Creating a New Normal for Loan Quality

Dave Parker

Dave Parker is Chief Product Officer for LoanLogics, Trevose, Pa., a provider of loan quality technology for mortgage manufacturing and loan acquisition. He is responsible for defining and executing the vision and direction of the company’s product portfolio and designing new solutions for the industry’s current challenges. He has more than 30 years of experience in mortgages and technology.

Lowering loan manufacturing costs, improving overall mortgage processing efficiencies and achieving optimal risk management begins with improving the quality of the underlying mortgage loan file data. Fortunately, automation is bringing much-needed improvements that efficiently bring data inconsistencies to the surface so that accuracy can be achieved and decisioning can be improved.

By leveraging artificial intelligence and machine learning tools, today’s originators are able to analyze large datasets, automate decisioning and eliminate a wide range of manual tasks formerly done by human staff. In addition to helping originators classify loan documents, these tools are being used to fuel data extraction programs and automate the evaluation of data quality. These innovations are also moving quality management up in the process, which can reduce or eliminate downstream costs for quality assurance and quality control.

Ultimately, these advancements in automation are creating a “new normal” in risk management, and it’s exciting stuff. But they’re also creating new challenges for lenders. Replacing human tasks with automation is a significant change that requires originators to more efficiently organize and manage their staff resources while ensuring an effective balance between automation and full-time employee skill requirements. The question is how.

Elevating Human Ability through Automation

Every day, news headlines warn us that we’re all about to be replaced by machines in what some have dubbed a “job apocalypse.” This summer, a report from the McKinsey Global Institute predicted that nearly 40 percent of all U.S. jobs are in occupations that will shrink by the year 2030 due to the increasing adoption of automation and artificial intelligence.

Of course, using technology to replace human tasks is nothing new. One only needs to see the growing number of self-checkout lines at the local Target or supermarket to see it in action. No one likes to say that automation will replace people, but it absolutely will replace certain jobs that people perform. The question then becomes with every “job” that is eliminated, what new “job” could add more value to the business?

It’s unlikely mortgage lenders will ever be able to completely eliminate the human element from loan production, nor would they want to. While automated loan quality tools like the ones I’ve described can eliminate a huge amount of manual labor, they also enable human skill to be more efficiently used on loan exceptions (i.e., when something falls out of the automated workflow) and on higher-value risk management tasks. 

Evaluating exceptions can help to identify systemic issues, and a risk management focus can lead to action plans and lending practices that drive more value for lenders. How to do this will depend entirely on the type of exceptions that occur, which is information originators won’t have until they implement loan quality automation. But once they do, lenders can refocus staff and make the necessary changes to manage and address exceptions, which ultimately is likely to create new roles for employees.

The Potential to ‘Upskill’ Staff

No one can say how much job displacement automation will create in the mortgage industry. But with that, it is more likely that automation will require some mortgage professionals to develop new skills that they didn’t have before. Economists describe this as the “upskilling” of workers. In fact, in the same McKinsey report mentioned above, economists also predicted automation will create significant job growth in new areas, which will offset many jobs displaced by automation.

We’re already seeing examples of this in our own industry. A growing number of originators have adopted artificial intelligence and machine learning tools to help manage loan quality. Because there are many different types of data and documents used in mortgage origination, however, these tools need to be “trained” by the vendors that provide them in order to work effectively. The more training these systems receive, the fewer exceptions a lender will have. On the other hand, vendors that lack document repositories may pass some of this work onto their customers, something to be considered when evaluating options in the marketplace and your own technical skills. If there are certain exceptions that reoccur and are systemic, it will take a human (on the lender or vendor side) to find out how to adjust these technologies to address them.

Automation, then, creates two options for lenders–reduce staffing, or leverage technology to keep the staff you have and re-focus them on tasks that support company growth, such as broadening loan products to capture more market share. In other words, automation can give lenders more flexibility to pursue a variety of staffing models and doesn’t necessarily have to eliminate jobs, since you still need people to manage products, sell loans and handle exceptions. Some positive benefits include the ability to scale your volume by four, five or even six times using the same resources you have today. For some lenders, it could also mean moving certain employees into different fields, such as moving an underwriter into a business analyst position.

Moving Quality Management Upstream

With the cost to originate in the retail production channel now exceeding a whopping $10,000 per loan, according to the Mortgage Bankers Association’s latest numbers, lenders need automation more than ever. But the real value of automating loan quality is not just in keeping humans from performing routine, repetitive tasks. Rather, it is the ability to move loan quality management upstream, where lenders can catch problems early, instead of after the loan closes, when everything is too late.

By leveraging automation to identify and classify loan data and documents when they are received at the front end of the mortgage transaction, originators are much more likely to be closing higher-quality loans at the end of the process. This eliminates the need for a post-mortem check after each closing, which in turn eliminates a huge amount of downstream costs. It also greatly reduces repurchase risk and potentially taking a 10 or 15 percent hit when a lender is forced to buy back a loan.

Automating loan quality management at the front end also creates a better customer experience. The more quickly a lender can validate and verify an applicant’s information during the origination process, the less likely the lender will need to go back to the borrower to ask for “one more thing” during the underwriting phase or later. This not only reduces extra labor, but it also keeps the borrower’s anxiety to a minimum during the home buying process.

There has been plenty of talk about automation replacing jobs in the mortgage industry. With the emergence of new loan quality automation tools, however, lenders no longer must invest so heavily in human staff to perform the routine work that ensures they are using good data and originating quality loans. By leveraging a configurable, scalable loan quality management solution, lenders will be able to improve compliance speeds, reduce production costs and eliminate the traditional, highly manual tasks that have been driving down profit margins for so many.

For some originators, this “new normal” in loan quality may result in a smaller workforce. For others that redeploy their teams to perform more valuable work, it could result in a more scalable and ultimately more successful business. Whichever strategy they choose, lenders that can leverage automation to move loan quality management to the front end of the mortgage process will undoubtedly see greater transactional speeds, greater efficiencies and greater borrower satisfaction—and will be better off for it.

(Views expressed in this article do not necessarily reflect policy of the Mortgage Bankers Association, nor do they connote an MBA endorsement of a specific company, product or service. MBA NewsLink welcomes your submissions. Inquiries can be sent to Mike Sorohan, editor, at; or Michael Tucker, editorial manager, at