Loan Document Automation

The mortgage lending industry presents a number of unique challenges for classifying and extracting data from key documents, due in part to the large volumes of disparate documents in most loan files.

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New documents and the regulations related to them put a new emphasis on the need for quick and very accurate data. Lenders in particular face significant penalties for inaccurate data and missed delivery deadlines. Sorting and capturing critical data from thousands of diverse documents has historically been labor intensive, slow, and expensive. To stay competitive, and meet these new and constantly changing challenges, automation through technology is no longer optional.

The key is finding a provider that specializes in automated document classification and data capture specifically for mortgage lending and the financial services industries, which scales to process millions of pages per day.

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Leading edge OCR solutions offer significant efficiencies for classifying large quantities of differing document types and extracting key data elements from those documents.  In the mortgage market, these capabilities allow for quick and accurate identification of over 500 unique documents in the typical mortgage file, along with the ability to capture nearly any data element from those documents that an organization requires.

Here are some examples of applying this advanced technology to specific mortgage documents:

Application Processing

Extract relevant content from borrower-provided pay stubs, W-2s, bank statements, and tax documents to expedite underwriting and reduce origination costs.

Post-Close Processing

Identification of each document in the loan file, bringing structure to what was a 300+ page blob of content.

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Verification that relevant documents have been signed

Compare key data elements from loan file with your systems of record to verify changes haven’t been made without your knowledge.

UCD File Generation

Create the Uniform Closing Dataset (“UCD”) file required (as of Sept 25, 2017) when selling loans to Fannie Mae and Freddie Mac

Reporting And Audit Automation

Extract key loan file data elements to support the following reporting/audit activities:

HMDA reporting – our system is ready to capture the additional demographic data on the new Uniform Residential Loan Application (effective Jan 1, 2018)

RESPA audit

TRID audit

Lenders can longer afford to manually classify and manage large volumes of disparate documents. Manually preparing a batch for scanning by inserting document separator sheets and manually classifying loan documents is a labor-intensive, inefficient and error prone process. Not only is it critical that this process be done accurately, but also that it be done efficiently in order to allow downstream underwriting and servicing decisions to be performed in a timely way.

At the end of the day it is about finding a provider that focuses its skills towards delivering the most efficient, accurate, and flexible freeform document classification and data extraction solution available. The time is now for lenders to reduces manual labor costs and increases accuracy levels associated with classifying and capturing data from loan documents.

About The Author

Mark Tinkham

Mark Tinkham is Director of Business Alliances at Paradatec, Inc. Over the past twenty-five plus years, Mark has worked for technology companies that deliver innovative solutions to the financial services industry. For the past ten years, his primary focus has been bringing efficiencies to the mortgage market through industry leading Optical Character Recognition (OCR).