Over the past decade, companies across all sectors have embarked on digital transformation journeys, revolutionizing traditional business processes through an ecosystem of advanced, cloud-based technologies.
However, different industries have embraced digital transformation at varying paces, and companies within these industries have adopted these changes in various ways and to different extents. While some sectors have rapidly plunged into digital waters, others have been more hesitant.
According to IBM^, digital transformation “takes a customer-driven, digital-first approach to all aspects of a business, from its business models to customer experiences to processes and operations. It uses AI, automation, hybrid cloud, and other digital technologies to leverage data and drive intelligent workflows, faster and smarter decision-making, and real-time response to market disruptions.”
Compared to other financial sectors, the mortgage industry—comprising loan origination, underwriting, servicing, and securitization—has been relatively slow in fully embracing digital transformation. Up until the last decade, it was common for mortgage professionals to rely on traditional methods, such as paper-based documentation or static spreadsheets, for managing tasks.
For instance, some mortgage companies continued using manual methods for loan calculations and documentation despite the availability of advanced digital tools.
The Difference Between Automation and AI
Automation and artificial intelligence are related technological concepts, and both are components of Mortgage Tech. Even though these two forms of technology certainly overlap, it is essential to know the difference between them.
Automation is a category of technologies that can repeatedly complete tasks or carry out processes without manual intervention. Robotics, software, and artificial intelligence can all be automation technologies.
Automation technology works based on built-in triggers that prompt a machine to take specific actions, completing predictable, repetitive, routine tasks that humans must otherwise spend significant time addressing themselves. While automation tools perform and streamline repetitive administrative tasks through standardized workflows, a human programmer must rewrite rules and scripts whenever a workflow requires a change or update.
AI is even more sophisticated than pure automation; it simulates human behavior and can react independently instead of strictly following a specific string of actions. It relies on algorithms that can readjust and update rules and scripts to change workflows as necessary to meet business needs. From the outset, these algorithms can be programmed to react appropriately to unpredicted catalysts.
AI-driven technologies can perform automation, but they can also identify and resolve IT issues. They can meaningfully analyze large quantities of data to produce intelligence that enables businesses to plan, strategize, and overcome problems.
The Evolution of Mortgage Tech
The launch of Freddie Mac’s Loan Prospector and Fannie Mae’s Desktop Underwriter in 1994 and 1995, respectively, stands as one of the most pivotal and transformative moments in the mortgage industry, ushering in a new age of Mortgage Tech. Marking one of the first successful, wide-scale implementations of early artificial intelligence, these tools revolutionized mortgage origination. They automated what had been entirely manual processes, using AI to provide real-time guidance and risk assessment, significantly improving efficiency and accuracy in the underwriting process.
This innovation paved the way for the introduction of Automated Valuation Models (AVMs), which emerged shortly thereafter. CoreLogic®, a pioneer in this space, developed AVMs that leveraged advanced algorithms to evaluate complex property data and provide accurate value estimations. These models significantly reduced the need for manual appraisals and expedited the property valuation process.
At the turn of the century, the mortgage industry witnessed another significant leap with the launch of Ellie Mae. By acquiring Contour and Genesis, the early Windows-based Loan Origination Systems (LOS), Ellie Mae pioneered the era of workflow automation. Their solutions streamlined the mortgage process from application to closing, integrating various functions into a cohesive platform and setting the stage for modern, end-to-end mortgage management systems.
Big Data and Cloud Computing
The near collapse of the mortgage market in 2008 ushered in an era of heavy regulatory change, leading to significant decreases in productivity, new required processes and procedures, and a substantial increase in the costs of originating mortgages. This crisis served as a catalyst for the industry to pursue a renewed push toward digital transformation.
This period can be viewed as the foundational core for enabling the industry in the current AI-emergent age. Like many other sectors, the mortgage industry began shifting away from siloed, on-premises data storage to cloud storage and computing, embracing big data. This era also saw a proliferation of digital data availability, encompassing everything from property details to employment and asset information.
These changes led to an exponential increase in the use of predictive analytics, machine learning, improved algorithmic decision-making, and enhanced risk assessment, setting the stage for the sophisticated AI-driven tools we use today.
COVID-19: Accelerating Digital Transformation in the Mortgage Industry
The onset of the COVID-19 pandemic marked a turning point for industries far and wide, thrusting digital transformation from a strategic initiative to an operational imperative. No matter where businesses found themselves on the digital journey, the global health crisis and the consequential lockdowns demanded innovative and virtual solutions to sustain operations.
In the thick of disruption, Artificial Intelligence (AI) emerged not just as a tool but as the cornerstone for ensuring businesses could continue amidst unprecedented challenges.
The pandemic laid bare the digital shortcomings within various sectors and intensified the need to weave AI seamlessly into the fabric of business strategies. Many in the mortgage industry-recognized, albeit in retrospect, the advantages AI could have offered in navigating the tumultuous period with greater agility.
As time progressed, the urgency for mortgage lenders and servicers to incorporate AI into their digital infrastructure became undeniably evident.
A 2021 McKinsey and Company report1 illuminated the inevitable shift towards digital platforms, underscored by an observation that customers are increasingly choosing digital channels, both to minimize physical contact and for sheer convenience.
On a similar note, Fannie Mae2 observed a noticeable increase in recent homebuyers' preference for online methods during the early stages of obtaining a mortgage, such as gaining insight into the mortgage process or comprehending different terms and options. Yet, there was a dip in the inclination to use online tools for follow-up processes, like pre-qualification or updates on their loan status.
Forbes Insights3 also noted a trend, citing, “The global pandemic drove an increased demand among consumers for a low-touch, no-touch mortgage process.”
As we look ahead to the next chapters, we're geared up to explore how AI applications are reshaping the mortgage sector to boost efficiency, heighten accuracy, and bestow a competitive edge. Stay tuned as we embark on an insightful journey into the heart of AI in mortgage lending, evaluating the tools, technologies, and strategic foresight that sculpt this critical industry's future.
