The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →There is no evidence-supported winner between AI and traditional small-business lending in 2026. “AI lending” can mean automated analysis by a lender, an online application process, or a borrower’s exposure to AI-related business changes. Those are different things. For a borrowing decision, compare the actual lender and offer: eligibility, total repayment, payment schedule, collateral or guarantees, application requirements, documented funding timing, and how to ask about or correct a decision.
What does “AI lending” mean?
The phrase is used for several distinct ideas. Separating them prevents a common mistake: treating an online loan as proof that AI made the credit decision.
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- AI in underwriting: A lender may use AI or other automated tools to analyze an application. The sources available do not establish that this necessarily makes decisions faster, cheaper, fairer, or more accurate.
- Online or fintech lending: This describes a lender channel or application experience. A digital process does not, by itself, show what underwriting method the lender used.
- A business’s AI exposure: This concerns whether AI may benefit or adversely affect a company. It is about the borrower’s business prospects, not whether the lender uses AI.
The Federal Reserve’s January 2026 Senior Loan Officer Opinion Survey (SLOOS) addressed the third meaning: banks reported being more likely, on net, to approve firms benefiting from high AI exposure and less likely to approve firms adversely affected by high AI exposure, compared with the beginning of 2025. That finding does not say banks used AI to decide whether to approve those firms. Federal Reserve SLOOS, January 2026
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There is evidence about AI adoption and lending, but it is not a controlled, current head-to-head test of AI underwriting versus a human loan officer across approvals, rates, defaults, fairness, explanations, and appeals.
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A bank AI-adoption study is not a universal verdict
A 2025 U.S. Census Bureau Center for Economic Studies working paper examined bank AI adoption and small-business lending over 2017–2022. In the study data, banks used AI on average 11% of the time. The authors reported more lending to distant borrowers at banks with greater AI use, as well as lower default rates from and higher interest rates to those distant borrowers. These are findings associated with a defined study and borrower group; they do not prove that AI caused those outcomes or predict what any particular applicant will receive. Census CES working paper, January 2025
Agency AI plans do not establish private-lender practices
An SBA Office of Inspector General report effective September 24, 2026, describes intended agency uses including fraud detection in disaster-loan programs and support for monitoring 8(a) program participants. The OIG said SBA had not implemented a process to identify high-impact AI uses as of the review ending April 30, 2026, and recommended stronger strategy and governance. This concerns SBA’s own AI governance; it does not show that private lenders use AI to underwrite small-business loans. SBA Office of Inspector General report
How do small businesses use different lender channels?
The Federal Reserve Banks’ 2026 Report on Employer Firms, based on the 2025 Small Business Credit Survey, shows that businesses seek several forms of financing and do not all receive the amount they request. In that survey, 38% of firms had applied for a loan, line of credit, or merchant cash advance in the prior 12 months. Among applicants, 42% received all the financing sought, 36% received some or most, and 22% received none. These are survey results for respondents, not a forecast of an individual application.
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Online lenders were sought by 29% of applicants in the 2025 survey, compared with 17% in the 2020 survey. The increased share indicates greater use of that channel among applicants in those surveys; it does not establish that an online lender uses AI or that an applicant will qualify. Federal Reserve Banks, 2026 Report on Employer Firms
| Option or channel | What to establish before comparing | What the cited evidence says |
|---|---|---|
| Bank loan or line of credit | Ask what credit history, cash flow, business performance, equity, collateral, and guarantor information will be considered; confirm payment terms and any guarantees. | SBA lender guidance says lenders may consider these factors and assess eligibility and reasonable repayment ability. It does not prescribe one formula for every lender. SBA lender resources |
| SBA-backed financing | Identify the specific SBA program and its lender, eligibility rules, application requirements, and repayment terms. Do not assume all SBA financing has identical procedures. | SBA programs have distinct terms and processes. SBA Microloans are $50,000 or less and are made through intermediary lenders. SBA lender resources |
| Online or fintech lender | Determine the exact total repayment, fees, payment frequency, term, and whether pricing can change. Ask what information is used and how to seek an explanation or correct an error. | In the 2025 Small Business Credit Survey, 29% of applicants sought an online lender. The survey does not establish an individual lender’s underwriting technology or terms. Federal Reserve Banks, 2026 Report on Employer Firms |
| Merchant cash advance | Read the contract for the total amount owed, how and how often payments are collected, fees, and the effect of variable sales or payment terms on cash flow. | The cited survey includes merchant cash advances in its measure of firms applying for financing; it does not supply comparable terms for individual products. Federal Reserve Banks, 2026 Report on Employer Firms |
Why should borrowers scrutinize the full cost?
In the 2025 Small Business Credit Survey, 60% of respondents who borrowed from online lenders said their actual borrowing costs were higher than expected. The corresponding shares were 37% for small-bank borrowers and 32% for large-bank borrowers. These are borrowers’ retrospective reports, not proof that every online loan costs more, nor evidence that AI caused a cost difference. Use them as a reason to check the specific offer rather than relying on a lender category or a headline rate. Federal Reserve Banks, 2026 Report on Employer Firms
Compare the obligation, not just the rate
Before accepting an offer, put the following terms side by side:
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- Total repayment: the complete amount due over the life of the financing, including fees and any charges that apply under the contract.
- Payment structure: payment amount and frequency, term, and whether payments or pricing can vary.
- Security and liability: collateral required and any personal or business guarantees.
- Amount actually available: distinguish the amount requested from the amount approved and ultimately disbursed.
- Cash-flow fit: whether the payment timing works with the business’s expected receipts and other obligations.
A lender’s quoted rate alone does not answer whether the payment schedule, fees, and repayment obligation fit the business. Obtain the full written terms and ask for clarification on any item that is not explicit.
How can you assess eligibility and the application experience?
“Traditional lending” is not one underwriting formula. SBA lender guidance says lenders may consider business credit scoring or history, credit histories of applicants and guarantors, cash flow, equity, and collateral, while confirming program eligibility and reasonable ability to repay. SBA programs also differ from one another, so the relevant requirements depend on the financing sought. SBA lender resources
For each lender, ask what records it reviews, how it evaluates repayment ability, what would make an application incomplete, and how to request an explanation of a decision. If information in a credit or business record is wrong, ask the lender how to identify the record and submit a correction. Do not assume that an automated or human process will be more explainable without asking.
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Fair-lending oversight is relevant to both application experience and recourse, but available evidence should be read within its scope. The CFPB’s pilot involved 100 in-person test visits across 25 unique bank branches in Fairfax County, Virginia, and 25 branches in Nassau County, New York. It found statistically significant disparities in two aspects of treatment. The limited geography does not establish a nationwide rate of discrimination, and the study did not test AI underwriting specifically. CFPB pilot study summary
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is changing in small-business lending data rules?
Section 1071 of the Equal Credit Opportunity Act concerns collection and reporting of small-business lending data. The CFPB lists its statutory purposes as “Facilitating enforcement of fair lending laws” and “Enabling communities, governmental entities, and creditors to identify business and community development needs and opportunities for women-owned, minority-owned, and small businesses.” These purposes concern data and oversight; they do not show that a lender’s underwriting is AI-driven. CFPB Regulation B rulemaking timeline
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Quick Recap
A practical way to choose between offers
- Define the financing need. Record the amount, purpose, timing, and whether a term loan, line of credit, SBA program, or another product matches the need.
- Check eligibility before applying. Ask each lender which business and personal credit records, financial statements, collateral, guarantors, and program criteria apply.
- Collect complete written offers. Include fees, total repayment, payment frequency, term, collateral, guarantees, and any variable pricing—not only the advertised or quoted rate.
- Compare access and execution. Check approved versus requested amount, documentation burden, any stated funding timing, and the steps for questions, explanations, or corrections.
- Choose by fit and verifiable terms. Do not infer that a lender is faster, cheaper, fairer, or more accurate solely because it describes itself as AI-powered, online, or traditional.
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