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68 borrowers about who is servicing their loan. e source of friction is a lack of standard processes for exchanging MSRs. Documents and data are being received from sellers in so many different ways that buyers can't keep up with the incongruities. For example, document images are classified using different taxonomies, stacking orders aren't consistent, and image quality varies, so no two loans are alike in terms of structure or legibility. Buyers receive loan packages from different sellers that look completely different from each other. All of this means buyers must manually go through every loan and restack documents in their preferred order and determine which things are missing from individual loan files. ere are also nuances that cause data discrepancies in the loan files that lead to extra costs. ese can slip by when work is done by staff with insufficient expertise to identify these issues. As an example, for a loan with co-borrowers, the fact that a credit report exists isn't enough. Is that credit report joint or single? Do you have a loan app for each borrower? For any loan, is hyphenated content consistent throughout the loan file? Fixing these inconsistencies across documents and data files using manual procedures is rife with human error. Rules- driven technology can capture these nuances much more efficiently. As opposed to reviewing all loan files to find the few loans that have discrepancies, staff can be focused on clearing conditions for only those loans that have issues. e lack of standardization in MSR trading also limits business opportunities. Banks, lenders, and investors that buy MSRs want to conduct trades quickly and go back to market and do more—but every time they buy loans, they get a big blob of due diligence and document processing work on their hands, which means they can only do so many trades. Banks are particularly hamstrung, as they have special compliance burdens that make loan scrutiny more costly and time-consuming. ere are MSR trading platforms that make negotiating the deal easier, of course. But the delivery of the loans hasn't been substantially improved on in years. Sellers are challenged in meeting buyer requirements, which means buyers must still process documents and address discrepancies through a combination of manual work and piecemeal automation that is still not robust enough. Recently, however, a leap forward has taken place in the area of MSR transfers that could remove friction for all parties, including the consumer. TAKING THE LEAP Over the past couple of years, some pieces have fallen into place that will help streamline MSR trades. e Industry Loan Application Dataset (ILAD), a loan application data "superset" based on MISMO v3.4, may make it easier for buyers and sellers to exchange loan information digitally. Until recently, however, the biggest piece of the puzzle—the development of technology that can normalize and standardize both MSR document and data sets regardless of loan origination date—has been missing. Now that piece has arrived. By applying automated, repeatable processes, new technologies are streamlining MSR transactions by normalizing document naming and stacking, regardless of individual loan seller procedures or protocols. Technologies today can extract data from hundreds of data fields on dozens of the most common loan documents, enabling buyers and sellers to identify and resolve data inconsistencies more quickly. e key to these technologies is machine learning-based automated document recognition (ADR) technology and automated data extraction (ADE) tools. Together, they can perform multiple automated tasks and power automated rules for any type of document check, document-to-document comparison, and document-to-data comparison. Configurations can then be used to define a buyer-specific naming convention and stacking order for onboarding into a servicing system. is is significantly faster than sending large files to an overseas partner and waiting days for the files to come back. Essentially, these technologies consolidate the many manual steps in MSR transfers into highly automated steps, which are both configurable and capable of being universally applied to all loan files. ey are also much more powerful than standard optical character recognition (OCR) tools. For example, on loans involving two borrowers, most current OCR Feature By: Dave Parker Banks, lenders, and investors that buy MSRs want to conduct trades quickly and go back to market and do more—but every time they buy loans, they get a big blob of due diligence and document processing work on their hands, which means they can only do so many trades.