TheMReport — News and strategies for the evolving mortgage marketplace.
Issue link: http://digital.themreport.com/i/1196118
34 | M R EP O RT FEATURE N o doubt you've heard and read plenty about how artificial intelligence, or AI, is quietly revolutionizing every aspect of our lives, from transportation to how we shop for goods and services. But these emerging technologies are having an equally profound impact on the financial services industry, and more specifically, the mortgage industry. In fact, no industry stands to gain more from AI—and specifical- ly, from machine learning, a subset of AI—than mortgage lenders. For years, our industry has been handcuffed to manual processes that have prevented lenders from achieving stronger loan quality and a better consumer experience for a reasonable cost. Yet, even as mortgage organi- zations begin to invest in these innovations, few understand just how truly revolutionary they are to the mortgage production process. Even fewer organizations may grasp how these emerging technologies are fueling a new phenomenon called Capture 2.0 technology—and how this devel- opment could forever alter our industry's fate for the better. What Machine Learning Is All About T he buzz on AI and machine learning has reached a fever pitch as new solutions emerge almost daily that pledge to streamline how mortgages are originated, underwritten, and sold in the secondary market. Yet, they are also among the least understood terms in our business today, which is preventing many organizations from recognizing their full potential. To put it simply, AI is gener- ally a catch-all term describing technology that can analyze data and identify patterns in that data to make decisions. AI is about applying knowledge to a specific task or range of tasks to find the best answer. Think of AI as a parent teaching their child how to make their own decisions based on past experiences, logic, and cognitive reasoning. Machine learning, on the other hand, is a little more specific. It also involves data and pattern recognition, but it also enables systems to learn and improve as new information comes to light. This is done through a com- bination of human instruction and self-learning algorithms that distinguish data patterns. With machine-learning technol- ogy, organizations can train their systems to analyze large quantities of data and essentially complete tasks on their own. To put it even more simply, AI is about mimick- ing human abilities and machine learning is about training systems how to learn and complete a task with accuracy. Even if you currently do not use them, it's not terribly hard to visu- alize the impact AI and machine learning tools could have on things such as making credit decisions and meeting the requirements of regula- tors and investors. Both processes, and indeed many others, involve massive amounts of data that are collected and shared throughout the mortgage process. When used effectively, AI and machine-learning tools can help lenders lay the groundwork in pursuit of the fully digital mortgages. In fact, in 2018, Fannie Mae found both technologies were gaining momentum within our industry, and that 63% of lenders were familiar with AI and ma- chine-learning technology, and 27% of lenders were already deploying them. However, leveraging these tools effectively is where most lenders are falling short. The Quest for Better Data and Lower Costs T he most important thing to understand about AI is that it is only as effective as the data that goes into it. The way data is currently collected in our industry is not only time consuming but expensive as well. This is where machine learning comes in. The vast majority of lend- ers rely on manual processes, in combination with some form of optical character recognition (OCR) technology, through which they are able to "grab" data from documents provided by borrow- ers in either paper or scanned electronic format. The idea behind OCR technology is that it saves the time and money that lenders would otherwise spend by having their employees read documents and retype what they see into their system of record. Yet when "reading" loan docu- ments, template-based OCR tools count on data being found in ap- proximately the same location on every document—which almost never happens. Complicating matters are the wide variations of data patterns found in most loan documents. As a result, these types of tools work best when identifying information on structured documents, leveraging templates or keyword search to find and extract information. For structured documents, such as the URLA or closing disclo- sures, this works well enough. This is not the case, however, if image quality is low or the document type has a high degree of variation. As well, many other types of unstructured loan docu- ments represent a challenge and can make OCR an imperfect When 'reading' loan documents, template-based OCR tools count on data being found in approximately the same location on every document—which almost never happens. Complicating matters are the wide variations of data patterns found in most loan documents. As a result, these types of tools work best when identifying information on structured documents, leveraging templates or keyword search to find and extract information.