DSNews delivers stories, ideas, links, companies, people, events, and videos impacting the mortgage default servicing industry.
Issue link: http://digital.dsnews.com/i/1214226
73 loans, they could do so in seconds using machine learning tools to aid the process, rather than spend several days poring over them by hand. ese tools are also great for filtering loans. For example, if I'm buying MSRs and I don't want a large concentration of loans in a specific state or ZIP code because of the perceived risk involved, I can get that information with a click of a button. I could also build a view of key data elements that are important to me for the loans I'm acquiring and have this information presented in nanoseconds. New technologies also give sellers a huge advantage when it comes to market timing as well, which is a big deal in a rising rate environment. When you've made a commitment to sell a pool of loans and there is an agreement in place, and a price locked in 10 or 15 days, and you don't deliver the files in time, you're at risk of having your loans repriced at today's rate, which cuts into the premium you were expecting to receive. Because new technologies enable faster trades, the timing issue virtually disappears. When it comes to onboarding loans onto one's servicing platform, buyers want to make sure there are no data defects and make sure they reach out quickly to borrowers to let them know they are their new servicer. Automated technologies allow them to make sure they have all the correct information, send out borrower letters and have staff reach out to borrowers much faster—servicers can even set up these processes with automated dialers and messaging. is saves an enormous amount of time and improves loan retention. HOW MOMENTUM IS GROWING Over the past two years, we've seen a tremendous amount of pickup in machine learning and data extraction tools in the secondary market. Ultimately, I believe this will bring greater consistency to how the secondary market operates, as well as new best practices. Similarly, secondary market participants that invest in these new technologies won't simply be able to absorb a great number of loan file types faster and more efficiently, they will also be able to build stronger, more efficient organizations that are better able to compete going forward. Yet there is a major obstacle that lies in the way that I haven't talked about. Inertia. ere's an entire generation of secondary market professionals who grew up doing things a certain way and remain loyal to those processes—and the people who perform them. ere is a different way to think about this, as new technologies do not necessarily mean these jobs will go away. If institutions that both originate and service loans no longer need 30 acquisition team members to onboard newly acquired loans, but instead only need five, they can move their staff to the origination side of the business and have more flexibility to scale on either side as market volume ebbs and flows. It's really a matter of reallocating your human resources to best utilize their skills to improve your business. Sometime in the future, there will be an inflection point at which participants will either adopt these new emerging technologies or put their companies' futures at risk. roughout the business world, there are countless examples of companies that are no longer around because they couldn't keep up with the pace of accelerating change. It is naïve to think that it can't happen in the secondary market. At the end of the day, it's not a question of if the secondary market will embrace new AI and machine learning technologies, but when. e reduction in time, the cost savings, and the higher quality assets that will result from these changes are too great to ignore much longer. In any event, there will come a time when the predominant technologies used in the secondary market today will be looked at like yesterday's vinyl records—minus the nostalgia. Craig Riddell is EVP, Chief Business Officer at LoanLogics. He is responsible for establishing and developing ongoing relationships with LoanLogics' largest enterprise clientele, as well as leading the sales, marketing, and account management functions. He can be reached at Craig.Riddell@LoanLogics.com. There's a huge variation among loan purchasers in terms of the data elements that they are concerned about. Certain buyers require checks on 40 different data fields, while others may want to look at 30 or 50—there's really no consistency among them. Their legal agreements all differ as well. If an investor needs to check 30 unique data elements when buying a pool of loans, they could do so in seconds using machine learning tools to aid the process, rather than spend several days poring over them by hand.