The Future of Big Data in the Mortgage Industry

mortgage industry

It’s common knowledge that Big Data has arrived in the Mortgage Industry. One of the most important questions leaders in our industry need to ask themselves, of course, is “Where is it all going?” We’re going to give you our take on this issue in just a moment. But first, let me give a short synopsis of what Big Data is for those who are new to this field.

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 before – data that is in the public domain, but we could never get a computer to work with it.

Now let’s take a look at where Big Data is going to take the mortgage industry.

Big Data in the Mortgage Industry

One important Big Data application is pre-populating mortgage applications. In other words, Big Data will mine data from bank records, publicly available data bases, social media sites, and other sites to collect all or nearly all the information required for a mortgage application. This will leave the applicant with the option of either clicking to ratify the pre-populated application as accurate or, on the other hand, edit a few fields here and there to fine tune the application.

Another approach here is for prospective home owners to complete their mortgage applications as they always have and then the mortgage company’s computers will compare the pre-populated versions with the applicants’ versions to identify discrepancies.

In either case, the objective of this exercise will be to enhance the accuracy of the data in the applications at the same time the system reduces the burden on the applicants.

We can also see that computer algorithms will score mortgage applications using machine learning algorithms. These algorithms will approve or deny the applications immediately. Approved applications may be forwarded for processing right away. Rejected applications will qualify for a human review if the applicants don’t feel they have been scored properly. The goal of this instant evaluation will be to eliminate the delays in the current manual evaluation process – delays that are often measured in weeks.

We can see that Big Data will be instrumental in projecting the number of applications for new mortgages or refinanced mortgages in specific geographies and specific time frames. Further, Big Data will project the total value of these mortgages. These projections will help mortgage companies reposition their people and processing power based on projected market demand. These projections would be based on the current mortgage portfolio the industry has in place in various geographic areas coupled with scenarios about shifts in mortgage interest rates.

Spending on Big Data applications and technology will soar. In 2014, 2015, and 2017, we’ve seen Big Data spending in the mortgage industry at $2.6 billion, $2.8 billion, and $3.2 billion respectively. We are going to see spending on Big Data continue to climb as the number of success stories grows.

The mortgage industry is going to suffer a severe shortage of Big Data analysts who know how to manipulate the huge and ever-growing quantities of data that will become available. We are going to need professionals who can manage the enquiries in ways that lead to highly defensible conclusions. The growth in the demand for Big Data analysts is going to outstrip the supply.

We are going to see the rapid growth of specialized firms that assist mortgage companies plan for and implement Big Data projects. This function is going to outsourced rather than treated as a core competence for several reasons. First, most mortgage companies will find it far too expensive to build their own in-house facilities. Second, the process of building their in-house facilities will take too long and are liable to face many dead-end alleys. Third, they will not be able to attract the talent they need at a price they can afford. Fourth, the management in existing mortgage companies will need to go through a steep learning curve that is best handled by a specialized firm. Over time, we can expect mortgage companies to build teams of in-house Big Data talent while leaving the technologies to cloud-based firms.

The privacy issue is going to become a big issue in Big Data. Although everything Big Data practitioners do is legal, the act of mining social media on a wholesale basis was never considered when social media sites were first introduced. We are going to see some interesting and instructive debates on ethical issues over the next decade before we see a consensus emerge. Any legislation passed before those ethical debates come to closure will prove to be ill-conceived and counterproductive.

Decades ago the local bank manager knew his customers well and was in a position to make an informed judgment call about the amount of credit to be extended. Bank managers rarely make those decisions in retail bank branches and mortgage companies today. Rather, those decisions are made by a committee – often in another city. We need to reinvest some humanity into the decision-making process. Incorporating social media will go a long way in that direction.

The mortgage approval process is going to become more transparent. At the moment, borrowers only know whether they are approved or rejected, but they rarely have an idea why they were slotted where they were. In the future, mortgage companies will be in a position to coach their applicants very specifically about what they need to do to be approved.

We are going to see many new, non-traditional players in the mortgage industry. They will spring from places like Silicon Valley. They will offer better service at lower costs than banks and traditional mortgage companies. For example, the Lending Club facilitated $3.6 billion in loans in the first six months of 2015. Likewise, Prosper is growing fast.

We can expect the Federal Housing Administration to develop a growing number of regulations that the mortgage industry must comply with. Many of these regulations will apply to a company’s portfolio of mortgages rather than any given mortgage. Mortgage processors will continue to ensure that they comply with application specific compliance issues, but they cannot be expected to deal with portfolio-wide compliance issues. In fact, it is unlikely that it is humanly possible to do so. This means that mortgage companies will necessarily embrace Big Data to do that job for them. Failure to do so means that they will face stiff penalties in court. It is far better for these companies to catch non-compliance failures on their own and take action than to face their regulators in court.

Carl Pry, a managing director at Treliant Risk Advisors, said “It’s in every bank’s best interest to get one step ahead of the regulators and understand what that regulator is going to know and find. They need to resolve any discrepancies [and] do any file review analysis needed to be able to explain any disparities before the regulators find them.”

Big Data is going to help reduce the risk in mortgage lending. Big Data will help brokers advise their clients about school performance and community crime rates. This will help the buyers make better informed decisions and, ideally, lead to lower risk mortgages.

Big Data is going to prove instrumental in flagging potential fraudulent mortgage transactions. The FBI and other law enforcement agencies are developing increasingly sophisticated techniques to identify potential abuses. Big Data algorithms will incorporate these fraud detection techniques into their algorithms and trigger pre-emptive enquiries.

Residential property appraisals will become more objective and more accurate. Big Data will propose the most appropriate neighborhood comparables. It will develop appraisals using industry standards that will be driven by an algorithm. MReport claimed that, “More than 30 percent of loans fall short of the collateral valuation agreed to between customer and loan officer.” Big Data will help fix that.

New regulations and compliance issues are making the appraisal process increasingly difficult. That, coupled with the fact that the number of qualified appraisers is not keeping up with the demand, means that the industry must necessarily rely on broad based, sophisticated tools like Big Data. This trend will continue.

We can expect federal compliance regulations in the mortgage industry to be applied ever more strictly. In the last few years we’ve seen fines and settlement agreements that were even more onerous that Dodd-Frank required. In these cases, the government targeted the big boys like Wells Fargo Bank, Bank of America, CitiBank, PNC Bank, EverBank, JP Morgan Chase, One West, Santander Bank, and U.S. National Bank. Given the government’s practice of starting with the big companies and working down to smaller companies, it is not hard to see what is liable to happen.

We can expect to see Big Data analysis of non-monetary defaults on mortgages to become more common if not universal. Here, I’m talking about flagging accounts where payments were made early and with an extra principal payment to being made on time with no extra payment. Or we will find homeowners whose home owner association is suing them. Or maybe the local government put a lien on the property on the grounds that the property is uninhabitable. Or the couple is getting divorced. These are all early warning signs that Big Data will track as a matter of course.

Small mortgage companies that cannot afford to buy the necessary technologies will be squeezed out of business. Larger companies will buy them.

Mortgage companies are going to increasingly focus on building higher quality portfolios with fewer staff. The only way to have a smaller staff complement and a larger mortgage portfolio is through automation. That should be obvious. Automation in general and Big Data in particular is the way of the future.

Just to wrap up, I want to make it clear that Big Data is already having an impact on how the mortgage industry operates and we are still at the early stages. We are going to be in for a very interesting ride over the next few years.