Credit & Risk

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

For lenders, mitigating first payment default requires data, advanced analytics, customer engagement, and agile risk management.

To better understand a consumer's credit behavior over time, financial institutions must leverage trended data.

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.

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

Using alternative data for credit underwriting is a modern and efficent approach to a risk-based credit approval strategy Read more!

The future of credit underwriting will depend on advanced analytics that can draw conclusions from vast amounts of data.

With automated income verification, lenders can approve more applicants quickly and provide exceptional digital experiences. Learn more!

Dive deeper into model risk management, its importance for organizations, and the key elements of a model risk management framework.

From initial screenings and data entry to determining a final decision or credit limit, loan origination automation provides many benefits.

Automate your credit limit management process to better serve your customers and quickly respond to the volatile market.

Being able to explain how an ML model works and what drives its decisions is important if you want to use ML-powered models for underwriting.

Learn how expanded data, AI-driven models, and increased automation can help you enhance your credit risk management strategies.

Learn how a well-designed underwriting strategy can help you drive growth and create more value out of your member relationships.