Stilt

Overview

Stilt made personal loans to U.S. immigrants who lacked the credit files that conventional lenders expected. It later packaged its lending licenses, underwriting, servicing, and reporting work into Onbo, infrastructure for other companies launching credit products. JG Wentworth acquired Stilt's digital lending platform in December 2022. The buyer did not disclose the price.

The story is both a focused lending wedge and a platform pivot. Stilt learned how to evaluate thin-file borrowers by operating the lender itself. It then tried to sell that operating stack to fintech companies. JG Wentworth ultimately bought the lending technology and data to accelerate its own consumer-credit entry.

Founding Story

Rohit Mittal and Priyank Singh met as international students at Columbia. Mittal struggled to rent an apartment without a U.S. credit record; Singh had difficulty financing school and buying a car despite a job offer. They tested alternative risk models at a Startup Weekend, but institutions did not buy the scoring product.

The founders switched from selling a model to making loans. They pooled savings and lent directly to strangers, using employment, education, visa, banking, and other information to judge repayment risk. Stilt joined Y Combinator's Winter 2016 batch.

Timeline

• 2015: Mittal and Singh began lending to immigrants with limited U.S. credit history.
• Winter 2016: Stilt joined Y Combinator.
• May 2020: It announced a $7.5 million seed round and roughly $100 million of lending capital.
• March 2022: Stilt announced $14 million in Series A equity, a $100 million debt facility, and the launch of Onbo.
• December 5, 2022: JG Wentworth acquired Stilt's digital lending platform.

What They Built

Stilt's consumer product offered loans to immigrants, visa holders, and other borrowers whose U.S. credit records were missing or sparse. Underwriting combined banking activity with employment, education, immigration, and credit data. The company also offered banking features as it tried to own a broader financial relationship.

Onbo exposed the machinery behind those loans to other businesses. It covered origination, compliance, payments, credit reporting, servicing, and access to debt capital. The pitch was that a fintech could add a credit product without assembling the licenses and operations Stilt had spent years building.

Market Position

Target Customers

The original customers were immigrants and international students overlooked by score-led lending. Onbo targeted fintech companies and neobanks that wanted to offer credit.

Market Size

Stilt addressed a large population with thin or nonexistent domestic credit files, then the broader market for embedded lending. Public sources did not provide a defensible serviceable-market estimate.

Competition

Consumer competitors included banks, credit unions, immigrant-focused lenders, secured cards, and marketplace lenders. Onbo competed with lending-as-a-service vendors, sponsor-bank programs, loan-management systems, and in-house compliance teams.

Business Model

Stilt earned interest and fees from loans while financing principal through debt facilities. Onbo shifted toward business contracts for credit infrastructure, likely combining software and financial-services economics. Pricing, cost of capital, loss rates, servicing expense, and contract terms were not public.

Traction

Stilt said it had lent hundreds of millions of dollars to tens of thousands of immigrants from more than 150 countries. In 2022 it reported fourfold annual revenue growth during 2021. JG Wentworth said the acquired system contained more than $5 billion in applications and 2.5 million bank transactions. These company-reported figures show scale but not loan performance or profit.

Post-Mortem

This was an acquisition of a lending platform, not a reported shutdown. JG Wentworth wanted technology, risk data, and operating capacity that could speed its move into direct consumer lending. Stilt's years of applications and transactions gave the buyer more than software: they supplied training and decision evidence for credit models.