The Client: Franklin American Mortgage Company (FAMC) Franklin, TN – USA

Founded in 1993, Franklin American Mortgage Company (FAMC), a privately-held mortgage banking firm located in Franklin, Tennessee, is a full-service professional mortgage banker licensed to provide residential mortgages across the nation.
FAMC, which offers a host of diverse, flexible mortgage packages for customers with a variety of backgrounds and needs, is committed to helping families and individuals achieve the dream of home ownership through its three divisions: retail, wholesale and correspondent.

The client’s view
“We had attempted to use OCR in the past for Automated Document Recognition (ADR). Due to our prior experience and a variety of technical issues we were very skeptical about OCR. We asked a number of vendors including the Paradatec team to help us perform an extensive due diligence process which included a proof of concept test with our own documents. Paradatec was the clear winner based on our comprehensive vetting process.”
Michael Rhoden (SVP of IT, FAMC)

Challenge

The Mortgage lending industry presents a number of unique challenges for manually classifying and managing very large volumes of disparate documents which are ubiquitous within this industry.

Manually preparing a batch for scanning by inserting document separator sheets and manually classifying loan documents was a labor intensive process. Not only is it critical that this process be done accurately, but that it be done efficiently in order to allow downstream underwriting and servicing decisions to be performed in a timely way.

  • It is common for a single mortgage loan to be comprised of up to 500 pages and above of various size documents.
  • A mortgage loan may include over 275 different possible document types.
  • Manually sorting each set of loan documents can be a very labor intensive and error fraught effort.
  • When scanning loan documents, significant labor is required to simply establish the first and last pages of the multiple page documents. This is most often done using the costly process of inserting “document separator” sheets prior to scanning.
  • To compete in this extremely competitive business, organizations need to look at cutting costs and streamlining their processes.

Outcome

The project was completed and is currently in production. The system is able to achieve 80% document recognition while keeping error rates low. This has allowed FAMC to position itself for an anticipated future increase in incoming document volume and provides them with a powerful competitive advantage.

Today FAMC is processing over 1.5 Million images per month and doing all this more accurately and with less production time than was formerly required.

Further Information

You can download the complete case study here: