The problem

In every other industry besides the mortgage industry, buyers know exactly what they are buying before they lay their cash on the table. Car buyers can read Consumer’s Reports and drive the car around the block. Camera and computer buyers can download YouTube reviews of any product on the market in less than 30 seconds. Mortgage originators do their best to collect all the information they can to determine whether a prospective mortgage will be paid as agreed.  They have their standard checklists of questions and they are free to ask more questions as the application process goes on.  But once the mortgage is put in place, the only way to see if the payments are made on time is to track actual payments.  No one can tell the future.  No one can tell if a mortgage holder is going stop paying.  No one can tell the future. Or at least that used to be the case.  Big Data is changing that picture.  Big Data can help us look into the future with some degree of certainty.  But before we get into how that works, let me give you a brief run down on what Big Data is.

What is Big Data?

Historically, all the data computers used was set up in highly structured data bases. In other words, we had separate fields for each piece of data and we spent a lot of time and effort to make sure all the data was clean and accurate.  Big Data does away with that. Big Data reads data that was never meant to be analyzed by a computer.  This includes everything from Tweets and Facebook postings to newspaper clippings. All of these were written for human consumption, not for computer processing. Big Data cut through that.  Big Data is able to read all of this unstructured, messy stuff that was never meant for computers and then makes sense of it.  It other words, it can read Tweets and Facebook postings and data from hundreds of different sources that are written in incompatible styles and assign meaning to what it’s reading.  In the mortgage industry, this means that we can now tap into huge reservoirs of information that were always available to us in the public domain, but we could never get a computer to work with.

Big Data is the Solution the Mortgage Industry Needs

Today, Big Data can tell mortgage companies whatever they want to know about the people who hold mortgages with them.  Big Data can operate as a kind of “distant early warning system” for account servicers.

1.Spending Analyzation

Big Data can look at the shops where your mortgage applicants buy their clothes and watches. Then it can determine whether those shops are in line with their stated incomes or are splurges.  That’s not to say there is anything wrong with an occasional splurge, but if someone consistently spends beyond her earnings, then something is wrong.

2.Social Media Analyzation

We all know the old adage that “birds of a feather flock together.” So, when you know who someone’s friends are, you know a lot about that person.  And where can you find out who someone’s friends are more easily than on Facebook? Big Data can collect a list of your applicants’ friends, build profiles, and assess applicants.  That assessment could accelerate the application approval or be instrumental in squashing it. Knowing the applicants’ friends can offer a second order benefit. If the company approves an applicant’s mortgage, then it can approach each of her friends as well.  This can be particularly lucrative for subprime mortgages.

3.Website Analyzation

Even knowing the websites your applicants visit is fair ball. Applicants who say they want to settle down and build a career but have recently spent a lot of time on overseas travel websites and airline websites are suffering some sort of a discontinuity.  It’s better to discover that earlier rather than later.  

4.Holistic Customer Account Analyzation

Big Data can look at the actual spending patterns of mortgage applicants and see if they are in line with their stated income.  If their spending is too high, they might prove to be good prospects for subprime mortgage at higher interest rates. Banks have historically operated in a highly siloed way.  What I mean is that the department that handles checking and savings accounts knows nothing about their customers’ mortgage accounts, car loans, or children’s tax deferred education savings programs. Big Data can pull this data together across the bank’s own internal databases without violating any confidentiality agreements.  This enables bank agents to make offers to their customers that are right on target.  Imagine a customer who has been surfing new car websites for several weeks but has not asked for a loan – yet.  When she stops into the bank on another matter, the teller could raise the question of a car loan, tell her the extent to which she has been preapproved, and direct her to the office that has already prepared the paperwork.  

So what’s the hold-up?

In spite of these advantages, only 38% of banks in 2013 were using Big Data that way, according to a survey Celent conducted that year.  There is no doubt that percentage has increased during the last four years. Some see the collection of this online data to be an invasion of privacy – and perhaps it is.  The jury is still out.  But as long as this information is in the public domain, it is hard to justify the argument that there is anything underhanded going on here. Nevertheless, customers who want to guard their data more carefully are free to limit access to their social media data to their “friends.” They can also instruct their browsers not to maintain histories or maintain “cookies.” This carries a cost, of course. It’s often very handy for a computer user to rely on her browser to maintain user names and passwords to accelerate logins. Full disclosure of web activity does not necessarily hurt customers, either. A bank could notify a user by email when someone is using her debit card to make a purchase that is out of character with her routine spending patterns.  If there is no cause for alarm, she could simply ignore the alert.  But if it is a threat, she could act immediately. By having a full picture of each customer’s browsing behavior as well as online and offline spending patterns, banks and other financial organizations can tailor offers that are genuinely appropriate and tailored to each customer.

The Future of the Mortgage Industry and Big Data

In the future, we can expect the mortgage industry to use Big Data to access an ever-wider range of publicly available information to build an increasingly comprehensive profile of each customer. It will integrate arrest records, bankruptcy records, credit records, court judgments, property ownership, and library fines available from publicly available online data bases. We can also expect companies in the business of buying existing mortgages to handle their own due diligence using Big Data. Each mortgage for sale may become more of less attractive over time depending on the recent behaviors of their mortgage holders. If you want to learn more about this, feel free to get in touch with me directly.  I’m Eskinder Assefa, CEO of SOMAmetrics in Berkeley, California. We work with mortgage companies to help them realize their full business potential by improving their sales and marketing strategies and leveraging emerging technologies that have an impact on the bottom line.