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Vendor Releases Innovative Web Services API

Paradatec, Inc., developer of an Optical Character Recognition (OCR) solution for mortgage file processing, has released their web services API for real-time integration to their clients’ line-of-business applications. This new functionality can seamlessly transfer documents from the loan origination system (LOS) to the Paradatec solution for page classification and data extraction, with the Paradatec-produced results transferred back to the LOS in place of manual data entry.

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“As application integration becomes tighter in response to the ongoing compression of service level timeframes, Paradatec’s new web services API stands ready to serve as the OCR extension to our clients’ line-of-business applications. Our first solution to leverage this capability is our new WriteUCD module, in which the final Closing Disclosure (CD) is submitted to us through the web services API, our OCR functionality extracts the relevant data from the CD, and WriteUCD then produces the corresponding Uniform Closing Dataset (UCD) file required by Fannie Mae and Freddie Mac” said Neil Fraser, Paradatec, Inc.’s Director of US Operations.

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“This new functionality allows for seamless OCR processing scaling from small document sets like borrower-provided paystubs and W-2s up to full loan files. With the ability to integrate tightly with any other web service-enabled application, we’re helping our clients create a very rich and efficient application ecosystem.”

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Paradatec’s 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 the quick and accurate identification of over 500 unique documents in the typical mortgage file, along with capturing nearly any data element from those documents that an organization requires.

About The Author

Tony Garritano
Tony Garritano is chairman and founder at PROGRESS in Lending Association. As a speaker Tony has worked hard to inform executives about how technology should be a tool used to further business objectives. For over 10 years he has worked as a journalist, researcher and speaker in the mortgage technology space. Starting this association was the next step for someone like Tony, who has dedicated his career to providing mortgage executives with the information needed to make informed technology decisions. He can be reached via e-mail at tony@progressinlending.com.

OCR For Mortgage In Action

The financial services industry is challenged with managing large volumes of documents with varying layouts containing immense amounts of data – part of which is highly critical with regards to compliance. The traditional manual process for classifying and keying data from these documents is time consuming, error prone, and costly due to the sheer volume and complexity of the mortgage documents. In an industry where standardizing forms is not always possible due to their varying systems and points of origination, an acceptable automation solution must be able to properly and compliantly handle this variability.

Client:

Top-Five Originator. This bank is one of the largest in the United States. It is a leading lender offering a range of quality home loans, including government and conventional. These loans are provided through multiple channels.

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Challenge

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

>>A typical incoming mortgage loan file may contain 250 to 600+ pages of various size documents, comprising more than 250 potential document types. Older loans files may grow to well over 1000 pages.

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>>Manually sorting each set of loan documents is a labor intensive and error prone effort, typically requiring the addition of document separator pages if the file is to be scanned.

>>Due to the sheer labor effort required, the typical level of detailed document sorting possible with a manual approach is very “coarse”. In other words, only the most critical documents and document groups are classified rather than attempting to identify all specific document types. An example of this limitation might be a manual grouping of a series of specific documents into a “Credit Documents Group” rather than breaking these out specifically by document types such as bank statements, credit reports, and brokerage statements.

>>To compete in this extremely competitive market segment, organizations are looking for ways to reduce costs and streamline their processes.

In addition to the challenges described above, this top five originator was looking for a solution to help automate the laborious task of providing data for a number of audit-centric applications. These ad-hoc projects commonly had tight timelines and included wide ranges of loans, and millions of pages to be audited.

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Project Description

At the start of the Project, this top five originator had a sophisticated document capture infrastructure feeding a well-known enterprise content management system in place. What was missing from this infrastructure was an advanced recognition module that could deal with the document variations expected in an organization serving borrowers across the nation.

The ideal solution needed to provide a seamless interface to this current capture infrastructure. This would greatly simplify the implementation by allowing the existing interfaces to both front-end scanning and back-end image storage to be largely unaffected by the addition of the recognition technology.

Prior to the installation of the new recognition components, a large team would manually classify incoming documents into a moderately broad range of categories or Document Groups. Once these documents had reached the enterprise content management system, a team of underwriters would review, manually enter data, and process the loan.

Limitations of this approach included:

>>Heavy reliance on the skills of the people manually classifying documents and extracting data. Error rates varied from operator to operator. Thus, a loss of a skilled operator for any reason had a negative impact.

>>Time is of the essence in any mortgage-processing environment. Using a human-centric approach meant that processing times were proportional to staff availability at any given moment.

>>People tend to be more expensive than computers and software.

>>Regulatory bodies as well as this originator would have preferred a greater granularity in the way documents were classified. However, this need was outweighed by the complexity and difficulty presented when attempting to teach and maintain a group of individuals in how to classify documents among over 250 possible choices.

The new extraction system was selected after an exhaustive evaluation process. A competing solution was initially tried. However, after months of tests, it was determined that a more advanced solution was available which had a number of capabilities that surpassed other solutions previously tested or reviewed:

>>This new solution was by far the fastest technology available to read OCR mortgage documents. Pre-production technical due diligence empirically showed a system that was capable of processing approximately 1 million images per day on a single twelve-core server.

>>This solution was able to use one set of rules to process and recognize all document variations. Because of the extremely large number of documents (and variations of each), which this top five originator encounters, they required the flexibility offered by a non-template-based ADR (Automated Document Classification) and data extraction solution.

>>This solution offered pre-built mortgage logic, which “understands” the vast majority of the document types and variations that were required to be recognized. This solution allowed this originator to rapidly implement an ADR and data extraction solution for their specific needs.

The initial focus was to implement an ADR solution that supported more than 250 different document types and potentially hundreds of variations of each document type. The vast majority of the pages in a loan are now identified automatically with no human intervention. The remaining exceptions are presented to operators who either accept the first choice page type or choose an alternative.

This system is able to narrow down the page types that are lexically possible based on the text on the page. Because of this, in most cases, the operator can choose from a list of no more than five alternate page types. This reduces errors and review time in the verification process.

Upon production implementation of the ADR solution, the focus shifted to automatic data extraction. A list of more than 1500 fields was identified for the first implementation phase of data extraction. Both this project and the ADR work that preceded it were initially implemented in one of the originator’s major channels in order to ensure a wide variety of document sources and variations.

Today both of the projects described above are in full production. The amount of manual labor previously required for these tasks has been reduced significantly. Error rates are lower than the human processes that preceded implementation. The end to end processing time has been vastly reduced due to the fact that much of the human labor has now been replaced by lightning fast computer CPU cycles. Additionally, this top five originator has implemented sophisticated downstream mortgage lending business rules to take advantage of the valuable data generated by the new system.

This top five originator, like any other mortgage lender, is subject to a variety of time-sensitive requests such as internal audits. These audits require that specific data be tabulated from each loan file and reported to the appropriate entity. In some cases, the volume of loans included in these audits can reach into the tens of thousands, with a very limited response timeframe. With the system now in production, it is possible for this organization to be more agile than in the past. New data fields can be configured and tested in a few hours and a million images can now be interrogated for salient data overnight.

Additional capabilities leveraged successfully at this customer include:

>>Verification provides a list of likely document types to further increase speed of verifying exceptions.

>>Ability to customize how documents are handled based on the type of process to be conducted (e.g. origination, servicing, audit, etc.).

>>Ability to quickly recognize additional document types using the automated learning facility.

>>Database lookups and business rule logic checks to ensure the highest degree of data accuracy.

>>No scripting interface, with easily configurable rules to modify customers’ highly sophisticated ADR and data extraction processes.

>>Ability to add processor cores (including new servers) to the environment in a matter of minutes to quickly scale and meet tight deadlines or increased staffing demands.

Outcome

The project was successfully implemented and released to production on time. As a result of this experience with both the Paradatec staff and the Paradatec solution, this customer is prepared to act as a reference on behalf of Paradatec. Prospective clients are encouraged to take advantage of this opportunity.

Paradatec is rapidly approaching the significant milestone of processing 300,000,000 pages annually for this client alone. As a company, Paradatec processes several billion pages per year.

Paradatec’s solution is an advanced and unique OCR recognition technology. It utilizes neural networks technology and artificial intelligence and is able to read structured, semi-structured, and unstructured documents. It then makes ‘decisions’ about document characteristics in much the same way as a human being does— only many times faster and without human intervention.

Paradatec takes a very different approach from other OCR forms processing technologies in that it is a truly template-free design, allowing the system to easily cope with the varying layouts of each document. In performance terms, Paradatec is capable of processing thousands of documents per hour with a single processor. It provides even further scalability by offering seamless support for the latest in multi-core processor technologies and multi-server configurations.

Per Neil Fraser, Director, of US Operations, “To be chosen by such a high-profile client for a project of this size was a vote of confidence for Paradatec and our leading edge technology. I would encourage other similarly placed clients to reach out to Paradatec to setup a ‘One-Day Blind Test Challenge’. In just a day it is possible to see what this technology can do, right out of the box.”

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).

Paradatec Named Verified UCD Producer By Freddie Mac

Paradatec, Inc., a provider of Optical Character Recognition (OCR) solutions for mortgage file processing, announced that it is a verified technology integration vendor for Freddie Mac’s Loan Closing Advisor platform. Paradatec’s WriteUCD module was developed in accordance with Freddie Mac’s requirements for producing valid Uniform Closing Dataset (UCD) files.

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The UCD is a common collection of data that mortgage lenders will be required to deliver digitally to Freddie Mac and Fannie Mae starting on Sept. 25, 2017. This requirement is part of the Uniform Mortgage Data Program (UMDP), an industry-wide drive to build a better housing finance system in the United States.

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The WriteUCD module leverages Paradatec’s advanced OCR solution for the mortgage market to extract data from closing disclosure (CD) documents in mere seconds per page and then format that data in the required format.

According to Neil Fraser, Paradatec’s Director of US Operations, “We’re pleased to have obtained Freddie Mac validation as our clients need the assurance that they can meet the GSEs’ requirements well in advance of the September deadline. If a lender’s current loan origination system partner or document provider is struggling to produce a valid UCD file, they can sleep soundly knowing that Paradatec has them covered with our new WriteUCD module.”

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Paradatec also announces the release of their new AuditUCD module for auditing UCD file content against the closing disclosure contained in the UCD file. Fraser continues, “Since we’re building the UCD file from extracted closing disclosure data, it’s just as easy for us to unpack a UCD file’s content to compare the individual data elements against the values extracted from the submitted CD to verify the integrity of both components in the UCD file. Any elements that don’t match will be flagged in our XML output for further review and resolution. Given the volume of content that will be produced and need verification with this UCD initiative, our solution is uniquely positioned to offer a high degree of automation and operator efficiency.”

About The Author

Tony Garritano
Tony Garritano is chairman and founder at PROGRESS in Lending Association. As a speaker Tony has worked hard to inform executives about how technology should be a tool used to further business objectives. For over 10 years he has worked as a journalist, researcher and speaker in the mortgage technology space. Starting this association was the next step for someone like Tony, who has dedicated his career to providing mortgage executives with the information needed to make informed technology decisions. He can be reached via e-mail at tony@progressinlending.com.

Paradatec Achieves Fannie Mae UCD Certification

Paradatec, Inc.’s new WriteUCD module was recently certified by Fannie Mae as meeting their requirements for producing valid Uniform Closing Dataset (UCD) files. This new module leverages Paradatec’s Optical Character Recognition (OCR) solution for the mortgage market to extract data from Closing Disclosure (CD) documents and then format that data in the MISMO standard required by Fannie Mae. The certification process required submitting test UCD files for multiple lending scenarios for Fannie Mae’s review, with all tests being passed successfully.

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According to Neil Fraser, Paradatec’s Director of US Operations, “Developing this new module was an easy decision for us. Many clients are already using our solution to extract data from the TRID documents to support certain internal review and audit procedures, so since we have the logic to extract this data it made sense to create a module which formats this data per the UCD specification. Now that we’ve attained certification with Fannie Mae, our clients who work with them are assured of complying with their September 2017 UCD requirement with this new WriteUCD module.”

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Paradatec also announces the release of their new AuditUCD module for auditing UCD file content against the Closing Disclosure contained in the UCD file. Fraser continues, “Again, developing this module was a logical extension to our mortgage solution. Since we’re able to build the UCD file from extracted Closing Disclosure data, it’s just as easy for us to unpack a UCD file’s content to compare the individual data elements against the values extracted from the submitted CD to verify the integrity of both components in the UCD file. Any elements that don’t match will be flagged in our XML output for further review and resolution. Given the volume of content that will be produced and need verification with this UCD initiative, our solution is uniquely positioned to offer a high degree of automation and operator efficiency.”

Featured Sponsors:

 
Paradatec’s 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.

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Lenders Look To Gain Efficiency

As the cost to originate increases, lenders need to be more efficient. One way to do that is to automate more within your LOS. For example, Visionet Systems Inc., a leading automated document classification, indexing and data extraction provider for the mortgage industry, has completed an integration with LOS provider LendingQB. The integration enhances operational efficiency by seamlessly automating document management within the mortgage lending workflow.

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Visionet Systems has over 20 years of experience in providing businesses with innovative solutions for electronic document processing and Business Process Outsourcing services. Visionet’s VisiLoanReview (VLR) Platform is a solution for mortgage lenders that eliminates the manual splitting and indexing of documents that impedes the loan process. “Lenders spend between 30 and 120 minutes combing through document packages on every loan file,” said Arshad Masood, CEO of Visionet. “Our technology and services allow lenders to offload a cumbersome and tedious process and improve the efficiency and scalability of their organization.”

The integration allows users of the LendingQB system to automatically push electronic documents to VLR for document processing, which splits document packages, indexes individual documents and extracts data elements according to the needs of the LendingQB client. Visionet provides a white glove service which handles all exception processing, including those involving handwritten documents and borrower supplied correspondence. Each document is then mapped to lender-specific naming conventions and automatically inserts the documents back into the LendingQB loan file. Visionet’s clients have reported efficiency gains of 30 percent or more using the VLR platform and services.

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“We are very pleased to add Visionet’s capabilities to our platform,” said Binh Dang, President of LendingQB. “Our two companies have a shared goal of helping mortgage lenders become leaner and more effective organizations. The integration of VLR is truly synergistic because it merges our core capabilities and transforms the way that lenders work.”

Visionet’s Masood agreed. “Visionet is continually enhancing our capabilities for mortgage lenders, and we welcome opportunities to partner with leading LOS providers like LendingQB.  We look forward to providing LendingQB’s clients with the proven process efficiencies provided by our unmatched technology and people.”

About The Author

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Tony Garritano
Tony Garritano is chairman and founder at PROGRESS in Lending Association. As a speaker Tony has worked hard to inform executives about how technology should be a tool used to further business objectives. For over 10 years he has worked as a journalist, researcher and speaker in the mortgage technology space. Starting this association was the next step for someone like Tony, who has dedicated his career to providing mortgage executives with the information needed to make informed technology decisions. He can be reached via e-mail at tony@progressinlending.com.