Assess an AI startup’s credit risk by testing whether its expected operating plan can generate enough cash to repay the proposed debt on time. Start with the contractual primary repayment source, build a going-concern forecast that includes operating costs and capital expenditure, measure liquidity through the debt maturity, and stress the assumptions that are unusually important to AI businesses. Then examine revenue durability, customer concentration, financing dependence, collateral, guarantees, and credible secondary repayment sources.
Fundraising prospects, a large addressable market, technical achievements, and recurring revenue can support the case, but none proves repayment capacity. The Office of the Comptroller of the Currency (OCC) states the central caution plainly: Recurring revenue is not equivalent to sustainable repayment capacity.
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1. Define the borrower, facility and repayment source
Credit analysis begins with the legal obligation, not the startup’s pitch deck. Identify the exact entity borrowing, every guarantor, the amount and currency, tenor, amortization schedule, interest structure, covenants, seniority, security package and events of default. Confirm which entity collects customer cash, pays employees and infrastructure providers, owns intellectual property, and holds pledged assets. A group chart should show where cash can and cannot move.
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Write the repayment source in one sentence before reviewing secondary support. For example: “Scheduled principal and interest will be paid from collections generated by the operating company’s contracted software customers.” If the proposed repayment actually depends on another equity round, refinancing, an asset sale, a parent contribution or a founder’s guarantee, state that separately. Do not silently treat a hoped-for financing event as operating cash flow.
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Primary versus secondary repayment
The primary source is the cash stream expected to meet contractual debt service in the ordinary course. Secondary sources are fallback options, such as committed equity, a refinancing, a public or private capital raise, an asset sale or a guarantor payment. The OCC’s venture-lending guidance notes that early-, expansion- and late-stage borrowers may have negative, intermittent or insufficient cash flow and may rely on these alternatives. That makes explicit assessment essential: identify who controls the source, when it could be available, what conditions must be met and how its value changes in a downturn.
Late-stage status does not automatically convert a venture borrower into a cash-flow borrower. A company can be mature in valuation terms while still depending on outside capital to cover payroll, compute bills or debt maturities.
Facility facts that change the risk
- Tenor and amortization: A short maturity can create refinancing risk even when cash is plentiful today. Confirm whether principal is fully amortizing, partially amortizing or due in a bullet payment.
- Priority: Map existing secured and unsecured obligations, intercompany claims and any payment blockage provisions.
- Covenants: Note minimum cash, liquidity, leverage, borrowing-base, reporting and additional-debt tests, including cure rights and testing frequency.
- Security: Specify the pledged receivables, equipment, intellectual property, cash accounts or equity interests, the lien priority and the jurisdiction governing enforcement.
- Guarantees: Record the guarantor’s net worth, liquidity, legal limitations and whether the guarantee is capped, subordinated or subject to a materiality threshold.
2. Build a realistic going-concern base case
The base case should represent the operating plan judged most likely to occur, not a wind-down forecast and not an optimistic fund-raising narrative. The OCC says recurring-revenue facilities should normally begin with cash-flow projections that include all planned expenses and capital expenditure. Include the spending required to keep the product functioning and customers renewing.
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| Area | What to model | Questions for the credit file |
|---|---|---|
| Collections | Cash receipts by customer, contract, geography and month | Are invoices collected on the stated terms, or does the model assume immediate payment? |
| Gross margin | Revenue less model inference, hosting, data, support and implementation costs | Does margin reflect actual usage, committed capacity and provider pricing? |
| Payroll | Engineering, research, sales, support, security, finance and compliance headcount | Are hiring plans, bonuses, severance and payroll taxes included? |
| Research and development | Model training, evaluation, data acquisition, tooling and safety work | Can planned releases be delayed without harming renewals? |
| Customer acquisition | Sales commissions, marketing, onboarding and implementation labor | What cash must be spent before a new contract produces collections? |
| Working capital | Receivables, payables, prepaid compute, deposits and refunds | Do payment terms create a cash gap even when revenue is recognized? |
| Capital expenditure | Servers, networking, data-center fit-out, security equipment and other required assets | Which purchases are unavoidable, and which can be leased or deferred? |
| Debt service | Interest, scheduled principal, fees, hedging and mandatory prepayments | Does the forecast show cash available after every contractual payment? |
Separate accounting revenue from cash collections. A usage contract may produce a large booked amount while payment arrives later or varies with inference volume. Reconcile the forecast to bank statements, accounts-receivable aging and signed contracts. Document each major assumption, the evidence supporting it and the person responsible for updating it.
Do not substitute a harvest case for ordinary performance
A wind-down or “harvest” case can help estimate recovery after the business stops investing, but it strips out costs needed to operate as a going concern. Removing research, support, security or customer-acquisition spending can materially overstate cash available for debt service. Use it as a separate sensitivity, never as the expected base case or as an excuse to rate a weak primary source as sound.
3. Measure runway, debt service and financing dependence
Runway indicates when the company may need new funding; it does not by itself show that a loan can be repaid. Calculate monthly net burn from actual cash movement, then reconcile unrestricted cash, committed capital, debt service and maturity dates.
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A simple starting measure is runway months = unrestricted cash available for operations ÷ average monthly net cash burn. Use a forward-looking burn that includes scheduled debt service and the planned operating budget, not a historical average from a period of unusually low hiring or unusually high customer prepayments. Show the result by month because burn often changes sharply when a model is deployed at scale or a debt facility begins amortizing.
Questions a runway analysis must answer
- How much cash is unrestricted, and how much is trapped in a subsidiary, reserved for a lender or committed to a vendor?
- When does each principal installment or bullet maturity fall due?
- Which equity, grant or credit commitments are signed, funded and unconditional, and which are only indications of interest?
- What happens if a planned round closes later than expected, at a lower valuation or for a smaller amount?
- Can the business reduce burn without causing customer churn, security failures or a breach of contract?
- Does the company need continuous access to private credit or asset financing to replace expiring facilities?
Compare the runway date with the earliest debt maturity and with the time required to raise capital. A company can have many months of cash and still face a high-risk bullet maturity if its next financing window is uncertain. Conversely, a business with negative free cash flow may be financeable when a committed, contractually available source covers the gap and the lender’s structure limits refinancing exposure.
4. Test revenue quality instead of using ARR as a proxy for repayment
Recurring or contracted revenue is useful evidence, but its credit value depends on what customers actually pay, how long they remain, and what it costs to serve them. Review each material customer and aggregate the portfolio by contract, industry, geography and channel.
Contract and collection review
- Distinguish committed subscription fees from usage-based, milestone-based and discretionary spending.
- Check contract duration, renewal mechanics, termination rights, service-level credits, price concessions and change-of-control clauses.
- Compare invoice dates, contractual payment terms and actual collection history. Investigate receivables that are repeatedly extended or disputed.
- Measure gross and net retention, logo churn, expansion, downgrades and renewal cohorts. A high renewal rate from a few customers is still concentrated risk.
- Calculate the cost to deliver and support each customer, including model inference, implementation, human review, security and regulatory work.
- Identify dependence on one platform, reseller, data supplier or strategic partner that could alter access to customers.
Ask whether customers can switch to a cheaper model, bring workloads in-house or reduce usage during a budget cut. A contract that is technically renewable but economically optional should not be valued like a long-term, non-cancellable obligation. The relevant test is collections after the full cost base required to retain and serve the customer.
Concentration and cohort stress
Model the loss or delay of the largest customer, the largest vertical and the largest reseller separately. Apply lower renewal rates and longer payment times to newer cohorts, where evidence is thinner. If one customer supplies a large share of cash, evaluate that customer’s own financial health, termination rights and procurement cycle. Concentration can turn a seemingly stable monthly revenue stream into a single-obligor exposure.
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5. Stress assumptions that are specific to AI businesses
AI startups can have cost structures and dependencies that change faster than ordinary software models. Stress the operating assumptions rather than assigning a generic “AI premium.”
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Compute and infrastructure
- Increase inference volume per customer and test whether gross margin falls when usage grows faster than prices.
- Model higher compute prices, minimum-spend commitments, reserved-capacity payments and interruption by a cloud or model provider.
- Test the loss of preferential access, a change in provider terms or a requirement to migrate models and data.
- Include energy, networking, storage, observability, security and human-review costs that may rise with scale.
Product adoption and monetization
- Delay the expected conversion of pilots into paid production deployments.
- Reduce willingness to pay, seat expansion and usage growth while preserving contractual support obligations.
- Extend implementation timelines and include remediation work when accuracy, latency, safety or integration targets are missed.
- Separate revenue from a genuinely repeatable product from one-off model-training, consulting or customization work.
Technology, data and resilience
Review rights to training and production data, licensing restrictions, privacy obligations, security controls, model-evaluation processes and business-continuity plans. A loss of data rights, a security incident or an unavailable model provider can interrupt collections even when demand remains strong. For a borrower whose product is materially AI-dependent, these are repayment assumptions, not merely technical diligence items.
The Bank for International Settlements reported in 2026 that financing for AI-related investment is shifting increasingly from operating cash flows toward debt, with private credit playing a rapidly increasing role. That trend makes earnings expectations, leverage and refinancing dependence important stress variables, but it does not supply a borrower-level probability of default. Test the specific company’s ability to meet the earnings and cash-flow assumptions behind its debt.
6. Evaluate collateral, guarantees and fallback recovery
Many venture borrowers lack conventional collateral and established net worth, so source-of-repayment analysis carries unusual weight. Still, a careful collateral review can reduce loss severity.
Collateral checklist
- Identify the legal owner and physical location of every pledged server, networking asset, receivable, deposit account, intellectual-property right or equity interest.
- Verify lien perfection, priority, exclusions, cross-collateralization and the lender’s ability to control proceeds.
- Use a defensible valuation basis. Specialized hardware may sell below book value, become obsolete quickly or require a buyer with compatible software and power capacity.
- Assess whether licenses, data rights, export controls, privacy duties or customer contracts restrict transfer or monetization.
- Trace assets held by special-purpose vehicles, lessors or affiliates rather than assuming the operating company can pledge them.
For guarantees, establish the guarantor’s liquidity at the time a payment would be demanded, not only its headline valuation. Note caps, subordination, fraudulent-transfer limits, foreign-law enforcement issues and competing guarantees. A founder or parent promise without accessible assets is not equivalent to cash collateral.
7. Compare financing structures by their real repayment mechanics
Different facilities should not be judged with one generic startup score. Compare the source and control of repayment, the lender’s monitoring ability and the downside recovery path.
| Facility | Primary repayment source | Key underwriting focus | Typical downside concern |
|---|---|---|---|
| Conventional cash-flow loan | Operating cash flow after all expenses and capex | Stable margins, debt-service coverage, forecast accuracy and amortization | Cash flow weakens before scheduled payments are complete |
| Venture loan | May be operating cash flow or an explicitly assessed secondary source | Runway, financing dependence, investor support, covenants and maturity timing | Expected equity or refinancing is delayed or unavailable |
| Recurring-revenue facility | Collections from contracted or subscription customers | Contract quality, retention, concentration, collection history and full cost to serve | Usage falls, customers terminate, or gross margin cannot support debt service |
| Asset-based facility | Borrowing-base collections or liquidation of controlled assets | Eligible receivables, advance rates, field controls, lien priority and collateral value | Receivables become ineligible or assets cannot be liquidated at the assumed value |
The OCC treats controlled asset-based lending as distinct from venture-loan guidance. For every structure, document what happens when the primary source fails and how quickly the lender can detect and control the loss.
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8. Use cash-flow and alternative data with disciplined controls
Bank statements, payment processors, payroll records, invoices and cloud bills can reveal whether recurring obligations are being met over time. Cash-flow analysis is an established component of small-business underwriting and can complement financial statements, especially when audited accounts are limited.
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Do not equate lender AI adoption with safer lending
A Federal Reserve Bank of San Francisco Economic Letter published September 21, 2026 found that banks with higher AI adoption had average return on assets about 0.38 percentage point higher than banks with lower adoption, but also had higher average shares of problem loans. The same analysis found an average SME-loan share of 12% at high-AI banks versus 21% at low-AI banks. The authors state that these are correlations and do not establish causation. They describe bank-level lending patterns, not the default risk of an individual AI startup. A lender’s automated underwriting system therefore cannot substitute for borrower-specific repayment analysis.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Turn the analysis into a downside-tested decision
Use scenarios that change one assumption at a time and then combine the most plausible adverse events. At minimum, test:
- Collections stress: slower payment, higher disputes, lower renewals and the loss of the largest customer.
- Margin stress: higher inference and hosting costs, more human review and delayed price increases.
- Funding stress: an equity round arriving later, at a smaller size or not at all; refinancing unavailable at maturity.
- Execution stress: delayed product launches, implementation overruns, security remediation and slower sales conversion.
- Infrastructure stress: provider outage, capacity constraints, changed terms or a forced migration.
- Recovery stress: lower collateral value, delayed asset sales and limited guarantor liquidity.
For each scenario, record the month in which cash falls below the minimum, the covenant breached, the amount of additional funding required and the lender’s available remedies. A useful output is not a single score but a clear decision: approve as structured, approve with tighter controls or a smaller amount, require additional support, or decline because repayment depends on an unverified event.
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10. Set monitoring that tracks the underwriting thesis
Monitoring should measure the variables that support repayment, not only standard financial statements. Set reporting frequency and trigger levels before closing.
| Monitoring item | Evidence to obtain | Reason to escalate |
|---|---|---|
| Cash and runway | Unrestricted cash, 13-week cash forecast and updated burn | Runway shortens materially or falls below the time to refinance |
| Collections | Monthly receipts, receivables aging and overdue concentration | Payment delays, disputes or a rising share of one customer |
| Customer health | Renewals, churn, downgrades, usage and pipeline conversion | Renewal weakness or dependence on uncontracted pipeline |
| Forecast variance | Actual versus budget revenue, margin, payroll, capex and burn | Repeated misses or unexplained changes in assumptions |
| Debt and funding | New borrowing, maturities, equity commitments and investor updates | Unapproved debt, delayed financing or a maturity without a credible source |
| AI infrastructure | Provider contracts, capacity, unit costs, outages and model changes | Material cost increase, loss of access or inability to meet service obligations |
Reassess the risk rating when the primary repayment source weakens. An alternative “harvest” analysis should not drive a curtailment or severe rating action before default is actual or highly probable; the OCC distinguishes expected primary repayment from alternative scenarios.
11. Governance context for AI-dependent borrowers
Governance is relevant when it protects the cash flows on which repayment depends. Review accountability for model changes, data rights, security incidents, vendor concentration, customer disclosures, human oversight and continuity testing. The Financial Stability Board’s June 2026 consultation report proposes 12 sound practices for financial institutions’ AI governance across the AI lifecycle. Those practices are contextual governance guidance, not a startup credit-scoring standard. Apply them to understand operational resilience, while keeping financial underwriting separate.
Jurisdiction matters. OCC Bulletin 2025-45 describes supervisory expectations for U.S. national banks and is not a universal rule for every lender or borrower. Alternative-data protections, privacy law, secured-transactions rules, insolvency law and enforceability of guarantees vary by location. Obtain jurisdiction-specific legal review before converting this framework into a regulated lending process.
12. Practical evidence pack for a credit file
A decision is stronger when every major assumption can be traced to a document and refreshed after closing. Request, as applicable:
- Current cap table, legal-entity chart, existing debt schedule and all guarantees.
- Monthly management accounts, bank statements, cash forecast and accounts-receivable aging.
- Customer contract register showing term, renewal, termination, pricing and payment history.
- Revenue-retention cohorts, customer concentration and usage-to-billing reconciliation.
- Detailed infrastructure and model-provider agreements, committed spend and unit-cost history.
- Headcount plan, payroll obligations, research roadmap, security budget and capital-expenditure schedule.
- Intellectual-property ownership, data licenses, insurance, material litigation and regulatory correspondence.
- Evidence for committed equity, grants, refinancing or asset-sale plans presented as secondary repayment sources.
Mark information that is management-provided, independently verified, estimated or contractually committed. The distinction helps the lender decide which assumptions need covenants, reserves or additional diligence.
Bottom line for an AI-startup credit decision
The decisive question is whether realistic collections, after the spending required to operate and deliver the product, cover interest and principal through the facility’s maturity. Recurring revenue and retention improve the evidence but do not establish sustainable repayment capacity. AI-specific cost, provider, adoption and data dependencies should be stressed alongside customer and financing risk. Equity expectations, refinancing and asset sales belong in the analysis only as explicitly identified and tested secondary sources. A lender that keeps those distinctions clear can underwrite an AI startup without confusing technical promise or fundraising momentum with debt repayment.
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