As mortgage industry technology continues to evolve, opportunities for greater efficiency and cost-per-loan reductions are continually increasing. Today, the utilization of robotics and automation – along with decisioning logic – has helped to advance the mortgage process from the sluggish, error-prone efforts of a decade ago to a far more productive environment with a focus on data integrity and customer experience.
However, newer technology innovations have now reached a point where it is possible to do much more than simply automate tasks.
With machine learning, mortgage professionals can now achieve bigger goals and deliver greater value to their customers. This advanced technology can handle much of the labor-intensive work that experienced, well-trained underwriters and processors have been responsible for in the past. The result is that these professionals can focus their expertise on managing processing exceptions and problem solving, rather than spending a large percentage of their time buried in “stare and compare” activities.
While the terms “machine learning” and “artificial intelligence” are often used interchangeably, there is a distinction between the two. Using the broadest of definitions, artificial intelligence is a subset of computer science that looks to replicate human reasoning through learning, problem solving and pattern recognition. Machine learning is a specific application of AI.
For simplicity’s sake, let’s consider an industry-specific example. AI-powered machine learning enables technology to ‘remember’ standardized forms, learn from them and then anticipate the type of information that should be in each field of the form. It can also leverage visual recognition to image and index a wide variety of documents that are typically reviewed by processors and underwriters, such as tax returns, W-2s, property titles and appraisals.
When machine learning is used, processors and underwriters can focus more time on higher-value activities to keep the mortgage process on track, and less time on document-level work. For example, by freeing up mortgage professionals time from tasks such as comparing and validating data on standardized documents, they are able to spend more time making sure the mortgage remains on track and the homebuyer’s experience is as trouble-free as possible.
Machine learning can also learn a task and combine it with other tasks to complete a specific process. For example, a system can learn to look at two paystubs, determine that the customer gets paid every two weeks, and then do the math to confirm the annual compensation on the application is correct. In addition, it can look at information under review, evaluate results, and employ interactive communication bots to advise the processor or underwriter of an issue that may need attention.
Neural networks are a machine learning approach that mimics the neurons of the human brain. They excel at performing such tasks as image recognition (as in the use case described above), piloting driverless cars and speech recognition, to list just a few. Neural networks form the basis of much of today’s applications of AI, and it is a widely-held view that their use will bring incredible change across virtually every aspect of the industry. In fact, many believe this technology is poised to transform almost everything we do, and is the basis of much of today’s applications of AI.
While the concepts behind neural networks have been around for some years, the necessary computational power and vast amounts of data required to build functional neural networks at scale has only recently become available. Neural networks are becoming increasingly more accessible and easier to adopt.