When a Shark Tank alum's import costs doubled overnight due to Trump's second-term tariffs, traditional banks were not an option. A merchant cash advance provider funded $350,000 within hours, keeping the business alive but adding high-cost debt that would reshape its operations for months.¹ The story, reported by NPR on February 9, is the most visible example of a larger trend: tariff-driven cash crunches are funneling small businesses toward alternative lenders at an unprecedented rate.²

Under US Customs regulations, importers must pay tariffs at the port of entry, before selling a single unit. There is no deferral option for small businesses. When tariffs escalated to 10-25% on Southeast Asian goods and up to 60% on Chinese imports, importers who had planned inventory cycles around pre-tariff pricing were caught with immediate cash shortfalls.^{2 3}

Key Developments:

Import tariffs create a unique cash flow problem. Under 19 USC 1484, importers must pay duties when goods arrive at port, before any revenue is generated from sales. When tariff rates jumped from single digits to 10-60% across Asian manufacturing hubs, importers who had built their cash cycles around pre-tariff costs were hit with bills they could not cover from operating reserves.^{2 3}

The scale is significant. At least 236,000 small-business importers each paid an average of $151,000 more in tariffs between April and September 2025 compared to the prior year.^{6 7} Traditional banks, constrained by lengthy approval timelines and risk-averse credit committees, have largely declined to serve this segment. Fintech and alternative lenders now serve roughly twice as many small business borrowers as traditional banks, and that gap is widening.¹²

Slope, a JPMorgan-backed B2B lending platform, reported a 730% year-over-year increase in applications tied directly to tariff-related cash needs. Co-founder Alice Deng described the impact as "immediate."⁵ MCAs, revenue-based financing, and invoice factoring have become the primary bridge between tariff payment and product revenue for import-dependent businesses in 2026.

The same cash crunch that drives borrowers to alternative lenders also increases their default probability. Equifax's Small Business Delinquency Financial Index (SBDFI) stood at 3.14% in November 2025, with Texas (4.4%), Florida (4.3%), and Georgia (4.2%) posting the highest default rates.¹⁰ These states also carry significant import-dependent business populations.

The core risk is margin compression. The Shark Tank hose company saw import costs more than double to $8.67 per unit, while at least 10 other Shark Tank-backed companies rethought suppliers, pricing, and product launches as costs spiked without warning.³ An MCA funded against future revenue looks safe when margins are stable. But if the borrower raises prices and loses customers, or absorbs costs and burns through cash, the repayment stream weakens rapidly.

Portfolio managers should be watching for default clusters in import-heavy NAICS codes, particularly wholesale trade (423-424), manufacturing (31-33), and retail trade (44-45). A borrower whose cost of goods sold jumped 30-50% in one quarter is a fundamentally different credit profile than the same borrower six months prior.

Not all alternative lending borrowers carry tariff risk. The concentration is specific and measurable. According to the American Action Forum, 97% of the 242,515 US importers are small businesses with fewer than 500 employees. Among these, the smallest firms (under 50 employees) paid over $86,000 more in tariffs during the April-September 2025 period alone.⁷

High-exposure industries include consumer goods importers (NAICS 423), electronics distributors (NAICS 4236), apparel wholesalers (NAICS 4243), and auto parts distributors (NAICS 4231). E-commerce and direct-to-consumer brands are particularly vulnerable because they typically source the majority of their products from China and Southeast Asia.³ Lower-risk segments include domestic service businesses, professional services firms, and companies with primarily US-based supply chains. Lenders who segment portfolios by tariff exposure can make smarter allocation decisions and avoid concentration risk in import-heavy verticals.

Standard underwriting models were not built for overnight cost shocks. A business that shows 24 months of strong revenue history may still be a poor credit risk if its entire margin structure shifted in the past 90 days.

Three adjustments are worth considering. First, require documentation of supplier diversification. Borrowers who source from multiple countries or have begun reshoring some production are more resilient than those with single-source supply chains. Guardian Bikes, a Shark Tank alumnus, had already diversified away from Chinese manufacturing, allowing it to avoid the worst tariff impacts.³

Second, weight recent bank statements more heavily than historical averages. A 90-day trailing revenue figure captures the post-tariff reality better than a 12-month average that includes pre-tariff performance.

Third, evaluate the borrower's pricing power. A business that can pass tariff costs to customers without losing significant volume presents a fundamentally different risk profile than one in a price-sensitive commodity market. In the auto industry, manufacturers initially absorbed tariff costs rather than raise prices, a strategy that works for companies with deep balance sheets but not for small importers running thin margins.⁴

Revenue-based financing has a structural advantage in tariff-stressed markets. Because RBF payments flex with actual revenue, a borrower who experiences a temporary sales dip pays less during the downturn and more during recovery. MCAs, with fixed daily or weekly ACH debits, can drain a business that is already cash-constrained.

For lenders, RBF offers better alignment with borrower health. A tariff-stressed importer using RBF will naturally pay slower if sales soften, giving both parties time to adjust. The same borrower under an MCA may default simply because the fixed payment schedule does not match their post-tariff cash flow pattern.

The trade-off is yield. MCAs typically deliver 20-50% effective APR on short terms, while RBF factors tend to run lower at 1.1-1.3x. The US MCA market reached $19.65 billion in 2024 and is projected to grow to $32.7 billion by 2032, suggesting that demand for both products will expand.⁹ Lenders evaluating the tariff-driven segment should weigh the lower unit economics of RBF against the potentially higher default rates of MCAs when borrowers are under margin pressure.

Distressed borrowers are more likely to engage in stacking, taking multiple MCAs simultaneously, and tariff pressure creates exactly the conditions that enable it. A business owner facing an immediate customs bill may apply to multiple lenders in the same week, hoping to aggregate enough capital to cover the shortfall.

The Federal Reserve Bank of Boston has flagged a broader concern: synthetic identity fraud losses crossed $35 billion in 2023, and generative AI is amplifying the threat.¹¹ When demand surges and lenders compete to deploy capital quickly, verification shortcuts become tempting. Shell companies with fabricated revenue histories can look like legitimate importers seeking tariff relief.

For alternative lenders, entity verification becomes more critical during market stress, not less. Confirming that a business is legally active, operating in its claimed state, and registered under consistent ownership takes seconds with automated tools but catches the fraud signals that manual review misses under speed pressure.

The regulatory environment is shifting in ways that directly affect how alternative lenders serve tariff-stressed borrowers. In November 2025, the CFPB proposed to remove MCAs from Section 1071 small business lending data collection requirements, reducing a significant compliance burden for MCA providers and pushing the compliance deadline to January 2028.¹³

However, media coverage framing alternative lending as "shadowy" and "predatory," as NPR's reporting did, tends to attract legislative attention.¹ State-level rate caps and disclosure requirements continue to expand. Meanwhile, a Goldman Sachs survey found that 77% of tariff-affected business owners cite uncertainty as their primary concern, which may increase political pressure for either borrower protections or lending incentives.⁸

The most productive regulatory posture for alternative lenders is proactive transparency. Lenders who document their underwriting rationale, maintain verifiable data trails, and demonstrate responsible lending practices are better positioned when scrutiny increases. Those who rely on speed alone, without corresponding verification, are the ones who generate the headlines that invite regulation.

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Zocks, the healthcare AI startup, has closed a $45 million Series B to scale its agentic AI platform. While Zocks targets healthcare documentation, the underlying technology, autonomous AI agents that execute multi-step workflows without human intervention, has direct implications for fintech lending operations. Alternative lenders exploring AI-driven underwriting, compliance monitoring, and portfolio management should watch how agentic AI moves beyond single-task automation into full workflow orchestration.

New York-based alternative asset manager Davidson Kempner is expanding its asset-based lending operations to target European small and medium enterprises. The firm sees a large addressable market as traditional European banks pull back from SME lending, mirroring the pattern US alternative lenders have exploited domestically for over a decade. For US-based lenders, this signals growing global competition for deal flow and potential partnership opportunities in cross-border lending.

New Jersey's Attorney General has introduced rules that significantly expand disparate impact liability for lenders, including those using AI and algorithmic underwriting systems. The rules require lenders to demonstrate that their automated decision-making does not produce discriminatory outcomes, even unintentionally. Alternative lenders using machine learning models for credit scoring, pricing, or risk assessment should review their systems for compliance before these rules take effect.