Millions of Credit Files Contain Errors. Revelar Finds Them.
Alternative data credit bureau capabilities are reshaping what lenders expect from their data partners. Traditional credit scoring was built on the assumption that the underlying data is accurate. It often is not.
1 in 5 Americans has a material error on their credit report. 69% of all reported errors originate in minority communities. These errors suppress scores, limit credit access, and create downstream FCRA compliance exposure for everyone in the data chain, including bureaus.
Revelar gives credit bureaus a machine learning solution built specifically to detect those errors at scale. As alternative data credit bureau capabilities continue to advance, Revelar stays ahead by scanning existing credit files, identifying suppressed credit signals, and delivering the kind of data accuracy that benefits consumers, lenders, and the bureaus providing the underlying intelligence.
How Alternative Data Credit Bureau Intelligence Drives Better Outcomes
- Strengthen scoring accuracy: Revelar surfaces errors that traditional models miss, giving bureaus a more defensible and accurate picture of consumer creditworthiness.
- Expand credit access responsibly: Corrected errors can increase credit scores by 80 points or more, opening lending opportunities for previously overlooked consumers without adding risk.
- Reduce dispute volume and FCRA friction: Catching errors before they generate disputes reduces the compliance burden on bureaus and furnishers alike.
- Deploy with zero data movement: Revelar runs in a secure, cloud-agnostic Docker container. No data leaves the environment, which means full compliance posture from day one.
- Add differentiated intelligence to your data products: Bureaus that can identify and correct errors at scale have a competitive data product that lenders need.
Alternative Data Credit Bureau Results Proven at Scale
Best Egg, a leading fintech lender, used Revelar to scan 20 million historical credit reports in under three minutes. They discovered 3.4 million consumers with material credit report errors. These were errors suppressing legitimate credit access and blocking loan approvals.
The discovery triggered a board-level decision to deploy Revelar directly into their Financial Health platform. Within six months, engagement rose 160%, dispute activation increased 62%, and they unlocked a nine-figure net-new lending opportunity from their existing database.
For credit bureaus, this result is a direct signal. Error detection at scale is not just a compliance story. It is a revenue story for every lender using bureau data to make decisions.
Built on Nearly Two Decades of Dispute Intelligence
Revelar is trained on more than 30 million dispute outcomes across Equifax, Experian, and TransUnion. It does not guess at errors. It recognizes the specific patterns that real credit disputes have validated over nearly 20 years.
That depth of training makes Revelar the most accurate alternative data credit bureau detection engine available, and the most FCRA-aligned, since it is built entirely on regulated, bureau-sourced data.
Credit bureaus that want to deliver higher-quality data products to lenders, reduce dispute friction, and demonstrate a measurable commitment to fair credit access have a direct path to all three with Revelar.
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