The Big Data Waste: Missed Opportunities in the Mortgage Industry

As a software and service provider in the mortgage industry, I can confidently say that the volumes of data produced by companies in this space are astronomical, to put it mildly. I also dare to say that, for most of these companies, the wealth of information that could be extracted from this data is completely wasted. After speaking to a couple executives and frontline managers about their perceptions on Big Data and its analysis, I see a pattern contributing to the active disinterest toward exploring data sets:

• General misinformation about Big Data, and all that comes with that

• A lack of imagination about how data can be used to make a direct and meaningful impact on operations.

Perhaps it is far too ambitious to say that this article will solve both (or either) of these issues. But… maybe a brief introduction to predictive analytics and how these can be applied can help prompt a shift in this mentality. It is all about knowing how to ask the right questions.

What is Big Data, anyway?

Simply put, Big Data is a large data set. Enormous actually. More specifically, it is a data set that is so large it requires special technologies to house, manage, and analyze, as conventional tools prove inadequate or impractical. The term, however, has been expanded mostly thanks to the marketing efforts of firms offering services in this space to also include the methods and practices available to interpret the data. Each marketing piece and slogan developed around Big Data has contributed to obfuscating its definition while bringing some level of very marketable mysticism. Sales pitch aside, Big Data is the collective term for the troves of information produced across an enterprise or even across an industry.

What can Big Data tell us?

The answer to this question really depends on who wants to know and for what purpose. That’s the key: identifying a specific purpose. Without clear ideas linked to measurable results, looking into Big Data is like getting a bunch of answers for which there are no questions,  that is, lots of information about nothing we care about very much. It is hard to come up with these questions. It is even harder when we don’t know how best to frame these questions to get the meaningful answers we might be expecting. I’m not a data scientist, and frankly, some of the theories and a lot of the math behind Big Data are beyond my grasp. I think in terms of business processes and software-driven solutions. Learning about predictive analytics and its application has completely changed my attitude about Big Data, opening up a new playground of productivity. Here’s a little about how it works and how best to start thinking about applying predictive analytics so you can start phrasing your own questions.

Predictive Analytics = Forecasting the Future

Forecasting the future is not the same as seeing the future. Predictive analytics uses mathematical modeling and machine learning to predict the outcome of specific scenarios given some data inputs related to the process. The mathematical modeling component helps measure the likelihood of the different scenarios happening given past and fresh data inputs. Machine learning is the really exciting piece; it takes into account historical outcomes to better predict the likelihood of different scenarios, AND even predict new scenarios given patterns and nuances that only machines can identify in the data. So, the wrong way to ask Big Data a question is “Will [this scenario] happen?” -- that’s seeing the future. An appropriate question for Big Data would be worded as “How likely is [insert scenario] to happen?” or “How much more likely is [this scenario] to happen instead of [this scenario]?”.

Opportunities Missed

Given that our industry relies so heavily on data, it is somewhat crazy to me that this same love for data has not extended to using predictive analytics. The possibilities are virtually endless, only limited by the process owners’ imagination. Below are some quick ideas of questions I’d be asking Big Data to help tighten business processes and operations, albeit from the high-level perspective of a solution provider to the industry, rather than the pointed precision expected from a business process owner:

In Originations:

• How likely is a candidate to close on a loan? How long with the process take? Where in the process chain can we expect hold-ups?

• What loan products will work best for a particular group of prospective borrowers? How many post-close problems can we expect? What percent will represent buybacks?

• How will volume be affected given a new incentive or program? Over time, does the metric hold true?

In Servicing:

• How many loans are likely to have modifications or delinquencies or become paid in full? What factors are directly contributing to the portfolio performance?

• What population of loans will have reconciling items? What is the expected source and resolution of these items? How many of these items hit 90 days or go to Reserve?

• How many errors can we anticipate in a given business process? Where will these errors likely come from? If we implement a change, what might be the effect? Once implemented and over time, will these assumptions hold true?

These are a handful of questions in just two areas within the vast world of mortgage operations. Now that you have a framework for how to ask questions of Big Data, what would you like to know? How would you manage if you could predict the future? Where would you invest capital? Would you buy a lottery ticket? Wait, Big Data and predictive analytics cannot see the future.