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Issue link: http://digital.themreport.com/i/1315471
34 | M R EP O RT FEATURE fication known as the Industry Loan Application Dataset (iLAD), which incorporates ULAD, the automated underwriting re- quirements for both GSEs, and importantly, additional details needed by the industry that either were in the old 3.2 file or are items that are required to support other needs for the collection and conveyance of the application amongst trading partners. Improving Data Quality O verall, the use of standardized data for interacting with the GSEs has an overall effect of im- proving data quality industrywide, a phenomenon that evolves as each new data specification comes online. The base of each of these requirements as issued by the GSEs are XML message specifications that provide strict instructions on what data is to be provided, how it is to be formatted, and what is required or optional. This type of specification then is implemented by software providers to convey the datasets through electronic integration for conveyance from seller/servicers (or their agents) to the GSEs' respective portals. Merging data collection and presentation at the start of the loan process will also make it easier for lenders to use more detailed information for determin - ing borrower eligibility, which will speed up originations—a key benefit at a time when lenders are struggling with capacity. It also enhances back end processes as well. Gathering all key data digi - tally in a standardized way opens up new possibilities for automa- tion to shorten days to originate and get to closing much faster. Standardization has already led and will continue to lead to the rise of new services and technolo- gies that can capitalize on defined data and offer new and improved capabilities. Ultimately, this makes the quest for the elusive digital mortgage closer to a reality, and not just a futuristic pipe dream. I suspect that by 2021, kick-started by 2020 technology adoption trends, we will see new platforms that enable even greater automa- tion in mortgage origination, servicing and loan commerce. For example, there are some MISMO exploratory commit- tees that are considering reviving the work started on the now- abandoned Uniform Mortgage Servicing Dataset (UMSD). At minimum this would mean moving the UMSD up to at least the MISMO 3.4 release for congruency to ULAD. Reviving an industry standard for mortgage servicing would benefit both new loan setup on servicing platforms as well as smoothing servicing transfers. Simultaneously, there are discussions taking place on for - mulating a standard for evaluating mortgage servicing rights (MSRs). No doubt, the industry will continue to look for ways to move the benefits of data quality forward from the origination channel. The advent of the GSE Uniform Security Instruments and FHFA's Uniform Mortgage- Backed Security Rules are other examples of standardization that are intended to facilitate faster delivery of mortgage loans into the secondary market. All this being said, our industry is constantly evolving to new ways of doing business. Making such a significant change in the application process will be a chal- lenge for many as they train their staff and ready their systems to support the new requirements. The key, however, is not to look at the URLA as an obstacle, but as an opportunity that is truly unparalleled in recent his- tory. Thanks to ongoing, comple- mentary data standardization initiatives, the new application will undoubtedly lead to a more efficient origination process, lower loan processing costs, higher qual- ity loans, and a smoother, more enjoyable consumer experience. Ultimately, clean, reliable, machine-ready data made possible by the adoption of data standards will be the lifeblood of technology improvements in the mortgage industry, as well as advancements in big data analytics and artificial intelligence aimed at improving profits and reducing risk. Clean data is indeed our industry's own rising tide that will lift all boats, setting them up for a journey that promises even more exciting in- novations to come. . Elizabeth Green is the Chief Data and Product Solutions Officer at LoanLogics, where she is responsible for the development of the company's solutions and data strategy as well as expansion of its products, including in the area of collateral risk assessment. Green is a strategist, solutions architect, speaker, and valuation advocate. As a recognized mortgage technology veteran in software product leadership for solutions in residential property valuation, loan origination, mortgage servicing, and secondary marketing, she has helped to foster a new level of understanding in credit and collateral risk assessment through the application of digital intelligence. While the new application still requires certain key information to be provided, it's also a dynamic form, meaning it expands to account for additional borrowers and data needed for the case at hand.