Great Companies can be found in all industries

great companies

If I were to indulge myself in a Sunday brunch, sip on a cup of coffee, and think about the great companies in America today, they would come from the most exciting industries in the country – like the internet companies and the space age industries. It’s hard for me to imagine great companies springing from lacklustre industries. The research data, however, would show me to be dead wrong.

The companies that Jim Collins qualifies as “good to great” in his book Good to Great: Why Some Companies Make the Leap and Others Don’t come from run of the mill industries. Industries without flare or panache. That’s not what I had expected at all.

But before we look at companies that made the transition from good to great, we need to understand the criteria Collins used. He is the first to admit that his criteria are arbitrary. His criteria are very strict. Companies that he excluded today he might include a decade from now – simply because they would have been around long enough to have a 30-year history. His criteria were companies that:

  • Had 15 years of mediocre financial achievements followed by a turning point and then 15 years of extraordinary achievements. In fact, the companies Collins selected had average cumulative stock returns 6.9 times the general market.
  • Outstripped their respective industries 3 to 1. If the entire industry went through a massive ramp up and the subject company rode the surf with its cohorts, it was not a “good to great” company.

In fact, there are many great companies in America that have far outstripped the market year after year for decades. Collins excluded these great companies precisely because they had been great for a long time. He was only interested in “good to great” companies. The eleven “good to great” companies and their industries are listed below:

Abbott Health care
Circuit City Consumer electronics
Fannie Mae Mortgages
Gillette Consumer packaged goods
Kimberly-Clark Personal paper products
Kroger Grocery
Nucor Steel
Philip Morris Consumer products
Pitney Bowes Business services
Walgreens Retail
Wells Fargo Financial services

What is instructive here is that these “good to great” companies come from a wide range of industries. This means that any company can become a great company. It’s a matter of the senior management or the board making that decision – and then following through.

Cheat Sheet: Everything you Need to know about Big Data and 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.

 

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 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 can be used to improve already existing mortgage processes.

  1. Pre-populate mortgage applications

We believe that Big Data is going to pre-populate 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.

  1. Computer algorithms to score mortgage applications

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.

  1. Big Data analysis of non-monetary defaults

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. 

  1. More Objective Residential Property Appraisals

Residential property appraisals will become more objective and more accurate. Big Data will propose the most appropriate neighborhood comparable. 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.

Big Data is bringing big changes

 

How is the Business of Big Data Affecting the Mortgage Industry? 

  1. Increase in spending on Big Data

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.

  1. Increased need for big data analysts within the mortgage industry

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.

  1. Increase in consultants

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.

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

  1. Automation and Big Data will be an important pair

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.

 

*Warning*: New Players

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.

 

How Does Big Data Help the Mortgage Industry Keep up with New Regulations and Laws?

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.”

Here are a few more examples of how Big Data helps keep Mortgage companies out of legal trouble:

  • 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.
  • 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.

 

Big Data, The Mortgage Industry, and the Mortgage Buyer: How Relations Can Be Vastly Improved

 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.

Additionally, 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.

**Warning:**Potential future issues: Privacy

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.

 

Conclusion

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.

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.

4 Ways Big Data is getting Mortgage Companies the Information They Need

big data

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 Mortgage and Big Data

In the future, we can expect mortgage companies 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.