Many studies over the past year have covered how the mortgage industry is advancing toward the digital mortgage. Many companies have been driven to act in response to lockdowns, forced remote work and other disruptions due to the COVID-19 pandemic rather than through strategic vision and long-range planning.
Despite some indicators of progress as technology advances throughout the lending process, the mortgage industry is lagging in the adoption of artificial intelligence (AI) to improve business success. The volatility of the mortgage market and the high compliance-risk environment has always made innovation tough, and this has instilled a cautious mindset (and associated resistance to change) in many mortgage professionals.
With the industry’s cyclical nature and other disrupters, it’s hard to plan long-term investments and transformational strategies. And even though some of the industry’s systems are aging and inefficient, many mortgage professionals have come to trust manual processes and controls as a form of protection from audits and exams.
With these considerations in mind, many industry leaders wonder if change is worth the investment or risk. It’s this hesitancy to innovate that ultimately hinders their ability to adopt new technologies. Recent experience with e-closings provides an excellent example of how hard it is to gain technology adoption at scale even when the need is obvious.
Missed opportunity
E-closings have been around for many years. The share of lenders with the technology needed to close loans electronically surged last year, but mortgage companies didn’t necessarily use it, according to a new Stratmor Group report.
Although the share of mortgage companies with the technology needed to e-close jumped to 43% in 2020, up from 18% in 2019, only 12% of companies had at least three-quarters of their users adopt these tools. While there’s a 54% adoption rate among individual users of e-closing technologies, it’s only 30% at the company level. This results in a net adoption rate of only 16% when measuring both lender and user adoption, according to Stratmor.
Too often, the motivation to solve a problem with technology occurs when a manual process has simply become unbearable from an employee or client perspective, or untenable from a control perspective, but not from an innovation mindset. Many in this industry have relied heavily on procedures manuals that got bigger as products, investor requirements and compliance rules got more complicated.
And as the burden grew, too few paused to think about how machines could learn these rules and execute them instead. Nor did many see the trade-off: If a mortgage originator is hyper-focused on following the rules, who is paying attention to the client?
It’s beyond time for a change. (Who in the mortgage industry hasn’t heard that before?) For mortgage lenders, originators and servicers, the future is about using data and artificial intelligence to modernize and optimize the business.
Signs of optimism
Several technologies that are subsets of AI have already become common in our lives. Many in the business use speech recognition and natural language processing through various automated assistants, and they use optical character recognition and automated data extraction (more commonly known as OCR and ADE) to scan documents and extract data.
Seeing how easily these technologies are being incorporated into everyday life is a sign of optimism that the mortgage industry can apply them for the benefit of their clients and their businesses. A caveat: The more sophisticated technologies become, the more likely they are to be dependent on good data, and lots of it. Poor-quality or incomplete data allows you to achieve only a fraction of possible success. In fact, you can cause much more harm than good.
Each mortgage company that considers investing in artificial intelligence should consider good data as a prerequisite. Some opportunities where AI can be used in the mortgage industry include:
- Predictive analytics. While the best mortgage companies retain a personal touch and a client-for-life mentality, the mortgage business often involves hard choices about where to spend our limited time. That’s where predictive analytics comes in. Having reliable guidance about what will happen in the future tells us where to focus to achieve the best outcomes. Although this is not an AI technology, predictive analytics is a great first step on the road to AI and it can help you assess whether your data is ready for more autonomous AI “prime time.” As the saying goes: garbage in, garbage out.
- Robotic process automation. When mortgage companies remain constrained by extensive manual processes, they steal time away from better client service, increasing sales volumes and process efficiency. Many of these manual processes can be automated via robotic process automation (RPA), and they’ll be done faster and more consistently. Better yet, this automation frees up your employees for their job duties that require critical thinking. Remember the previously mentioned procedures manuals that encourage us to be robotic in our process execution? Why not leave that to robots?
- Machine learning. Whereas predictive analytics deals with relatively static patterns, machine learning allows for more open-ended analysis of data and prediction of results without explicit guidance. If RPA technology, at its core, is designed to outdo humans in terms of task speed and accuracy, machine learning is somewhat the analytical equivalent. Many mortgage professionals wish for the time and capability to see all the data; discern hard-to-find patterns and advantages; employ creative thinking to see data in new ways; and constantly devote brainpower to achieving better results and continuously improving on lessons learned. These are core objectives of machine learning.
It’s important to note that these technologies aren’t plug and play, and they don’t solve problems without considerable investments of time and business knowledge to ensure that your outcomes are in line with your objectives. Nor are these implementations one and done. Over time, data changes, business needs change, processes change and opportunities change —so constant care and attention is a must. In addition, the change-management and governance processes around these new technologies are just as important as they are for your existing systems, and for the same reasons.
The volatility of the mortgage market and the high compliance-risk environment has always made innovation tough, and this has instilled a cautious mindset … in many mortgage professionals.
Challenges ahead
The annual Deloitte Insights report highlighted how industry frontrunners integrate AI into their strategic plans. They pursue multiple options for acquiring and developing AI applications, as well as for sourcing big data. They apply AI to client engagement opportunities throughout the lending and servicing processes, and for revenue enhancements, with metrics to track their progress.
Challenges exist. Beyond the pitfalls mentioned above, there are considerations about company morale, training, setting a vision, and how to communicate early and clearly about strategic plans for adding technology in new ways. It’s no coincidence that when one colleague mentions artificial intelligence, another usually pipes up with a reference to “The Terminator.” The misunderstanding of the technology is real, as is the fear of it when it comes to employee displacement.
The message needs to be this: Most people want to be doing smart things, not mindless tasks and busywork. Introducing AI as relief for overworked fingers, voices and eyes (and as a path to more data-refined inputs that inform our creative thinking) is the way to deliver on these desires.
From there, the possibilities are virtually endless. As stated in the MIT Sloan Management Review, significant financial benefits are likely only when organizations define multiple effective ways for humans and AI to work and learn together. Who knows where this evolution will lead? All that can be said for sure is that it’s happening — with you or without you. And without AI technologies, you simply won’t be able to remain competitive in the future.
Successful technology execution is much more likely when excellent partnerships exist. Technology in and of itself solves nothing. Technology leaders and their teams must be business savvy to understand how new tech tools can be applied to solve problems. Business leaders must be savvy to understand the art of the possible and, when it’s there, overcome the fear of the unknown.
With a strategic commitment to incorporating the right technologies and a dedication to continuous improvements in all that you do, your organization will be well on its way to becoming a respected lender of the future. You will have better business practices, productivity, client retention and profitability. ●
