Credit union delinquency management

This summary outlines important research findings that help lenders drive a more inclusive environment for customers, while mitigating risk.

A few takeaways:

• Understanding the profiles of who’s more prone to exclusion informs risk management.
• Machine learning techniques can also be deployed to help detect economic interaction effects or shifts.
• Attributes like education level, occupation or rent/utilities payment history have predictive power in credit outcomes.

Read the latest findings about "Closing the credit gap" white paper, and what they mean for financial inclusion and risk management.

Key insights:

• Income and credit trends reveal significant disparities that impact access to financial products.
• Demographic shifts across gender, household structures, ethnicities, and generations are reshaping borrower profiles.
• Lenders who adapt their strategies and product offerings can expand their customer base, reduce default rates, and lead in inclusive finance.

Every lending decision you make relies on data, but what if that data only tells part of the story? Download our use case to meet two borrowers, Claudia and John, and discover how alternative data can help you:

• Reveal hidden financial stability in members.
• Identify early warning signs of risk.
• Enable more accurate and inclusive lending decisions.
• Drive stronger member relationships.

Credit unions exist to serve all members of their communities. Yet millions of financially responsible individuals remain invisible to conventional scoring. Download our alternative data guide to discover:

• The strategic advantages of alternative credit data 
• The future of inclusive lending
• Why you should partner with us