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Addressing Automation Needs in Mortgage

Many technology options are available today to automate mortgage document processing tasks. Some solutions are well marketed with great claims of mortgage ‘knowledge’ and an ability to provide tremendous results. In some cases, providers use offshore labor rather than technology, or some combination of the above. When conducting due diligence, understanding the differences in these approaches is key to supporting expansion and scalability requirements.

Three technology approaches help determine the right solution for automating mortgage processing documents:

1. Zonal OCR

The zonal Optical Character Recognition (OCR) approach looks in specific locations, or ‘zones’, on a page for relevant text. The benefits of this system include:

However, this process is administratively heavy, as variations in document layout require distinct zonal templates. Many times the relevant text is in a highly volatile location, making it difficult to find.

2. Full Page OCR

This approach makes a full-page OCR ‘read’ of every page of every document, much the same as a human being. Ideally, each page is read in less than one second and the content is processed through a set of rules to determine the document type of each page. While this may seem to be the obvious way to approach the task of indexing the diverse documents found in the mortgage industry, most technology providers are unable to deliver the speed necessary to successfully scale with this approach. The benefits of this approach include:

3. Visual Classification (a.k.a ‘Fingerprinting’)

This legacy approach has recently been remarketed for use in the mortgage industry. While it does have the advantage of sub-second speed, it is not an OCR solution. Instead, an image analysis (non-text based) approach is used to identify documents and page types.

Visual classification attempts to differentiate between document types A and B largely by examining the distribution of ink on samples of each known document. Similar to thumbprint analysis, the graphical signature of each document type is learned and remembered. It has the following advantages:

However, this system also has certain drawbacks, which include: