Models & Scores

Our most recent AI innovation, Experian Assistant, is redefining how financial organizations improve productivity with data-driven insights.

How can lenders ensure they’re making the most accurate and fair lending decisions? The answer lies in consistent model validations.

Prescreen targeting solutions have evolved significantly, offering a competitive edge through more precise and impactful outreach strategies.

Here’s a snippet from our open banking webinar’s Q&A session with Ashley Knight, Senior Vice President of Product Management.

Reject inferencing techniques unlock a more comprehensive view of your applicant pool for more informed underwriting decisions. 

Learn how leveraging credit attributes can help you reduce your risk exposure, prioritize accounts, and modify your pre-collections strategy.

Ensuring fair lending practices while leveraging machine learning models is crucial for organizations committed to ethical and compliant operations.

Mortgage lenders who connect with Gen Y and Z where they are will be better positioned to serve this demographic and grow their business.

When equipped with the right data and strategies, mortgage lenders can drive growth by identifying and engaging first-time homebuyers.

Optimization modeling provides actionable insights that drive decisioning, allowing businesses to achieve their marketing and growth goals.

Lenders who use AI-driven credit risk decisioning can help improve outcomes for borrowers and increase financial inclusion.

Credit risk analytics can help financial institutions quantify the risk that a borrower won't repay a loan as agreed.

Data-driven marketing insights can help your organization target more accurately and create a better customer experience.

New approaches to model operations are also helping lenders accelerate their machine learning model development processes.

In this blog post, we explore the empowering impact of income and employment verification on financial institutions.