Finda: Freelance Bank Loans Up 300% Per Quarter Over the Past Year

"Freelancers Account for 10% of All Users in the First Quarter"

Finda, an artificial intelligence (AI) loan comparison platform, announced on April 21 that the volume of freelance loan brokerage transactions through its application has increased by an average of 300% per quarter over the past year.

Freelancers often face loan rejections from commercial banks and internet-only banks due to their irregular income.

Finda has provided a platform that allows freelancers to thoroughly compare various loan products, including the Sae-Hyup Hope Policy Loan and living expense loans, which are also available to freelancers.

Finda explained that the increase in freelance loan performance was due to tripling the lineup of bank products offered on its platform.

From the first quarter of last year to the first quarter of this year, the number of loans obtained by freelancers through Finda increased by an average of 305.7% per quarter. The total loan amount increased by 389.2%.

The loan conditions for freelancers were not inferior to those for other occupations. According to Finda, the average interest rate for emergency loans from commercial banks for freelancers was 6.20%, similar to the overall user average of 6.22%.

For auto title loans, where freelancers had the highest average loan execution amount at 16.78 million won, the limit was higher than that for all users, who averaged 16.24 million won.

The number of freelance users on Finda continued to rise, increasing by 22% year-on-year in the first quarter. Freelancers accounted for 10% of all users.

During the same period, the number of credit limit inquiries by freelancers also increased by 170%, reaching approximately 283,000 cases in the first quarter.

Seo Gwansu, Head of Partnerships at Finda, stated, "We are expanding products tailored to new filers, such as freelancers, who lack sufficient financial transaction history," and added, "We will support loan approvals by utilizing Finda's unique alternative credit evaluation model, which combines non-financial information such as actual income and consumption data."

This content was produced with the assistance of AI translation services.