Increasing efficiency and funding rates with automated decisioning

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.

Improve your credit risk decisions with advanced approaches to validating and optimizing decision trees. Learn how stronger models drive smarter, more consistent outcomes across your portfolio.

• Enhance model accuracy
• Strengthen underwriting and collections
• Improve decision speed and consistency
• Align data science and risk teams

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.