Big data may seem like buzzword du jour, but with the proper application, there is big potential in big data to transform the default servicing industry.
Of course, before you can harness your data, you need a way to capture it. This is where technology comes into play. The promise inherent in automation is increased efficiency and improved workflow. However, that premise operates on the assumption that the process being automated is as optimized as it can be in its manual form. For example, the advent of the loan origination system didn’t fundamentally change the way a loan is manufactured – it simply automated the highly perfected process that already existed.
In contrast, automation has done little to improve the default servicing process because the Consumer Financial Protection Bureau’s (CFPB’s) servicing rules changed the game drastically for the mortgage servicing industry as a whole. It required servicers to pivot from a processing mentality to one focused on the customer experience. That dramatic of a shift requires servicers to reassess how they operate, and the process has yet to catch up with servicers’ new reality.
Cost to service non-performing loans is another consideration. According to a white paper from the Mortgage Bankers Association (MBA) and PriceWaterhouseCooper (PWC) titled, “The Changing Dynamics of the Mortgage Servicing Landscape,” the per-loan cost for non-performing loan servicing has increased dramatically over the past few years. In 2008, the cost to service a non-performing loan was $482. By 2013, that cost had jumped 489 percent to $2,357. Unsurprisingly, 2013 was the same year that the CFPB announced its servicing rules.
Specifically with regard to default servicing, the CFPB’s servicing rules have mandated timelines to which servicers must adhere, and these timelines weren’t built around how long the process currently takes. They were built around what’s reasonable for the consumer to expect an answer on their foreclosure alternative request. When the process doesn’t fit the timeline, it’s time to rethink the process.
As the MBA and PWC point out in their white paper, “While some servicers have attempted to mitigate these cost-to-service increases through technological innovation, many remain challenged by legacy platforms that require time-consuming and costly changes to accommodate the latest requirements and servicing standards.”
The key word in that sentence is “legacy.” Digitizing the current default servicing process may shave off a day or two of paperwork “transit time,” but it doesn’t fix the fundamental flaws inherent in the way the current process works. Servicers spend far too much time analyzing the file to get to an answer when the answer is based on a simple calculation. Whether the borrower will ultimately qualify for a loan workout doesn’t matter – servicers spend the exact same amount of time and manpower on a “No” file as a “Yes” file.
That needs to change.
So how does big data play into all of this? Imagine being able to predict which of your defaulted borrowers will be more likely to qualify for, much less respond to, a loan workout offer. How much time could you save if your organization devoted the bulk of its energies to only those files that truly need it? What level of service could you provide to the borrowers that those files represent? How much could your organization cut its cost to service non-performing loans? What would it mean for your organization to be able to provide a detailed, step-by-step accounting of when each piece of the loan workout process was completed?
In a fully digitized default servicing environment built around the customer experience, it’s possible to capture data at the first point of entry directly from the consumer. From there, the ability to parse out and dissect that information creates limitless possibilities for servicers to improve the process and reduce the cost to service while maintaining the transparency and accountability that is at the heart of the CFPB’s servicing rules.
It’s time for the default servicing industry to think big. By making big changes to their process and embracing big data, they have the potential to realize not merely big, but enormous, results that can transform default servicing into a service-oriented paragon of efficiency.