Digital Transformations

A 2015 McKinsey study reported that food retailers can improve their operating margins by up to 60% simply by harnessing the power of Big Data. In order to keep pace with consumers’ fickle buying habits, food and beverage companies need to begin combining raw point-of-sale data with the Big Data that is now available. Analytical capabilities then can transform this data into meaningful intelligence that can inform management decisions. Those decisions will boost sales and improve their overall bottom-line performance. For example, food and beverage retailers, suppliers, and trading partners can share Big Data to ensure they offer the right products, in the right quantities, in individual stores and online.

Big Data Helps Drive In-Store Revenues

Food and beverage companies can use Big Data to increase traffic to their brick and mortar stores. The GPS location capabilities of most mobile phones provide a channel for retailers to display “pop-up” promotional messages that are highly relevant to an individual’s specific location and past purchasing history. A shopper, for example, standing in a frozen food aisle can receive a text offering a discount for a certain ice cream flavor nearby that she has bought in the past.

Big Data Helps Schedule Food Deliveries

Big Data can optimize on-time deliveries of orders to restaurants, food chains, and home customers. Big Data will collect recent information from various sources about road traffic, weather, temperature, routes, etc. and provide an accurate estimate of the orders’ times. This data analysis helps ensure that food/beverage companies don’t waste their resources transporting stale products. They will deliver perishable food items when they are fresh.

Big Data Helps Allocate Food and Beverage Across the Country

By using Big Data to track purchasing decisions from wholesalers down to the customer level, food and beverage companies can learn what products are being purchased and where. For example, a company might learn that customers in the Pacific Northwest are purchasing 15% more of a diet beverage than the nationwide average. Further, they may learn that the Midwest is purchasing 15% less of that same beverage. This knowledge allows the company to know to ship more of the diet product to the Pacific Northwest and less to the Midwest.

Big Data Helps Maintain Consistent Food and Beverage Quality

Big Data allows restaurants to maintain consistent quality of their products. Consumers expect the same taste in food at the chain restaurants they love. The taste of food not only depends upon the proper measurement of ingredients, but also on their quality, storage, and season. Big Data analytics can analyze such changes and predict the impact of each on the food quality and taste. The insights from these analyses will be used to identify pain points and suggest measures for improvement.

Big Data Analyzes Customer Sentiment

Big Data can analyze customer sentiment by monitoring customer emotions expressed on social media networks. Food companies use sentiment analysis to track their customers’ emotions. They can assess negative reviews and take appropriate preventive steps before the word spreads. Large food retailers like McDonald’s, KFC, and Pizza Hut have found this particularly valuable.

Big Data Has a Good Idea What Customers Will Purchase Next

Food and beverage companies use Big Data for “market basket analysis.” Market basket analysis is a technique which predicts the most obvious item that a customer is likely to purchase next based on her purchase history and the items already in her cart. Food retailers and restaurants use these projections to create effective combo deals and improve their marketing messages. For example, if the market basket analysis identifies that a customer prefers a muffin with her coffee, then it can create a combo to help her enjoy them together.

The Home Depot is the unchallenged leader in the home improvement retail sector in terms of applying Big Data to advantage. The Home Depot collects data from its own website, promotional emails, and social media. It uses that information to drive traffic to its stores by improving their marketing programs. As a result, The Home Depot is beating investors’ expectations and is described as “Amazon-proof.” Interestingly, this is happening at the same time many retailers are struggling to connect with their customers and deliver meaningful results to their investors.

The Home Depot Will Spend $4 Billion Over Three Years on Big Data

The Home Depot is spending roughly $4 billion from 2016-2018 to improve the company’s e-commerce platform and physical stores and bolster the link between the two. The Home Depot is creating a system that allows customers to easily order what they need online, have their employees collect these orders in store, and let their customers drop by the stores to pick up their purchases in moments. This buy-online, pickup in-store (BOPIS) model has proven vital for The Home Depot. According to the 2016 Internet Retailer’s report, about 25% of The Home Depot’s $3.76 billion in total website sales, or nearly $1 billion, came exclusively from their BOPIS program.

The Home Depot Uses Big Data to Reconfigure Its Supply Chain

Using technology and Big Data to rethink The Home Depot’s supply chain has been a key part of the company’s success within the home improvement industry. It will prove increasingly vital as the company moves forward. The Home Depot has used Big Data to improve its supply chain several ways:

• Dynamic ETA, which gives customers delivery data and delivery estimates based on their exact location.
• Sync is a multi-year project that will reduce shipping and inventory costs through better coordination between stores and distribution centers.
• Its Customer Order Management System helps balance store and web inventories. It also enables buy-online, pickup in-store customers to choose the store with the shortest wait time for pick up rather than requiring customers to choose stores only by location.
• An easy-to-use website and mobile shopping platform will make the customer experience more seamless while allowing The Home Depot to better collect customer data. It will use that data to further improve its Big Data initiatives

Wayfair Is a Winner in Home Improvement Due to Big Data

Home goods e-commerce company Wayfair was created in the digital ecosystem of 2002. Since then it has thrived due to its consistent commitment to and use of Big Data. In 2016, Wayfair introduced a search with photo capability. This capability taps into Wayfair’s Computer Vision System. This Vision System is based on the company’s own machine learning techniques and its massive proprietary data sets. This system allows customers to upload images of furniture they are looking for and Wayfair will give customers search results that match the image provided as closely as possible.

This data collected from this visual search feature creates a powerful feedback loop, which makes Wayfair’s results more useful for customers in the home improvement industry. Wayfair measures the impact of its photo search system by tracking the number of loyal repeat customers. In the second quarter of 2017, the numbers of orders per customer and repeat customers both increased year-over-year. The number of repeat customers grew to be more than 61% of total orders in the second quarter of 2017; this compares well to the 58% in the second quarter of 2016. Repeat customers placed 2.6 million orders in the second quarter of 2017, an increase of 55% year-over-year. These increases in repeat customers and their orders is testament to the effectiveness of the Big Data driven photo search capability.

Big Data is everywhere in the Retail Industry. It would be be hard to find any part of the management of retail operations that is not deeply touched by Big Data. In fact, it is already clear that to survive in the Amazon era, all retailers will have to rely heavily on Big data to help them store the right merchandise, at the right times, in the right quantity, and at the right price. Those that ignore it will die.

This does not mean that only the large companies that have the resources to exploit Big Data will thrive while small companies will die. Small companies will be able to harness the power of Big Data – but they are likely to do so through niche consulting firms that have developed the professional and technical skills, hired a stable of experts, built the computing platforms, and acquired access to the massive data required to operate effectively in this field. Smaller retail outfits that buy their services will find them expensive – but the benefits should far outweigh those costs.

The biggest costs will likely not appear on the company’s financial ledger. The biggest costs will be the time, focus, and energy that senior and middle management will need to invest to come to grips with how to leverage Big Data and how to build investment arguments that make sense. This last point proved to be a major stumbling block when general data processing began to make inroads into large and then medium sized companies some 40 years ago. It is quite liable to prove to be a stumbling block in the application of Big Data as well.

Online and Store General Merchandisers

With regards to department stores, the most promising use of Big Data is through recommendation engines.

Recommendation engines — Recommendation engines use the historical purchasing decisions of customers to predict future purchases and recommend other products to customers that they may be interested in. Big Data using these engines have the potential to generate accurate product recommendations to customers before they even leave the webpage. Amazon, for example, sees a 30%-60% revenue uplift due to these recommendations alone. These recommendation engines are a widely-used way of incorporating Big Data into department stores because they are easy to implement and have an immediate positive impact on revenue: recommendation engines are shown to have the potential to boost revenue by 24% on average.

Trend Forecasting — The second biggest use of Big Data in department stores is predicting trends and forecasting demand. Trend forecasting algorithms comb social media and web browsing habits to find what products and services are causing buzz. These algorithms also analyze ads to see what products marketing departments are pushing. The algorithms then compare the data gathered from social media with the data gathered from current ads to accurately predict what the top selling products for a given quarter will be, how to better market products, and how to develop more cost-effective marketing strategies.

These predictive algorithms assist retailers in making better informed decisions about stocking and product ordering. This capability is particularly helpful during the holiday season when shopping rates increase – machine learning can use past historical shopping data to forecast future purchasing and revenue outcomes. It is anticipated that this kind of predictive analysis in department stores will grow from a $2.7 billion global market in 2015 to a $9.2 billion by 2020, a CAGR of around 27%. In the US alone, predictive analysis from big data is expected to reach a $3.6 billion market by 2020. As of 2015, less than 25% of department stores had adopted predictive analytics. Between 2018 and 2020 this is anticipated to grow to 70%.

After identifying trends, Big Data (particularly in regards to customer economic and geographic information) can be used to understand where and when this demand will come from. This helps business to generate effective marketing and advertising campaigns. For example, Ozon.ru (Russia’s first online retailer) analyzed that demand for books rises when it gets colder during the winter months, and thus increases the number of book ads their customers see. This ability to accurately forecast demand, that comes with using Big Data, is helpful in lowering a business’s costs, as it is expensive to keep excess inventory on shelves and having too little stock drives down revenue and decreases customer engagement and loyalty.

Price Optimization — The third main use for big data in department store retail is optimizing pricing. In retail, Big Data can be used to help assist in determining when prices should be dropped (marked down optimization) or when they can be raised without customer dissatisfaction (reflected by a lack or reluctance to purchase). Previously, before the advent of Big Data, markdowns occurred at the end of the buying season, with stores hugely discounting their remaining merchandise. The problem with this approach is that demand is already gone by the time that markdowns occur. Big Data analytics demonstrate that what is actually most effective in increasing revenues is to gradually lower prices once demand initially begins to decrease. When the US retailer Stage Stores employed this technique, it could increase its traditional end of season sales revenue over 90% of the time.

Weather Optimization — Big Data is particularly helpful in optimizing prices in accordance with weather conditions. The Weather Company (part of IBM) has found that “weather is one of the largest swing factors for economic and business performance” – 60% of shoppers change their behaviors when it is either raining or it is hotter than average out. A 1^o F drop in temperatures below 60^o produces a 2%-3% drop in apparel sales. Approximately 60%-70% of a retailer’s excess expenses are due to weather-impacted supply chain costs (e.g., trucks held up due to poor weather conditions). In the UK, if temperatures reach over 65^o F there is a 22% rise in fizzy drink sales, a 20% rise in juice sales, and a 90% rise in lawn furniture sales. In the US, temperatures below 64^o F increase sales in soup, porridge, and lip care. Food, drink, pharmaceutical, and apparel sales are the categories most impacted by weather.

Targeting Individual Consumers — The final use of Big Data in general retail is identifying individual customers and how to most effectively market to and target them specifically – whether through email, text, or location-based alerts. Retailers, for example, can install sensors in their stores to identify customers’ locations through their smartphones. If a customer’s smartphone’s WiFi is turned on, it will attempt to connect with the store’s internet and this is how a customer’s location can be sensed and tracked. Retailers can then track what specific stores she visited, what departments she visited, and what products she purchased at what time and on what date. This information can be used to better understand each customer’s movements and patterns when it comes to shopping. Retailers can then use this information to reorganize their stores to optimize customers’ shopping experience and even to offer special deals and coupons to bring further business to their stores.

General Online Retailers

In addition to the Big Data applications listed above, there are four other applications that apply specifically to online retailers.

Dynamic Pricing — Dynamic pricing is Big Data at its finest. Dynamic pricing is highly responsive to external factors such as consumer demand and competitors’ prices. Dynamic pricing collects trend data about which products are being bought to automatically adjust prices. Its analytic capabilities slowly increase prices on items that are popular and discounts prices on items that are less popular. Dynamic pricing is key to increasing online retailers’ overall revenue.

Individual Customer Experience — Big Data analysis gives sellers insights about customer behavior and demographics and provides customers a personalized experience. For instance, customer data can be used to create buyer-specific e-mails for promotional campaigns. For example,  Amazon’s “Customers who bought this item also bought…” recommendation feature increased sales nearly 30% when it was first implemented. This is a simple and remarkably effective way to keep customers on a retail site and keep them buying. Consumers might have reservations about their favorite retailers knowing intimate details about their lives, but they’re going to love the results in practice. Sharing all those personal tidbits is helping companies like CNA identify fraud and prevent customers from having their identities compromised. Retailers can use information from live transactions and other sources (such as social feeds and geo data from apps) to prevent credit card fraud in real time.

Better Quality of Products — Amazon is the e-commerce standard when it comes to smart, effective pricing. It can easily access its competitors’ pricing data and respond quickly with its own deals — changing some items’ prices up to 10 times a day. The industry-wide shift to dynamic pricing means that companies will no longer be competing on price alone. They will now need to establish a reputation for offering their customers the best value and the best experience.

Reduce incidents of shopping cart abandonment — Companies can also use cross-device tracking to reduce shopping cart abandonment rates. EBay research found that the average consumer uses as many as three or five devices or platforms during the course of her buying journey. Mapping this journey with data allows retailers to help their customers’ transition from one device to the next and complete their purchases.

The next few years are going to see an explosion in the rate at which detailed data is collected about the moment-by-moment operation of nearly all new cars. This data will be stored and collated in centralized databases that make Big Data analyses possible. McKinsey published a report in 2014 that estimated that the global market for connectivity components and services for cars was $38 billion that year. The report went on to project that the data-driven connected car industry would grow to $215 billion.

Big Data Will Transform Fleet Management

Fleet management will enjoy the greatest benefit from this Big Data analysis. Auto makers will now be able to determine which settings and features drivers actually use. This will help them improve their marketing. It will also identify the features that drivers really care about; this will focus the auto makers’ on-going R&D efforts.

Further, automakers can easily monitor their cars, identify potential problems, and issue maintenance calls. This will help maintain their fleets in peak performance. They will be able to identify drivers who are abusing their cars; they can issue advisories based on that information. All of these efforts are geared to minimizing the maintenance costs and maximizing the performance of their fleets.

Big Data Is Transforming At least Five Other Auto Practices

City Planning — City planners and engineers can use this same data to improve their plans for roadways and traffic flows.

Onboard Navigation — Navigation systems can use real time driving data to discover and display the fastest routes based on current traffic patterns.

Insurance Rates — Insurance companies can access the Big Data collected from connected cars to monitor each driver’s performance and, potentially, use this information to adjust rates and to determine what really happened in accidents.

Auto Dealer Marketing Campaigns — Dealers can use this Big Data to assist in planning their marketing campaigns. For example, Bullseye Prospecting is a product that helps dealers and their marketing agencies automate their marketing campaigns by leveraging third party and internal data on consumer behavior, incentives, and vehicle equity/valuation. This prospecting tool can cut the $600-$800 average per-car cost of sale by about 30%. It also helps dealers by sending a detailed, personalized message to their best customers at precisely the right time to prompt sales and services.

Used Car Valuations and Inventory Management — The 2016 Black Book survey indicated that nearly two-thirds of dealers are using 30%-50% more data since 2014 to establish vehicle valuations. This data also helps them set regional pricing, determine the appropriate supply of cars, and assess each vehicle’s history to manage their inventories. Some 69% of these dealers say the data is giving them better insights on pricing and profitability. 58% say Big Data is providing better insight into managing their inventory procurement. The majority of dealers believe they can avoid a market catastrophe similar to the one in 2008 because the data allows them to make more accurate decisions.

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 and how it can greatly benefit mortgage companies.

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 Streamline Processes at Mortgage Companies

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 and mortgage companies.

2. 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.

3. 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. 

4. 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.

2. Increased need for big data analysts in mortgage companies

Mortgage companies are 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.

3. 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.

4. Automation and Big Data will be important for mortgage companies

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 Mortgage Companies 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, Mortgage Companies, 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: Big Data and Mortgage Companies

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.

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.

There are six ways Big Data impacts the mortgage industry. And what I’m going to tell you about now is just the leading edge of a transformation that that is going to make all the difference between the winners and the losers in this industry.

But before I get into that, let me give you a very brief explanation of what Big Data means for readers who don’t have any experience with it, and how Big Data impacts industries today.

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.  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 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 opens up new and exciting opportunities in the mortgage industry. I’m going to tell you very briefly about six of them.

1.Account Origination and Underwriting

The first is in the area of account origination and underwriting.  In the past, we only looked at the information applicants gave us on their mortgage application forms.  Of course, we would verify the information whenever we could by contacting third parties. But the fact is that we didn’t – and couldn’t – go beyond that.  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.

There are many millennials who don’t use banks as much as their parents did. This means they don’t 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.

Big Data impacts the mortgage industry by giving us the tools we need to mine data sources that were unavailable before.  I’m 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’ll be able to make better informed decisions about approving mortgages.

2. Big Data Impacts Account Servicing

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.  We don’t have any way to know that an account is in distress until the payments start coming in late – or don’t come in at all. On the other hand, we don’t know that a couple is ready to move up to a larger house and a larger mortgage until they file a new mortgage application.

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.

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’ 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.

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.  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.  Or maybe you should knock on her door to let her know that you’ve already qualified her for a larger mortgage.

3. Cross Selling

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.  Here’s 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.  A bank that can build a single view of its customer – what bankers call an SVC – can cross sell financial products that include a mortgage, a car loan, and a child’s education fund.

But let’s 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’s fee or a referral commission.  This wouldn’t be possible without Big Data.

4. Risk and Regulatory Reporting

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’t run afoul of these programs. But here’s the catch: when mortgage companies are managing their day-to-day business, they’re 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’s 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.

5. Mortgage Fraud

Mortgage Fraud is a fifth opportunity area. Here I’m 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’re going to come across a fraudulent transaction – and that can be expensive. You are far better off if you can catch the fraud early on and deal with it immediately.

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.  The Big Data algorithms will run in the background and let your staff get on with the business of approving your mortgage applications.

6. Corporate Acquisitions

The last area I want to talk about is corporate acquisitions. Think back to the period right after the 2008 collapse.  At that time, we saw Bank of America buy Countrywide Financial and then go on to lose $40 billion from that little acquisition.  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.  Within a couple years it became clear this was not a very good business decision at all.  In fact, JP Morgan eventually sued the Federal Deposit Insurance Corporation over that purchase for non-disclosure.

If these companies had access to Big Data tools and used them wisely, they could have avoided these debacles.  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.  It’s not as though anybody was trying to hide anything.  All the information was there for anyone to see.  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 – or even a team – to sift through the material and highlight the problems that would be evident if you just knew where to look.

In Summary: Big Data Impacts

In summary, I think it’s clear that we already have some compelling evidence that Big Data impacts the mortgage industry significantly when it has been applied wisely.  These initial successes are going to spur others on to use this rapidly developing technology more widely in their own companies.

If you want to learn more about this, I urge you to read what Vamsi Chemitiganti from Hortonworks has written on the subject.  Or 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.