A Case Study: The Underwriting for Racial Justice (URJ) Lender Pilot Program
Community lenders are uniquely positioned to expand credit access, but they face the operational realities of limited staff resources, manual bottlenecks, and traditional underwriting systems that weren’t designed with underserved borrowers in mind.
To stay competitive and profitable, they need modern, data-driven infrastructure that scales relationship-driven lending without swelling overhead or increasing risk.
The Underwriting for Racial Justice (URJ) Lender Pilot Program, led by Beneficial State Foundation (BSF), was designed to help lenders overcome these challenges and expand credit access to the communities they serve.
As the program’s sole technology provider, Stratyfy has helped lenders nationwide make faster, more precise, and more transparent credit decisions, improving both efficiency and equity across their portfolios.
The URJ Lender Cohort
The URJ program is comprised of 18 lenders of different sizes, each serving different communities across the nation and offering a wide range of products, including residential mortgages, small business loans, automobile loans, consumer loans, and development loans.
Stratyfy’s Approach
Traditional underwriting models rely on rigid, one-size-fits-all scoring that can be slow, labor-intensive, and expensive. It leaves lenders defaulting to manual reviews – or even flat out rejections – whenever an applicant doesn’t fit a standard profile.
The result? Staff capacity consumed by manual work, growth capped by processing speed, and good applicants slipping through the cracks simply because the system couldn’t evaluate them accurately enough to say yes with confidence.
Through URJ, Stratyfy offered an alternative: inherently interpretable AI-powered decisioning, so lenders can get more capital to underserved communities efficiently without losing sight of risk.
Stratyfy’s decisioning approach mimics human logic by applying weightings to each determining factor within a decision – meaning each decision is calculated with a more nuanced, precise view of risk. Unlike black-box AI, Stratyfy demonstrates exactly how a decision is made, ensuring every credit decision can be fully traced, audited, and explained down to individual feature weights.
During the URJ program, Stratyfy led participating lenders through a 3-step process:
- Analyze each institution’s loan data to provide tailored recommendations
By analyzing each lender’s data, Stratyfy helped them identify individualized goals for improving their underwriting efficiency, equity, and reach. By leveraging cohort-level insights, Stratyfy also filled in gaps where historical data was incomplete or insufficient. - Create custom credit models
After establishing tailored goals, Stratyfy used its data to train bespoke credit risk models for select participants. This individualized approach allowed lenders to update their existing criteria and expand access to target demographics while adhering to their specific risk tolerances. - Monitor performance for continuous improvement
Stratyfy monitored model performance and outcomes to measure the direct impact on their loan portfolios and communities served.
Results: Faster Decisions, Higher Approvals, New Products
Approvals Up 18% Without Manual Review Bottlenecks
Consumer lender BetterFi used Stratyfy to identify the attributes most predictive of borrower performance in its portfolio, then used that insight to fast-track underwriting for low-risk applicants. The result: a 18% increase in approvals in six months, plus an efficiency gain that supported BetterFi’s expansion into new, more diverse urban markets.
Loan Processing Time Cut by Two-Thirds
Small business lender Working Solutions CDFI used Stratyfy to launch an automated “Fast Track Loan Decision” for microloans, cutting processing time from 6 hours to 2 hours. That freed up underwriting staff to focus their time where it mattered most: coaching borrowers through complex applications, providing technical assistance to borrowers who might otherwise be denied, and building the relationships that define community banking.
New Products, Launched with Confidence
With a more precise view of risk, one mortgage lender safely reduced minimum credit score and loan-to-value requirements for its first-time homebuyer product, and rolled out a new 0% APR down-payment assistance loan.
Better Data, Better Strategy
For Washington Area Community Investment Fund (WACIF), Stratyfy surfaced a quantifiable link between borrower technical assistance and long-term repayment performance. That insight directly shaped WACIF’s new Resilient Growth Fund, turning a data analysis into a concrete growth and retention strategy for equitable small business banking.
A Template for Scalable Growth
The URJ pilot program demonstrates that when lenders pair mission with advanced data analytics, they can better serve their communities and grow responsibly.
By investing in advanced analytics and AI-powered decisioning, community banks and mission-driven lenders can:
- Boost efficiency, build relationships: Automate the routine decisions so staff time goes where it’s needed most: relationship-building.
- Approve more, safely: Get a holistic view of risk to say yes to more applicants confidently.
- Launch with confidence: Use expert-driven rules plus data insights to build and defend new products and criteria.
- Stay resilient: Maintain consistent lending performance even through economic and regulatory shifts.
At Stratyfy, we build technology that helps lenders do more with the data they already have: faster decisions, higher approval rates, and sustainable growth into new markets, all without compromising portfolio health.
Read the Urban Institute‘s full report on URJ here.
Learn more about how Stratyfy’s interpretable AI decisioning helps mission-driven lenders boost growth, efficiency, and impact. Connect with our team.