MReport January 2020

TheMReport — News and strategies for the evolving mortgage marketplace.

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M R EP O RT | 35 FEATURE "data-picking" tool. Even in the best of cases, when reading bor- rower documents, OCR is only able to capture a portion of data, which is why lenders continue to spend heavily on human "check- ers" to capture what OCR missed and validate information pulled from borrower documents. To be sure, OCR tools are valuable, but cannot get the job done alone. Where Capture 2.0 Comes into Play W hen combined with OCR technologies, machine- learning tools enable lenders to ac- curately identify and classify more loan documents and extract more data, more accurately, from them. That's because machine-learning tools can be "trained" to recognize patterns on both structured and unstructured documents. This blend of tools is generally referred to as Capture 2.0 technology, also referred to as "intelligent capture." A great example is gift letters. With no standard format, OCR alone is likely to fail. Yet every gift letter has the same informa- tion—somebody is giving someone else money. By using machine learning, trained algorithms, and leveraging a large enough document sample size, Capture 2.0 technologies can classify gift letter documents and pull data from them with as much as 95% accuracy. For the vast majority of mortgage participants, the differ- ence maker for Capture 2.0 will be partnering with a Regtech provider that has tested, scalable solutions that can be delivered in a cloud environment to ensure performance. Such providers should also have access to a document training set that is large enough for machine-learning tools to maximize the document types that can be classified and the data that can be effectively extracted. Grasping the Potential W ith Capture 2.0 technology, lenders are not only better able to capture and interpret greater amounts of data and route it to automated business processes, but also to evaluate loan quality and identify defects throughout the origination process, inclusive of due diligence between buyers and sellers of loan assets and ser- vicing rights. These processes are being streamlined because of the accuracy with which machine- learning tools can identify and classify documents and extract data with higher quality. Using more verified, validated data to power loan file reviews enables audit staff to perform additional audits in less time and refocus their efforts on manag- ing exceptions. In fact, I have personally witnessed loan auditors evaluating as many as 18 compli- ance reviews a day, compared to the industry average of about five or six. When leveraged appropriately, Capture 2.0 technology can also help lenders leverage larger sets of "purified" loan file data to analyze their origination practices and get a much better view of their lend- ing patterns. They can "slice and dice" large, high-quality datasets to gain new insights on their operations, implement process improvement plans and even root out possible issues related to their lending practices. Ultimately, this can help lenders find and fix any problems before they gain the interest of regulators. Capture 2.0 technology can also help lenders deal with what is perhaps their biggest challenge of all—controlling costs. As recently as Q1 2019, total loan production expenses were over $9,000, ac- cording to the Mortgage Bankers Association, which is the highest amount ever. For most lenders, staffing is a huge cost factor. By using Capture 2.0 technology, lenders can improve staff effi- ciency and streamline processes to stem the ever-increasing expense of manufacturing loans—while also creating a better borrower experience. The bottom line is that improv- ing loan quality and achieving efficient loan production should not be a mystery that takes un- necessary time and effort to solve. Today's machine-learning tools are already helping lenders compen- sate for the limitations of OCR technologies and reduce human intervention in many aspects of the mortgage production chain. At the same time, today's tools are improving lenders' ability to meet constantly changing regulatory and investment requirements. The potential of Capture 2.0 technology doesn't stop there. In the end, it will enable the auto- mation of all kinds of decisions throughout the mortgage lifecycle, including the point-of-sale stage and servicing. It will help bor- rowers as they are applying for loans, and help lenders streamline underwriting decisions and even find out which borrowers might be ready to refinance. Indeed, it may be a while before machine learning and Capture 2.0 technology becomes ubiquitous in our industry—but it is starting to happen. Today lend- ers are implementing these tools to slash labor-intensive tasks and reposition staff on more valuable work in which their expertise is truly needed. That's just too strong of a value proposition in the current lending environment to be ignored. In other words, it's no longer a matter of if Capture 2.0 technology develops into an industry norm, but when. . BRENDA B. CLEM, CMB, is Chief Product Strategist at LoanLogics. She is responsible for developing and implementing strategic plans for all LoanLogics products and maintaining strong relationships within the mortgage industry to understand the prioritization of market needs. With more than 30 years' experience in residential mortgage operations, her expertise includes secondary marketing, loan delivery, warehouse lending, post-closing quality control and investor relations. [Capture 2.0 technology] will enable the automation of all kinds of decisions throughout the mortgage lifecycle, including the point-of-sale stage and servicing. It will help borrowers as they are applying for loans, and help lenders streamline underwriting decisions and even find out which borrowers might be ready to refinance.

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