlords will use equity, bank loans, corporate bonds, securitized bonds, and project-finance vehicles. Leverage will likely increase for developers building hyperscale facilities between 2026 and 2028. Moody's also notes that power, water, and development approvals act as constraints.

    For operators in merchant cash advance (MCA), revenue-based financing (RBF), or equipment finance, the parallel is found in repayment logic. Rapid demand can mask poor repayment structures. A lender might see a merchant with high revenue but miss that the cash flow relies on a single processor, a seasonal buyer, an expiring contract, a second-position lender, or a landlord dispute. Similarly, a data-center lender might see AI demand but overlook risks regarding power delivery, tenant renewals, lease increases, obsolescence, construction timing, or the exit market.

    The underwriting process requires identifying the revenue source, the constraints, hidden debt-like obligations, and the exit strategy. If a deal relies on refinancing, lenders must identify the specific future buyer and the evidence required to secure them.

    Crowding is also a factor. If private credit absorbs hundreds of billions in data-center finance, lenders will remain selective elsewhere. The insurance, pension, and private-credit buyers funding infrastructure also fund warehouse lines and asset-backed facilities. If these buyers concentrate on one theme, specialty-finance originators should expect more scrutiny regarding concentration, liquidity, documentation, and performance history.

    AI infrastructure is not inherently bad credit. If leases are durable, power is secured, the tenant mix is stable, and a refinance path exists, the assets may be attractive. However, AI demand is not collateral. Collateral consists of the contract, the operating asset, power access, tenant payments, and the market capable of refinancing the structure.

    Opinion

    The collateral test is more than just the building

    AI infrastructure credit depends on leases, power access, tenant concentration, and takeout markets, not just rising compute demand. Smaller finance desks face the same reality: high demand in a sector does not fix a weak credit file. It only increases the number of lenders asking for proof of cash flow.

    Plutus tracks how capital-market pressure reaches the underwriting file at plutusai.net.