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» VISIT US ONLINE @ DSNEWS.COM 65 REALIZING THE POTENTIAL Properly leveraged, AI and machine learning also have the ability to help lenders and servicers take full advantage of digital mortgages, which has the added benefit of improving the customer experience and lower loan costs. By leveraging AI and machine learning, however, lenders are much better able to determine the accuracy, quality, and completeness of a borrower's information as it is being collected, and without having to continually ask the borrower for "one more thing." When it comes to loan audits, AI and machine learning can reduce and even remove traditional manual tasks involved with "checking the checker" and greatly improve the accuracy and throughput of data and documents. By means of creating purified data, automation can perform a significant number of audit tasks, focusing auditors on exceptions and significantly increase the number of daily audits lenders are able to perform. AI and machine learning can also be applied during the default stage to help gather, monitor, and verify data to produce an accurate loan decision or the best possible loss mitigation path. ey can also be used by specialized default servicers to improve loan file management and transfer loans smoothly and accurately, with less manual effort. is would place servicers with these tools at a distinct advantage—especially with first mortgage defaults starting to tick higher. AI and machine learning are both initially based on human instruction. erefore, both are susceptible to programming flaws and human bias. Continuous statistical analysis of results can help to fine-tune these technologies. is is a critical step that can help address challenges to the industry and to software developers, which, if not addressed, could lead to fair-lending issues and greater risk. Fortunately, these new tools are highly scalable and can integrate larger data sets for more rigorous decisioning. A current example is the new Closing Disclosure, which alone contains more than 1,000 data elements. By leveraging AI and machine learning, more of this information can be used to inform decision models, providing lenders both flexibility and greater confidence. As AI and machine learning are applied more broadly to the processes and segments of the industry, new sources of data and needs for validation will increase. For example, as non-QM lending grows, the ability to leverage these technologies to ensure compliance with independent investor guidelines is paramount. e potential benefits from AI and machine learning are seemingly endless. When lenders move away from traditional "stare and compare" practices, checklists, and Excel spreadsheets and toward AI and machine learning, they will begin realizing significant right-sizing and reallocation of staff, improvement in efficiency, and cost reduction. For example, machine learning can also lead to huge reductions in error rates for certain processes, such as Home Mortgage Disclosure Act reviews, and AI can generate huge increases in loans per-day, per- person when originating a loan. AI and machine learning may also drive higher demand for new skills that can provide instruction and guidance for new applications. In addition, staffing on the loan production side can become more focused on control and review versus manual redundant tasks. is will help to reshape investments in training and drive faster output without increasing staff, leading to faster closings—and better overall consumer experience. AI and machine learning applied throughout the origination and servicing process can enable lenders and loan servicers to better assemble, monitor, and verify data at every stage, through closing and throughout the entire loan life cycle. Essentially, this results in data purity, which can then be used to render an accurate loan decision or loss mitigation path based on the best available data. At the end of the day, lenders and servicers are only scratching the surface of what can be done with AI and machine learning. In the next several years we will see more and more innovations being used within loan manufacturing and default servicing. And since they have the potential to greatly improve loan quality and reduce costs—challenges our industry has struggled with for the past decade—they paint a bright future indeed. AI and machine learning can also be applied during the default stage to help gather, monitor, and verify data to produce an accurate loan decision or the best possible loss mitigation path. They can also be used by specialized default servicers to improve loan file management and transfer loans smoothly and accurately, with less manual effort.