But before I get into that, let me give you a very brief explanation of what Big Data means for readers who don\u2019t have any experience with it, and how Big Data impacts industries today.<\/p>\n
Historically, all the data computers used was set up in highly structured databases. In other words, we had a separate field for each piece of data and we spent a lot of time and effort to make sure all the data was clean and accurate.\u00a0 Big Data does away with that. Big Data reads data that was never meant to be analyzed by a computer.\u00a0 This includes everything from Tweets and Facebook postings to newspaper clippings. All of these were written for human consumption, not for computer processing.<\/p>\n
Big Data cut through that.\u00a0 Big Data is able to read all of this unstructured, messy stuff that was never meant for computers and makes sense of it.\u00a0 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\u2019s reading.\u00a0 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.<\/p>\n
Big Data opens up new and exciting opportunities in the mortgage industry. I\u2019m going to tell you very briefly about six of them.<\/p>\n
The first is in the area of account origination and underwriting.\u00a0 In the past, we only looked at the information applicants gave us on their mortgage application forms.\u00a0 Of course, we would verify the information whenever we could by contacting third parties. But the fact is that we didn\u2019t \u2013 and couldn\u2019t \u2013 go beyond that.\u00a0 Some pundits have estimated that this data really only represents about 5% of what we should take into account when deciding to approve a mortgage.<\/p>\n
There are many millennials who don\u2019t use banks as much as their parents did. This means they don\u2019t leave the banking breadcrumbs that the folks who make decisions about mortgage approvals like to follow. But, in a larger sense, these millennials do qualify for mortgages because they have the potential to meet their monthly mortgage payments.<\/p>\n
Big Data impacts the mortgage industry by giving us the tools we need to mine data sources that were unavailable before.\u00a0 I\u2019m talking about social media data and actual financial purchasing patterns. When we learn to factor in the data about mortgage applicants from a far wider range of sources, we\u2019ll be able to make better informed decisions about approving mortgages.<\/p>\n
Account Servicing is a low margin business with lots of transactions. We never know when things go wrong with an account until they do go wrong.\u00a0 We don\u2019t have any way to know that an account is in distress until the payments start coming in late \u2013 or don\u2019t come in at all. On the other hand, we don\u2019t know that a couple is ready to move up to a larger house and a larger mortgage until they file a new mortgage application.<\/p>\n
Big Data impacts us in that it can help us be proactive in these situations by monitoring publicly available data and drawing some conclusions about problems and opportunities coming up soon.<\/p>\n
Big Data impacts the way we perform account servicing. Big Data allows us to pull together data from different sources in different formats to predict when trouble is in the offing. We can track household spending patterns from our clients\u2019 credit card records. We can see drops in their incomes by tracking their bank account deposits. We can find out when they lose their jobs by tracking their Facebook and LinkedIn accounts.<\/p>\n
By the same token, we can use these same sources to determine when a baby is on the way and a couple is likely to take a step up in housing. Or maybe a client announces his promotion or new job with a new employer on his social media.\u00a0 Maybe our Big Data engines track an announcement of a promotion in the business section of the local newspaper. These are indicators that your client will soon come knocking on your door asking for a larger mortgage.\u00a0 Or maybe you should knock on her door to let her know that you\u2019ve already qualified her for a larger mortgage.<\/p>\n
Cross Selling is a third area of opportunity that Big Data impacts and can facilitate. People outside the banking industry are shocked to learn that their banks operate as a collection of silos that happen to share a piece of real estate.\u00a0 Here\u2019s what I mean. The department that handles checking and savings accounts has no connection with the mortgage department. The department that handles lines of personal credit are divorced from the other departments. And it goes on and on like this throughout the bank.\u00a0 A bank that can build a single view of its customer \u2013 what bankers call an SVC \u2013 can cross sell financial products that include a mortgage, a car loan, and a child\u2019s education fund.<\/p>\n
But let\u2019s say that you work for a mortgage company that only offers mortgages. In a situation like this, you could identify cross selling opportunities and refer them to partners in other companies. When your partner closes a piece of business, you can pick up a finder\u2019s fee or a referral commission.\u00a0 This wouldn\u2019t be possible without Big Data.<\/p>\n
Risk and regulatory reporting is a fourth fertile area. After the crises in 2008, the Federal Housing Administration put several programs in place to protect mortgage borrowers. These include the Home Affordable Refinance Program or HARP for one. Another is the Home Affordable Modification Program or HAMP. Others are the Short Refinance Program and HAPA. Mortgage companies need to make sure they don\u2019t run afoul of these programs. But here\u2019s the catch: when mortgage companies are managing their day-to-day business, they\u2019re focusing on only one mortgage application at a time. No one is looking at the makeup of the full portfolio of applications at the detail level. And that\u2019s exactly why mortgage companies are shocked to learn that they are violating FHA guidelines. This is where Big Data can come to the rescue. Big Data can look at your full mortgage portfolio and test it against the full range of the compliance terms in each of these programs. Big Data can highlight upcoming problems before regulators do. Mortgage companies that proactively identify these compliance issues are going to have a far better year-end than those who find they are out of compliance when they are facing a regulator in court.<\/p>\n
Mortgage Fraud is a fifth opportunity area. Here I\u2019m talking about subprime fraud, property valuation fraud, and foreclosure fraud. Fraud is rare because, by and large, people are honest and they act in good faith. But, every once in a while, you\u2019re going to come across a fraudulent transaction \u2013 and that can be expensive. You are far better off if you can catch the fraud early on and deal with it immediately.<\/p>\n
Again, this is where Big Data impacts the mortgage industry. Big Data can look for patterns and discontinuities in those patterns. The FBI and other law enforcement agencies are always developing sophisticated techniques to identify potential fraud. We can harness those techniques in Big Data algorithms to analyze the mortgage applications under consideration.\u00a0 The Big Data algorithms will run in the background and let your staff get on with the business of approving your mortgage applications.<\/p>\n
The last area I want to talk about is corporate acquisitions. Think back to the period right after the 2008 collapse.\u00a0 At that time, we saw Bank of America buy Countrywide Financial and then go on to lose $40 billion from that little acquisition.\u00a0 The same sort of thing happened with Washington Mutual. The company failed and the government took it into receivership. JP Morgan bought Washington Mutual and took it out of receivership.\u00a0 Within a couple years it became clear this was not a very good business decision at all.\u00a0 In fact, JP Morgan eventually sued the Federal Deposit Insurance Corporation over that purchase for non-disclosure.<\/p>\n
If these companies had access to Big Data tools and used them wisely, they could have avoided these debacles.\u00a0 Big Data could have sifted through the huge masses of data available in both structured and unstructured forms and uncovered these problems well in advance.\u00a0 It\u2019s not as though anybody was trying to hide anything.\u00a0 All the information was there for anyone to see.\u00a0 The real problem was that there was so much information in so many different forms prepared for so many different purposes that it was virtually impossible for anyone \u2013 or even a team \u2013 to sift through the material and highlight the problems that would be evident if you just knew where to look.<\/p>\n
In summary, I think it\u2019s clear that we already have some compelling evidence that Big Data impacts the mortgage industry significantly when it has been applied wisely.\u00a0 These initial successes are going to spur others on to use this rapidly developing technology more widely in their own companies.<\/p>\n
If you want to learn more about this, I urge you to read what Vamsi Chemitiganti from Hortonworks has written on the subject.\u00a0 Or feel free to get in touch with me directly.\u00a0 I\u2019m 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.<\/p>\n","protected":false},"excerpt":{"rendered":"