Research Based
on Real Lending Data
RiskSeal analyzed 6.1 million real loan applications from seven lenders to determine whether digital scores can reliably predict default risk.
The results show a clear pattern: as digital scores rise, default rates fall—even when applicants have limited or no traditional credit history.
loan applications analyzed
lending institutions
decline in default rates
digital and bureau data combined
Explore The Findings
Behind The Numbers
AUC 0.73 demonstrates the predictive value of combining digital and bureau data.
The full report goes further—showing how the result was obtained, how risk changes across score bands, and how lenders can apply the findings to real credit decisions.
Understand the Methodology
See how 6.1 million applications from seven lenders were analyzed, how default was defined, and how the model was independently tested.
Explore Every Score Band
Compare applicant distribution and default rates across ten score ranges—not just one headline performance metric.
Turn Scores Into Decisions
Learn how different score bands can support fast-track approvals, additional verification, manual review, monitoring, and credit-limit decisions.
Evaluate the Findings Responsibly
Understand the study’s limitations, portfolio differences, and the recommended steps for validating digital scoring on your own lending data.
Get the complete analysis, score-band results, and implementation recommendations.
