Credit Card Provider Reduced Delinquency by 29.2%

Challenge

The provider’s internal model lacked visibility into applicants with limited credit or delinquency history. With few signals to rely on, risk assessments leaned conservative, slowing portfolio growth while delinquency remained high at 9.86%. To overcome this, the provider needed a way to expand approvals without adding risk—particularly among students and new-to-credit segments.

Approach

Pave partnered closely with the provider’s risk and data teams to establish clear thresholds, user segments, and evaluation criteria tailored for thin-file applicants. By integrating Pave’s Credit Card Score and Cashflow-driven Attributes into the provider’s framework, the team added meaningful visibility where credit history was limited. Trained on billions of transactions and cross-provider performance data, Pave’s models surfaced signals their internal models could not capture.

To validate the predictive power of Pave’s models, the teams backtested the Credit Card Score and Attributes using historical bank transaction, underwriting, and collections data, benchmarking results against the provider’s internal model. In parallel, they developed decline reasons mapped directly to the score. This ensured transparency and compliance while equipping internal teams with clear explanations and giving declined applicants actionable feedback to improve future eligibility.

Results

Working with Pave, the provider identified score thresholds that maximized safe approvals while meaningfully reducing risk. 

By declining only the bottom 30% of applicants by score, approvals held steady at ~70% while delinquency dropped by 29.2%—from 9.86% to 6.98%.

In addition, the provider introduced clear, data-backed decline reasons tied to Pave’s attributes. This gave internal teams greater visibility into decisions and provided declined applicants with actionable guidance to improve future eligibility.

Together, these changes allowed the provider to segment risk with more precision, approve more qualified applicants, and achieve sustainable portfolio growth powered by insights beyond traditional credit data.

Conclusion

The provider successfully maintained high approval rates of 70% while reducing delinquency by 29.2%. By delivering transparent, data-backed rejection reasons and improving decisions for thin-file applicants, they unlocked new opportunities for safe growth. These results validated Pave’s Credit Card Score as a powerful and explainable complement to their existing risk strategies.

Teams can leverage Pave’s Credit Card Score in their proprietary models to increase approvals without increasing risk. Read more about our Cashflow Scores or book a demo.