New Freddie Mac Capability Helps Lenders Calculate Borrower Income More Quickly and Precisely

New Freddie Mac Capability Helps Lenders Calculate Borrower Income More Quickly and Precisely

Written By: Joel Palmer, Op-Ed Writer

Freddie Mac launched an enhancement to its automated income assessment tool that enables lenders to use borrowers’ direct deposit digital paystub data to assess their income.

This capability is available through Freddie Mac’s Loan Product Advisor® (LPASM) asset and income modeler (AIM).

Freddie Mac said using digital paystub data can help lenders calculate income faster and more precisely to improve loan quality, simplify the mortgage process and expand access to credit.

“Over the last year, we’ve consistently rolled out innovations to ensure our digital tools are improving speed and efficiency, reducing risk and, ultimately, helping us serve our mission by reaching more qualified borrowers,” said Kevin Kauffman, Single-Family Vice President of Seller Engagement at Freddie Mac. “Today’s innovation further automates income assessment by using historical direct deposit pay patterns and current gross income from recent paystubs, which can help more families achieve homeownership.”

Last week’s announcement continues an effort by Freddie Mac and Fannie Mae to leverage tools to make the application and underwriting process more automated.

Freddie released a study earlier this showing that loans using its automated offerings are four times less likely to produce defects than those that don’t use technology. Freddie said process automation is especially beneficial for documenting income, both in the collection and assessment process because income verification issues account for nearly one-third of all purchase transaction defects.

Freddie’s study also found that lenders who use automated offerings at high rates:

  • Show 40 percent fewer loan defects than lenders with a lower usage of technology offerings.

  • Originated loans that, on average, were about 14 percent less costly per loan than lenders with a lower usage of technology offerings.

  • Have a seven-day shorter loan production cycle time, an improvement of 18 percent compared to lenders with a lower usage of technology offerings.

Freddie’s analysis also suggests that loans leveraging digital tools such as AIM perform better over time and show lower delinquency rates than loans that do not use technology offerings.

In addition to direct deposit data, AIM can assess income from tax return data for self-employed borrowers as well as bank account data to identify a history of positive monthly cash flow activity. This can include data from checking, savings and investment accounts, including those used for direct deposit of income and monthly bill payments, such as rent, utilities and auto loans.

Freddie said this data can help first-time homebuyers and borrowers in underserved communities who may not qualify with traditional methods of underwriting. Additionally, account data submitted to assess cash flow can only positively affect a borrower's credit assessment. And to help identify opportunities, LPA will notify lenders when submitting this account data could benefit a borrower.

The new AIM capability will be available to Freddie Mac-approved sellers using Loan Product Advisor beginning June 7. Finicity, a Mastercard Company, is the initial service provider supporting Freddie Mac's AIM for income using direct deposits plus paystub.


About the Author

As an NAMP® Opinion Editorial Contributor, Joel Palmer is a freelance writer who spent 10 years as a business and financial reporter and another 10 years in marketing for the insurance and financial services industries. He regularly writes about the mortgage industry, as well as residential and commercial real estate, investments, and retirement income planning. He has also ghostwritten books on starting a business, marketing, and retirement income planning.


Opinion-Editorial (Op-Ed) Disclaimer For NAMP® Library Articles: The views and opinions expressed in the NAMP® Library articles are those of the authors and do not necessarily reflect any official NAMP® policy or position. Examples of analysis performed within this article are only examples. They should not be utilized in real-world application as they are based only on very limited and dated open source information. Assumptions made within the analysis are not reflective of the position of NAMP®. Nothing contained in this article should be considered legal advice.