Glendale, CA - Oct 31, 2025 - RadCred, a leading AI-powered loan platform, today released a survey revealing that 58% of gig-economy workers seek emergency loans at least once every quarter. The findings highlight the financial strain caused by inflation, unstable income, and limited access to traditional credit among America's 70 million gig workers.

Inflation Drives Record Demand for Emergency Loans Among Gig Workers

According to RadCred's 2026 Gig Economy Lending Report, nearly six in ten gig workers, including rideshare drivers, delivery couriers, freelancers, and creators, reported borrowing for essential expenses such as rent, medical bills, and car repairs. Among those surveyed:

• 73% said inconsistent income made traditional loans inaccessible.

• 61% relied on no-credit-check or soft-pull loans for emergency funding.

• 22% increase in average loan amount year-over-year (2025–2026).

• 68% received funds within 24 hours using RadCred's AI-powered matching system.

The survey, conducted in February 2026 among 2,500 U.S.-based gig economy workers across 45 states, reveals widespread financial insecurity despite the gig economy contributing over $455 billion to the U.S. economy in 2025. With 36% of the American workforce engaged in freelance or contract work, demand for alternative lending solutions has surged as traditional banks reject nearly half of applicants with irregular income.

"The gig economy has created flexibility, but also fragility," said Alex Zadorian, CEO of RadCred. "Our data shows that millions of self-employed Americans are one unexpected bill away from financial distress. RadCred's mission is to connect them with responsible, same-day lenders who evaluate their income, not just their credit score."

AI and Alternative Data Are Redefining Credit Access in 2026

RadCred's study aligns with the Federal Reserve's 2024 Economic Well-Being Report, which found that 37% of Americans would struggle to cover a $400 emergency expense. The problem is amplified among gig-economy workers, who lack employer-sponsored safety nets and face loan rejection rates as high as 75% at traditional banks.

RadCred's platform addresses this gap by analyzing over 100 alternative data points, including bank deposits, rent payments, utility bills, and gig platform earnings, using AI-driven loan matching. This enables approval decisions in 2.5 minutes, compared to days or weeks at traditional institutions.

"AI-based loan matching allows us to evaluate deposit patterns and payment history instead of just credit score," added Sarah Kim, Data Analyst at RadCred. "It replaces bias with insight, and helps overlooked borrowers access credit safely."

Among respondents who used RadCred's platform, 91% received same-day funding, and 89% completed the application process in less than five minutes.

Emergency Borrowing Trends Among U.S. Gig Workers (2025-2026)

A 2025 study by the Alliance for Financial Inclusion found that alternative data in lending models reduced rejection rates by 70% for borrowers typically excluded by traditional credit systems. RadCred's technology uses machine learning to assess creditworthiness based on real-time financial behavior rather than historical credit scores.

Industry experts predict that by 2030, AI-powered lending platforms will serve millions of gig workers as the workforce shifts toward flexible employment. RadCred's findings underscore the urgent need for financial products tailored to the gig economy's unique income patterns and emergency borrowing needs.

About RadCred

RadCred is a U.S.-based online loan marketplace that connects consumers with trusted, licensed lenders. Using AI-driven loan matching, RadCred enables borrowers with low or no credit scores to access same-day personal and emergency loans. Committed to transparency and compliance, RadCred's mission is to promote financial inclusion for all.

Disclaimer: RadCred is not a direct lender and does not make credit decisions. Loan terms, rates, and approvals vary by lender and state regulations. Consumers should review lender disclosures carefully before accepting an offer.

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