The Evolving Ecosphere of Consumer Data

Before technology transformed the financial services industry, traditional credit reporting agencies (CRAs) captured mainly traditional credit data on consumers for their databases and solutions. Lenders typically only reviewed this traditional or conventional credit data when evaluating a consumer for a loan. In addition to their mainstay header information (name, date of birth, address and social security number), consumers’ files consisted almost entirely of conventional credit activity, such as credit card, mortgage and auto loans, and subsequent credit amounts/payment history on these loans.

Traditional credit data provided by CRAs is governed by the Fair Credit Reporting Act (FCRA), which governs the collection of credit information and access to consumer credit reports. All users must have a permissible purpose under the FCRA to obtain a consumer report.

Traditional credit data has been used for FCRA permissible purposes such as credit decisioning, account management, collections, and prescreen marketing applications.

Yet this traditional data-only model excluded a large segment of consumers that did not use traditional credit loans and banks – “non-prime” consumers with little credit history or largely unestablished standard lines of credit. These consumers, while not prime, are still considered primarily mainstream and creditworthy, as 99 percent of non-prime loan applicants have a checking account, and more than 90 percent have a direct deposit.

As the credit market began to become flooded with more and more competitors vying for the same consumer segment, lenders and auto financing companies had to begin to dig deeper, considering more consumers and becoming savvier in how they considered the consumers to whom they extended credit. As such, lenders began to look at consumers’ files a bit differently. Traditional data alone was not telling the full story – or at least not nearly enough of the story.

Enter alternative data. More data on consumers began to emerge, shedding light on the consumer’s ability to pay, providing affirmation or contradiction related to the consumer’s willingness to pay and highlighting assets a consumer may have to leverage as collateral. Alternative data points include non-traditional lending channels outside of traditional credit bureau data, like pay day loans and club or magazine subscriptions. In addition, alternative data assets such as checking and debit information, property, tax and deed records can also help add depth to a consumer’s credit file and can have tremendous impact on business growth.

With some adoption of alternative data in the ecosphere of consumer data – it still did not convey enough of the picture of today’s consumer. Enter alternative credit data, which is helping to complete the picture and truly identify potential high-opportunity or high-risk performers. This next generation of data introduced tradeline data on predominantly non-prime consumers. This includes data heretofore excluded, including non-prime, short-term loan tradelines, ACH payments, employment attributes, income, payroll type and payroll frequency. Generally, alternative credit data, like traditional credit data, is used for FCRA permissible purposes, such as credit decisioning, account management, collections, and prescreen marketing applications.

Trended traditional credit data scores are newer models that provide deeper insight into an individual’s changes with credit usage and payment behaviors over time. Trended credit data can incorporate more than two years of account history.

The most successful lenders in today’s ever-changing landscape are the lenders that have embraced the use of all these segments of data – traditional, alternative, trended and alternative credit data. These lenders get a complete view of the credit and financial profile of individuals, allowing them to make the best decisions about their customers, ultimately enabling lenders to be more profitable and helping to enrich the financial profile for many consumers.

Randy Bobb is the vice president, Sales, Auto Finance with FactorTrust/TransUnion. Bobb has more than 20 years of experience with information services, software, analytics and consulting. He is responsible for leading the auto finance sales team at FactorTrust around risk management solutions for underwriting, account management, servicing, and marketing for large auto finance companies, captives, banks, credit unions and buy-here, pay-here companies. Connect with him at rbobb@factortrust.com.

paryy-marchapril

Get your Credit Program in Shape

In September of every year, the Information Management Network puts on an asset-backed securities conference in Miami Beach, Florida, at the world famous Fontainebleau Hotel. The conference events are fantastic; the hotel is amazing and the beach is truly something to behold. For many years, I attended that conference faithfully in order to support my company’s efforts at promoting their bonds (cough, cough).

My business partner and I would always head to the conference early so that we could enjoy the surroundings before we were stuck in meeting rooms for 10 hours a day. Of course, going to South Beach is a lot of pressure in and of itself. With the obvious exception of the conference attendees, everyone walking around there is aesthetically perfect. I would plan my diet and workouts meticulously, and as the year progressed they would look something like this:
• January – “If I can lose 2 pounds per week, I will be in great shape by conference time.”
• April – “If I can lose 5 pounds per week, I should be able to come close to my conference goal weight.”
• August – “If I can drop 50 pounds by the end of this workout, I might make it.”

I suppose that is why Mark Twain said, “New Year’s Day–Now is the accepted time to make your regular annual good resolutions. Next week you can begin paving hell with them as usual.”

While I joke about my prior resolutions, being in good physical condition is extremely important. Adipose tissue (fat) requires blood flow to maintain, creating a resource drain on the body, which can lead to high blood pressure. Excess weight puts tremendous stress on the joints, leading to a higher risk of osteoarthritis. In addition, obesity can cause problems with the performance of the heart and brain.

In much the same way, an out of shape credit program can cause serious ripple effects throughout the whole organization. Many lenders looked to 2018 as a year where they would shore up the bottom line, meaning they put the focus on profitability over volume. Like those who are now reassessing their New Year’s resolutions, smart lenders will evaluate which activities are helping and hindering the pursuit of their goal.

How fit is your program?
An out of shape credit program leads to a number of negative consequences that erode profitability, not to mention dealer satisfaction. Recognizing that your credit program has deficiencies requires that the lender be able to to connect application data from the loan origination system to performance results stored in the servicing system.

With that link in place, data may be analyzed that confirms or refutes the value of each component of the program. There are generally three factors at the root of a sub-optimal credit program. These are irrelevancy, redundancy and imbalance. They coalesce to produce poor closure rates, bloated expense and inconsistent credit performance.

Irrelevancy
Irrelevant factors are those rules in the credit program that are believed to be important, but in fact do little to help the company predict performance. Without good data and analytics, this is the most difficult error to overcome. Most judgmental buying programs emanate from the lender’s resident credit expert, who is typically a senior manager, or founder, with a tremendous amount of lending experience.

Many of the rules developed by the company expert are both intuitive and valid. However, many others do not hold up when compared to actual performance. I will borrow from Mark Twain again and quote, “The trouble with the world is not that people know too little; it’s that they know so many things that just aren’t so.”
The following examples are drawn from actual sub-prime auto loans with 24 months of credit performance. The data comes from a random sample of credit bureau records with the following characteristics:
• Credit score < 600
• Lender type: Finance company (as opposed to a bank or captive)
• APR ≥ 18 percent
• Amount financed between $5,000 and $30,000

This profile is typical of sub-prime paper below a 600 score, and demonstrated a 24 percent default rate over the first 24 months on books. The sample contained 300,000 records of the credit and performance attributes of actual auto loans.

A classic case of irrelevancy is found in deferred student loans. While it is true that, historically, deferred student loans would have the effect of inflating the credit score, it is not true that people with a large number of student loans, or deferred loans, are a materially worse default risk than those who do not have them (refer to the student loans chart). Certainly, if the only credit an applicant has consists of deferred student loans, the lender should be careful; however, there is no meaningful drop off in credit performance as the number increases.

Another example is the number of charged-off accounts in the last 12 months (for consumers with no bankruptcy on file). Those with no charge-offs are modestly better than those with only a few, but for consumers with four or more, the charge-off rate is lower. Does this mean lenders should pursue applicants with many recent charge-offs? Of course, not – the consumers with more charge-offs also have many more years in the file and have many other accounts that have paid.

One of the most powerful attributes in the credit bureau file, as least as it relates to auto loan charge-offs, is the Average Months in the Credit Bureau File. Loosely defined, this characteristic measures the average number of months that all trades have been on record. As can be seen in the chart, those with fewer average months in file have a dramatically higher default rates, and vice versa.

I have seen many lenders scratch their heads when an account with recent charge-offs receives a high credit score. The reason for this is that the thickness of the credit file, combined with positive past history, compensates for some of the risk of a recent default. If one takes two applicants with a recent credit default, the risk is entirely different based on the total body of their credit histories. Refer to the chart on the bottom right of the graphic shown on page 15.

Including factors in your credit program that don’t correlate very well with actual performance leads to unacceptably low approval rates, and ultimately poor closure rates. Worse yet, forcing good scores (or other highly correlated factors like Average Months in File) to conform to irrelevant rules will quickly degrade credit quality.

Redundancy
I consulted with many lenders over the past few years, and the most common problem I see in credit programs is that they are far more complicated than they need to be. Most credit attributes are not independent of other factors, meaning that people with more tradelines also have been in the file for a longer period of time. Applicants with higher revolving credit limits have earned them by paying bills consistently. Consumers with numerous medical collections often tend to have an unstable employment history. Almost everything that can be measured on a consumer is in some way related to a large number of other attributes.

The purpose of a lending program, essentially, is to predict credit performance. Each tier has its own pricing and loan structure parameters, set with the expectation of making money. No rational lender would knowingly set a price that would leave the company under water. Unfortunately, this happens all the time because either the lender does not know what their losses should be, or the program itself is volatile.

Credit programs become overly complex for several reasons. First, credit managers want to account for every scenario, leading to dozens of redundant factors. Second, the desire to keep up with what the dealer says the competition is buying leads to additional rules designed to fish out the best of the deals the buyers were formerly turning down (or putting in lower tiers). Finally, when lenders start to grow, the number of exceptions naturally increases. Poor execution usually results in credit surprises down the road.

As a rule, the more factors that go into a predictive model (particularly redundant ones) the less robust it will be. I have seen programs where lenders have specific rules about the number of 30, 60 and 90 day past due tradelines within a specific period. That is completely unnecessary, as they are all measuring varying degrees of the same problem. The company should determine which one specific attribute is the most correlated to default, and simplify down to a single rule. Other lenders have rules about the payment patterns of auto loans, all installment loans and revolving trades. Again, this is likely overkill. Find the best predictor and simplify the program to achieve more stable results.

Imbalance
An imbalanced program is one where too much focus is placed on some factors, while not enough is placed on others that significantly affect net losses. For example, I often see programs where little is known about how many consumers are likely to default in each tier, but tremendous attention is given to vehicle risk. This is backwards, as the recovery value only matters if the company has to repo the car.

Consider the case where the lender has a 30 percent default rate and a 40 percent recovery rate. Assuming no principal paid down, a $10 million portfolio would have $1.8 million in net losses ($10mm x 30% x 60%). Improving recoveries by 500 basis points would change the net losses to $1.65 million ($10mm x 30% x 55%). However, improving unit losses by 500 basis points would change the net loss figure to $1.5 million ($10mm x 25% x 60%).

The default rate has the most influence on losses, and must be given the greatest attention in the credit program. Other factors, such as limits on LTV, mileage and down payment should be scaled based on the default rate in each program tier. In other words, give the less risky consumers more latitude – which will lead to a more competitive program and a higher closure rate.

Getting in shape
The benefits of weight loss and cardiovascular fitness are well documented. Increased mobility and strength, reduced risk of disease and improved mental ability are but a few of the benefits. Likewise, a fit credit program requires fewer resources in terms of headcount and system capabilities. Furthermore, approval rates and decision speed will increase. Finally, the company will see both reduced (and more predictable) credit losses. All of this leads to lower operating expense and higher profitability. Now is the time to review your program in order to eliminate irrelevant rules, select the best of the redundant ones and place the emphasis on the factors that contribute most to the bottom line. As for me, I will focus on getting back into conference shape.

Daniel Parry is co-founder and CEO of TruDecision Inc., a fintech company focused on bringing competitive advantages to auto dealers and lenders. He is also co-founder and CEO of Praxis Finance, a portfolio acquisition company, and co-founder and former chief credit officer for Exeter Finance Corp. For questions or inquiries, please email danielparrynaf@live.com visit www.trudecision.com.

Never Say Never: Confessions of a Woman in Technology

In the car the other day my kids started listing all the things they would never do. “I will never smoke a cigarette. I will never drink a beer. I will never get my nose pierced.” And the lists went on until I told them, “I have learned to never say I will never.” I’ve found that somehow, some way the act of saying “I will never” seems to propel events into happening.
They asked for examples.
1. “I’ll never have less than 10 kids” (I did).
2. “I’ll never be divorced” (shocked me).
3. “I’ll never get remarried” (twice).
4. “I’ll never move to the suburbs” (I did).
5. “I’ll never move specifically to one particular Texas town” (here I am).
6. “I’ll never get fired” (did that, too).
7. “I’ll never go back to school” (it’s now on my bucket list).

And then one hit me: “I’ll never think that men and women in the workplace think or act differently.” (I do now.)
I’ve been incredibly blessed with amazing mentors my entire career – men and women. But everywhere I turn lately – from Magdalena Yesil to Mary Barra to Hollywood scandals and corporate messes – people are talking about women and men in the workplace. It made me sit back and ponder. Why do people care so much? Why is it such a hot topic?

In business, we all believe in hiring the best person for the job regardless of gender. But if it’s just about hiring the best person for the job, why are so many organizations working so hard to get women in leadership positions? Do women get different results or is it just a matter of balance and fairness?

In the general business place, 57 percent of women versus men hold professional positions. In technology that isn’t the case. Technology is all about process, efficiency, improvement, and organization – all aptitudes of women. These numbers vary from report to report, but here’s what we found in looking at the National Center for Women & Information Technology, USA Today.
• 25 percent of women work in fields related to computing/technology
• 18 percent of software developers are women (higher than I thought as we can’t seem to find any!)
• 14 percent of women were named to the boards of tech companies

Interesting facts, but not reasons that compelled me to think or do anything differently.
And then one day, I discovered what, for me, was the missing piece of the puzzle, the one thing that helped me pull it all together and realize the importance of diversity.
I recently went to a private equity conference attended by many venture and private equity firms and companies like ours. There were, I think, over 100 people and I was one of only two females in attendance (both from our company). The guy leading the conference was great – you couldn’t help but like him. Nice. Down to earth. Helpful and smart. He stood in front of the group, talking during one session and made a comment that virtually everyone in room agreed with and laughed at. And I did not. And the reason I did not was largely due to my gender.

In this moment, it hit me why it’s helpful to have both male and female voices leading organizations. Diversity of opinion leads to better discussions and better discussions lead to better results

I thought about our own company. Our initial board of advisors was half women, half men. Our current executive team is half women, half men. I didn’t plan it that way. I didn’t go out and seek that balance. I did seek to balance out the types of people and opinions. For the board of advisors, I sought out an expert in operations, in marketing, in risk and in technology. For the exec team, I looked for people with different skill sets than me. In hindsight, the varying perspectives have helped remarkably.

I will continue to consider the importance of different perspectives in the workplace, because in all fields – related to technology or not – diverse team members working together and challenging each other get to the best results.

Stephanie Alsbrooks is CEO of defisolutions.

Opportunities Uncovered with Alternative Credit Data in 2018

When it comes to helping auto financing sources improve profitability in the new year, one crucial element is alternative credit data. Several years ago, this type of data was still in its infancy. But in 2017, we saw alternative credit data begin to turn the corner. Now we are seeing many lending institutions beyond traditional finance companies not only considering its impact, but fully embracing alternative data. Use cases have shown that alternative credit data has helped with loss mitigation and pricing, as well as expanding the consumer universe for financial service providers. While it wasn’t long ago that alternative credit data was considered an emerging concept, it is now becoming increasingly more mainstream.

In 2017 we also saw ongoing declining originations among auto finance companies. This decline began in the third quarter of 2016. As a result, lenders are pulling back in the sub-prime market, with a corresponding reduction in the lending community on prime, near prime and sub-prime originations.

Reduction leads to gaps, new players revealed
With a reduction in originations, a gap is exposed. This gap presents itself as an opportunity as we move into the new year. Some prime plus and super prime lenders are investigating opportunities within the near prime consumer market. With the reduction of lenders in the near prime and sub-prime markets, newcomers now have the ability to price appropriately, while buffering some risk exposure.

chartNear prime sweet spot
As lenders are finding out, near prime can be a true sweet spot if they can make a more informed credit decision, offer competitive pricing, and remain efficient with their servicing strategies. Also, near prime often includes used vehicles which are of interest to dealers because of high inventory levels. In addition, with the change of strategy to exit sub-prime lending by major full-spectrum lenders (those who lend to all consumer categories), opportunities have been created for others to gain new market share.

This new lending opportunity can be a welcome offset to some of the potential unfavorable trends anticipated for 2018, including increasing interest rates and slowing vehicle sales. Although, higher interest rates can mean financing sources are able to charge more and, if done right, generate more revenue. In fact, the auto industry reached new heights in terms of total balances ($1.17T) and total number of borrowers (83M) in 2017 (Q3).

Credit unions looking to buy deeper too
Many credit unions are looking into the near-prime segment, as well. They are evaluating and validating the same opportunities that other lenders are considering. The underwriting process can be different for them because they spend more time getting to know their members, and in turn, these consumers become more familiar with the credit union. Credit unions also tend to have a large interest in the refinance market. Consumers in this market are becoming increasingly savvy and taking advantage of competitive credit opportunities. One example: a consumer buys a car from the dealer, then goes to a credit union shortly thereafter to get a better, lower rate. Meanwhile, the consumer has benefitted from receiving a higher trade-in amount on the front end and a lower rate on the back end.

Many of these lenders in the past would not have been as aggressive to enter the market were it not for alternative credit data. With alternative credit data, they now have the ability to underwrite consumers at almost all credit levels, or close to it. By having more information than they’ve had in the past, they are better equipped to find these opportunities and to price more appropriately. Alternative credit data is typically defined as unique tradeline, employment, banking, or contact data reported by alternative lenders that generally have not reported this data to traditional credit bureaus in the past.

Specialty finance companies that get funding from secondary markets were among the first to embrace alternative credit data. Most of these companies have also primarily focused on near prime and sub-prime segments. Now we are seeing other traditional companies, such as banks and captives, considering alternative credit data. It is in the next state of adoption. One example is the recent announcement by a top captive auto finance company to look beyond traditional credit scores to boost sales – including specifically looking at alternative credit data and applicants with limited credit histories to help accomplish this goal. The two-year pilot will research what alternative credit data attributes work best to most effectively set pricing and evaluate risk appropriately.
The changing market conditions have impacted these lenders and will likely motivate other lenders to be more effective and efficient with their underwriting and pricing strategies and to embrace new technologies. With sub-prime and full-spectrum lenders, their first objective is to understand the complete financial view of a consumer’s obligations, past performance and current stability. Alternative credit data can help with this key objective and provide a more full credit picture of consumers, allowing lenders to identify pockets of profitable consumers across all credit tiers—especially in sub-prime markets. As a result, lenders have opportunities to serve more consumers looking to build better lives.

Brian Landau is the SVP and auto business leader, TransUnion. Landau joined TransUnion in 2014. He is responsible for executing TransUnion’s growth strategy for auto and providing thought leadership to the market. Landau has more than 15 years of experience in general management and management consulting, working for and serving many of the largest financial institutions in North America.

Randy Bobb is the vice president, Sales, Auto Finance with FactorTrust |TransUnion. Bobb has more than 20 years of experience with information services, software, analytics and consulting. He is responsible for leading the auto finance sales team at FactorTrust around risk management solutions for underwriting, account management, servicing, and marketing for large auto finance companies, captives, banks, credit unions and buy-here, pay-here companies. Connect with him at rbobb@factortrust.com.

alsbrook

2030 Auto Time Travel – Take the Survey, Drive the Future

Right now, any one of us might reasonably and accurately predict the future. Say we are talking about paper statements. One day, very soon, they won’t be an option. We see it coming. Getting bill reminders texted to our phones. Typing YES and bills are paid instantly. At some point, service providers will move 100 percent away from the once genius paper statements to the now near standard electronic reminder. But in 1980, most of us wouldn’t have predicted that.

When I sat in my ’82 Chevy Beretta with my first bag phone, I didn’t see how that device would invade all aspects of my life, and certainly couldn’t see the future of paying bills from it. When I got my first text reminder, or even as little as a few years ago, I didn’t dream that I would one day click a PAY NOW button and take care of a bill without even logging into a portal and providing a username and password. We might have been saying “computers will rule the world,” but we didn’t really have an idea of what that meant.

Little by little and by leaps and bounds we are making progress. The closer we get to something, the more real it seems.

I’ve spent nearly my entire career in the auto finance industry, thinking and talking about “what is” versus “what should be” and “what could be” and “when.” We all feel powerless at times, dictated by the needs or whims of dealers and other factors outside of our control. Whether it’s perception or reality, feeling controlled can lead to a state of complacency. Complacency can slow our forward motion. And now is not the time to slow down.

Change is coming – especially in the auto industry. It’s just a matter of what and when. So, I thought it would be fun to try something.

Take a look at the list of questions below and predict what you think will be affecting our industry in the future. If you are reading this in the print magazine, clip it out, write in your answers, and photograph or scan it and email to me at salsbrooks@defisolutions.com. If you are reading this online, click the link below and take the survey, and watch for a follow up blog at blogs.defiSOLUTIONS.com.

What do you think 2030 is going to look like?
1. What will be the average age to get a driver’s license? (In 2014, just 24.5% of 16-year-olds had a license, a decrease from 1983 when 46.2 did.)
2. What percentage range do you think is most accurate in the year 2030 for each of the bulleted questions below?
• What percentage of cars on the road will be self-driving?

Experts today predict autonomous vehicles will help curtail the number of lives lost in accidents. But can Americans give up control?
• What percentage of Americans will own a car?
Currently it’s over 90 percent.
• What percentage of cars will be sold through a traditional dealership?
According to Autotrader, car buyers spend 59 percent of their time researching online; the majority use third-party sites. While 81 percent of car shoppers give the test drive portion of shopping an 8-10 on a satisfaction scale of 1-10, after an average of three hours spent at a dealer, the customer satisfaction rate drops to 56 percent.
• What percentage of prospective first-time buyers will actually purchase a vehicle (not just lease or rideshare)?

Millennials currently own fewer cars than previous generations, but some say they were simply late bloomers or impacted by the Great Recession or school loans and are now buying cars in big numbers.)
• What percentage of prospective first-time buyers will lease (not buy or rideshare)?

According to Edmunds, “Over the past five years lease volume grew by 91 percent and in 2016 accounted for 31 percent of all new vehicle sales, up from 29 percent of sales in 2015.”
• What percentage of first-time buyers will rideshare (not buy or lease)?

According to Kinsey & Company, Lyft and Uber accounted for 30 million vehicle miles travelled (VMT) in 2013 and 500 million VMT in 2016. Still only one percent of overall VMT.
• What percentage of cars will be electric?
According to “The Economist,” in 2018 the electric car will be cheaper to own and operate and as “hip as the miniskirt once was.”
• What percentage of loan documents for auto will be fully 100 percent electronic, never printed?
For auto loan documents, current technologies make it possible to prepare, send, sign, and maintain all loan documents digitally. While the true percentage of total is unknown, according to Jeff Belanger, RouteOne’s senior vice president of Business Development, 40% percent of overall eContracting eligible contracts are booked electronically. Ford and Toyota lead the industry at a much higher rate.

3. In 2030, how many lenders will a dealer’s system send one application to?
Currently one app is reviewed by 2-3 prime lenders and 4-5 sub-prime lenders.

4. Innovation pushed Blockbuster out of the brick and mortar movie rental business. Which auto related lines of business will be the next “Blockbuster”? (Rank from most likely to least likely to survive.)
5. Forget about cars – self driving or not – what will be the next mainstream mode of transportation?
6. Would you mind telling us the year you were born?

When you sit back and do the math, you realize that 2030 is a mere 12 years away. How much can we change in one decade? A lot. We’ve seen it happen. We’ve made it happen. Let’s continue to work together to predict and drive what “what could be,” “what should be,” and “when.”

Stephanie Alsbrooks is CEO and chief soothsayer of defisolutions. E-mail Stephanie at salsbrooks@defisolutions.com.

parry

Disruption! How Blockchain Technology will Impact Consumer Lending

Blockbuster Video opened its first store in Dallas, Texas, in 1985. Over the next 20 years, they added more than 9,000 stores and 58,000 employees worldwide. In 2011, the bankrupt company was acquired by Dish Network for $320 million. The irony of this is that in 2000 former Blockbuster CEO John Antioco passed on an offer to acquire Netflix for $50 million, thinking it was just a niche business. Today, Netflix has a market cap of over $82 billion, while Blockbuster stores are but a memory.

The annals of business are replete with similar stories. For example, Yahoo passed on a chance to buy both Google and Facebook, and spent 2008 thwarting acquisition offers from Microsoft. In 2003, Friendster rejected a $30 million offer from Google, and they were soon after displaced by Myspace and Facebook. Each example illustrates a case where the company had achieved so much success that they did not take emerging threats seriously. Perhaps the old adage should be revised to say, “Nothing impedes like success”.

It is said that everyone has perfect vision when looking into the past. It is a different thing, altogether, to see things in real time. Blockchain technology is one of those things. It is the latest buzzword sensation, and companies are rushing to force fit the suggestion of it into their technology offerings in hopes of obtaining a higher valuation. All of the hype makes it very easy to dismiss, but blockchain technology is already having a profound impact on how lenders will operate in the future.

Blockchain overview
The initial concept of blockchain was published under the pseudonym Satoshi Nakamoto in 2009, and was first deployed as a core component of the cryptocurrency Bitcoin. The model is essentially a distributed ledger, meaning that each transaction is recorded in many locations (nodes). The transactions (blocks) are unalterable once accepted by the chain. Each new transaction must reconcile with a majority of the other nodes, making it virtually impossible to forge. There are numerous other benefits to blockchain technology, which will become apparent when considering how to apply this technology to consumer lending.

There are three basic configurations, which are public, private and consortium (often referred to as federated). In a public blockchain, anyone can send transactions, and see them if they are valid. In addition, anyone can participate in the consensus process. All transactions are encrypted and anonymous, but they are also completely transparent. Public blockchains are the slowest because the records exponentially increase with each new transaction, but they are still faster and cheaper than the traditional process of audit and reconciliation occurring between companies today. The middleman is cut out in the public blockchain, because each transaction must reconcile with other nodes in the network before it is written into the record and assets are transferred.

In a private blockchain, read permissions are kept centralized within one organization. Read permissions may be public or private, and a limited group within the organization is responsible for the consensus process. A consortium operates in the same way, except that multiple organizations share the responsibilities of maintaining the core functions. Private blockchains are significantly faster and less expensive than public blockchains, but are less secure due to their centralized structure (i.e., a single point of failure). The consortium offers a hybrid approach, where there is the speed and efficiency of a private blockchain, but power over the network is not consolidated in one company.

Applications in auto finance
The auto finance industry is large, fragmented and inefficient. A recent Experian Automotive report stated that for used vehicle finance (which represents two-thirds of the U.S. auto finance market), the top 20 lenders only accounted for only 38 percent of the total market share. The twentieth lender had less than a one percent market share, meaning that 62 percent of the auto finance market is made up of thousands of bit players. In addition to the multitude of lenders, there are approximately 60,000 franchise and independent dealers interacting with these companies. The speed, efficiency and security of blockchain holds the promise of numerous improvements throughout the industry. Some of the major initiatives that are presently in development are:

•    Dealer loyalty programs – There are a few constants in the universe. Among them are that dealers want to get paid, and paid quickly. Some of the most successful lenders have developed loyalty programs that incent dealers to create efficiencies by paying for a certain number of contracts, clean funding packages or other factors that drive down cost per acquisition. These programs can be cumbersome to track and time-consuming to administer, minimizing their value to the dealer. Blockchain allows for real time tracking and execution of loyalty rewards, which could have a substantial impact on program effectiveness.

•    Smart contracts – The terms of contracts may be embedded into programs that allow for automatic verification and enforcement, eliminating the need for a legal and/or accounting intermediary to certify. This construct can be fully or partially self-executing, saving a tremendous amount of time and money for all parties connected to the deal.

•    Credit reporting and identity management – A major point of frustration for consumers, and lenders alike, is that the three credit reporting agencies may produce different information on a report for a single individual. That can influence not only the consumer’s credit score, but also whether or not that person can qualify for credit. A blockchain system can resolve this age-old problem, by reconciling all transactions and payments. Furthermore, the ability of this technology to put control of credit information in the hands of the user could literally wipe out identify theft and make data breaches a thing of the past. When enough consumers demand this, it will not be up to the industry to change – it will be legislated.

Preparing for disruption
According to a recent report by Accenture, 9 out of 10 banks in North America and Europe are actively exploring blockchain technology for payments. The report estimates that this technology could reduce bank infrastructure costs by 30 percent. A 2016 survey, conducted by IBM with 400 banks and finance companies, suggests that 65 percent of banks globally will be actively using blockchain by 2019. Change is coming, regardless of whether we in the auto lending space prepare for it.

Consider that electronic documents and e-contracts have been around for nearly 20 years, but very few lenders have embraced them. Amazon, Google and Microsoft have amazing cloud technology on the world’s biggest and fastest servers. Superior technology and service is available from them for pennies on the dollar of what it would take a single company to develop, yet most lenders still build and maintain their own technology infrastructure.

Almost 15 years ago, I remember some senior operations managers I worked with commenting on how Capital One Auto Finance had transformed to analytic, model-driven underwriting (auto-decisioning) from a manual, judgmental process. One of these managers smugly quipped, “They just went out of business!” Of course, that did not happen – the company has done quite well. You might forgive such a myopic comment at that point in time, because the idea was fairly new.

Fast forward to 2012, and some of these same leaders were still laughing off the idea of auto-decisioning, saying it would never work. This was in spite of the fact that many of the players who were out-competing us in the market had been successfully deploying this technology for over a decade. Eight years before the Wright Brothers made their first flight at Kitty Hawk, famed British scientist Lord Kelvin said that heavier than air flying machines were impossible. It seems unimaginable that Kelvin would make that statement today, sitting in an office outside of one of the busiest international airports in the country.

The punchline in all of this is that the auto finance industry has been slow to adapt to new technological innovation. The size of the market, and the number of dealer intermediaries has allowed lenders at all different levels of sophistication to thrive; but, it is the nature of markets to become efficient – and blockchain is the technology to facilitate that.

The combined wealth of the top five tech-giants is greater than the GDP of all but four countries. Amazon has disrupted dozens of industries, and it is now focused on grocery and pharmaceutical delivery. Google has massive amounts of data on every consumer’s online activity, and has the analytic horsepower to make use of it. It is just a matter of time before these companies leverage their unprecedented amount of capital, and use technological innovation to change the competitive landscape of auto finance forever.
Banks, captives and independent finance companies who are not actively investigating this technology would be well advised to learn the lessons of Blockbuster, Yahoo and Friendster. At TruDecision, we are actively researching ways to leverage blockchain technology, so that we will be part of the new market and not a victim of it.

Daniel Parry is co-founder and CEO of TruDecision Inc., a fintech company focused on bringing competitive advantages to auto dealers and lenders. He is also co-founder and CEO of Praxis Finance, a portfolio acquisition company, and co-founder and former chief credit officer for Exeter Finance Corp. For questions or inquiries, please email danielparrynaf@live.com visit www.trudecision.com.

Using Unique Data in Portfolio Management Provides Improvements, Returns

To stay competitive and within budget, many auto financing companies are evaluating and determining which emerging technologies and other resources to tap into and which to pass on or postpone.

Fortunately, not all innovations include costly modern technology systems or trends. And not all business challenges require big overhauls in spending and processes.

Sometimes the most tried-and-true options—the obvious fixes—yield the most beneficial outcomes.

These can include staffing or policy changes, new equipment and new software, to name a few. In addition, adding unique data to traditional sources can offer big returns in the areas of underwriting, servicing and collections. By leveraging proprietary, alternative credit data—in real-time—financing companies better know how to interact with the consumers they serve, thereby growing and protecting their company’s portfolio.

Such unique data sheds light on underbanked consumers, a real bonus for auto financing companies who are dissatisfied with a lack of visibility into the rapidly changing alternative credit market—an estimated 20 percent of all households, or 51 million adults, according to the FDIC. This proactive tactic provides insights into the underbanked market that the Big 3 bureaus don’t have. By pairing unique alternative credit data with traditional data, auto financing companies begin to get the complete picture of their customers and prospects. And consumers begin to receive the credit they deserve.

Case study: Uncovering life events increases net returns

A case in point: Let’s look at a real-world example from an active non-prime automotive finance company. The company leveraged alternative credit data to efficiently monitor all its consumer accounts that were 30 days past due. By using this data, the company was able to identify when customers had varying life events—a change in address, mobile phone number, employment or salary, new loans or inquires, for instance—that might affect their ability to pay their auto loan. For this scenario, the file being monitored included 74,000 records.

Within the first 30 days, there was about a five percent level of activity, meaning that 3,870 of the company’s customers from this file were found to have had some type of life event that might impact their ability to pay on their existing loan. This access to timely, relevant information helps auto financing companies determine when and how to proactively start a conversation with customers and respond with the appropriate servicing or collections activity.

By identifying these customers with life-event issues, the auto financing company was able to help their customers manage their payments, provide a new credit product or even refinance, in an effort to avoid repossession of the vehicle. It also enabled the company to give reliable and current right-party contact (RPC) information, when needed, to its servicers, skip-tracers and collectors, eliminating otherwise time-consuming searches for each past-due consumer.

Within 60 days, 11,380 customers, or 15 percent of the total file on the accounts that were 30 days past due, showed some sort of level of activity or change. However, when that timeframe moved out to 180 days, that figure jumped to 29,660 customers.

Therefore, in a six-month timeframe, by monitoring this one batch of records of 30-day past due customers, the company was able to identify 40 percent of its customer base as having had some sort of life event significant enough to impact their original loan terms.

With these results, the company was able to build a use case about alternative credit data based on its monthly net savings return alone. By identifying 40 percent of its customers in this file as having a life event, the company was able to speak to these consumers on their level and help them by adjusting the terms of their loans.

After implementing alternative credit data and incorporating this approach into its portfolio management, the company conservatively estimated that it kept at least five customers’ motor vehicles from being repossessed each month. Considering that the average net charge-off rate per unit per repossession totals about $5,000 in costs and fees, it is estimated that the total monthly loss avoidance totaled about $25,000. Even with nominal monthly fees in monitoring the data at about $3,500, the company had a net monthly return of $21,500, or an annual savings of about $258,000.

Auto financing companies can continue business as usual and see the same results. Or they can take a look at available, cost-effective, easy-to-implement resources like alternative credit data, and see immediate improvements to their bottom line.

Dallas Munkus is director, customer analytics, with FactorTrust, The Alternative Credit Bureau, where he helps customers leverage the power of FactorTrust data to positively impact their business. He has more than 20 years in consumer finance, leading analysis and strategy on diverse teams, including organizations, operations, servicing, collections and more. Connect with Dallas at dmunkus@factortrust.com. Learn more at www.FactorTrust.com.

three-lenders

Stephanie and the Three Lenders

So often I am on the go and rarely do I have time to sit and think about what just happened and why. However, after a recent 24-hour trip to the offices of three lenders, I got that chance. Each of the three lenders was strikingly unique, in very different stages of the business lifecycle, with differing approaches to business. And after the meetings, for a couple hours in the airport with my e-mail muted, I pondered what had taken place.

I found myself thinking about the classic fairy tale of the girl and the three bears, the one who found bowls of porridge, chairs and beds that were “too cold,” “too hot,” “too large,” “too small,” and “just right.” And I thought how, in business, particularly in the lending business when it comes to technology, we tend to peg ourselves as “too small,” “too large” or any number of other “too’s” that we can let get in the way of making effective change. In reality, we all have the ability to make things “just right.”

My business is ‘too small’

During the trip, I first met with a client who had just opened a brand new office. His business is not funded by private equity money; he’s just a guy building a business. When he signed up with defi SOLUTIONS nearly a year ago, he almost didn’t, because he believed his business was “too small.” But he was an entrepreneur with a passion for getting the right tools and the right people.
He stood in front of his team members, his dealers, and his business partners, and told the company’s story. The humbleness in his voice was inspiring and I smile even now thinking about it.

It’s tough when your business is small. You need to convince partners that you are different, you will grow, and that you are worth spending time and money with. And you need the right technology to help you.

After working with many lenders that felt their business was too small, I say this: don’t sell yourself short. Set your sights on the technology and the partnerships that position you for growth while letting you operate the way you want to operate. Whether you stay small or expand, you add value to our industry, to your customers, and to your dealers. You and they deserve good technology and the resulting good processes and efficiencies.

My business is ‘too fast’

On my second visit, I met with a client that is growing rapidly. It is 100-fold bigger than the “too small” client. This business is backed by PE money; it has easily doubled in size since I last visited. The company has big dreams and has to balance dreams with reality.

The company had made a pivotal decision, early on, to switch its lending technology systems. They put money and resources into their technology in the belief that their business would grow into the technologies they were investing in. And now they are running as fast as they can and making certain their technologies continue to keep up with their business.

As the client walked me around their offices that day, my long-time colleague mentioned how nice it was that that the business was all finally starting to come together. It didn’t happen overnight. And it didn’t happen without a lot of work and a lot of guts. Team members popped out of cubicles to say hello. They were excited. Happy to be part of something that is growing and successful. And I was grateful to have played a little part in it.

Scaling a business as quickly as they did requires balance — keeping investors happy, staffing a great team, and making hard choices about what to work on and what not to work on.

For the lender whose business is growing quickly, I say this: continue to put money and focus into your technology and automated processes, and don’t be afraid to step off a wrong path. You can’t get to the right place on the wrong path. Don’t pull back on projects and efficiencies. It may be hard to believe, but I often see lenders going hard at it and then stopping dead in their tracks. While it’s tempting to try to work your team harder in the manual processes or thinking you can live without improvements for a while, keep a clear picture of where you are and where you want to go and conquer the delta one step at a time.

My business is ‘too old’

The third lender in this series of visits was a business with a well-established history, with processes that were working and, more importantly, with which all had become comfortable. They had recently undergone a notable change in leadership and the new leader questioned: Why do we do this? He made it clear that “because we always have” or “because it’s what we have” was not an acceptable answer.

When things have been going well for a long period of time, there can be a lot of fear about shaking things up. That’s especially true when you are making changes that impact the core of the business, what people know and feel comfortable with. There is a risk that the team, the investors, and the board will only have so much patience with your efforts, and demand you go back to what they know. But fear can’t get in the way of progress.

This lender’s technology was old. But it was held together with enough duct tape that it was working for the time being. And they were concerned that ripping it out could have an impact of a magnitude that they could not possibly imagine.
For lenders whose technology and processes are too old, I say this: life is too short to sit in a too small chair, sleep in a too hard bed, eat too cold porridge, or operate with duct tape.

While there are no fairy tale steps to perfect technology, in today’s technology world you have choices. Technology is designed to be a table at which everyone has a good chair, regardless of budget and expertise, and a partner enables access to the broadest array of technologies to all lenders, regardless of size. A partnership is an investment in your success, and can help you create the things about your business that make you “just right.”

Stephanie Alsbrooks is the CEO of “just right” at defi SOLUTIONS, the most configurable loan origination platform on the market. E-mail Stephanie at salsbrooks@defisolutions.com or visit defisolutions.com for more.

the-credit-process

What’s New is Old Again

In 2010, the subprime auto loan market began to grow once more, returning from a near complete decimation to where it was pre-crisis. As far as the media is concerned, all growth was the product of a universal collusion to throw underwriting standards out the window. The following represents just a fraction of the stories that have been published since that time, sounding the alarm on risky auto loans and predicting another subprime crisis:

• 2010 – The Subprime Lending Business (auto) Survives, Even Thrives – Time Magazine
• 2011 – Ally Financial Bets on Risky Subprime Car Loans – Reuters
• 2012 – Subprime Auto Loans Grow as Lenders Charge a Premium – Forbes
• 2013 – How the Fed Fueled an Explosion of Subprime Auto Loans – Reuters
• 2014 – The Next Subprime Bubble to Burst: Auto Loans – New York Post
• 2015 – The Next Crisis, Subprime Auto Loans, Won’t End Well – Forbes
• 2016 – Significant Concern in Subprime Auto Loans – Investor’s Business Daily
• 2017–‘Deep’ Subprime Car Loans Hit Crisis-Era Milestone – Bloomberg

Nearly all of these stories reference subprime bond data, which is about $23 billion out of $250 billion in annual subprime originations. Of the roughly 10 percent that is put into bonds, about 60 percent comes from three companies – none of which are blowing up.

What has yet to become headline news is that subprime auto finance has been in a mild contraction for the last year now, with major lenders pulling out of the deepest paper and others limiting originations due to capital costs (otherwise known as rational lending). The real risk in auto finance comes not from conventionally structured auto loans, but from auto leasing.
Reviving the past

The oil crisis of the 1970s, along with a slew of regulations from the Environmental Protection Agency, pushed Americans to drive smaller, more fuel-efficient cars. Gone were the 428 Cobra V-8 engines and Mopar 400 blocks. Powerful muscle cars were soon replaced by aerodynamic wedges with better mileage. It was kind of like going from Sean Connery to Pierce Brosnan, for you 007 fans.

Fast forward 30 years, and we see that fuel prices have come down, technology has improved – and what is old has become new again. Camaros, Mustangs and Challengers roar down our streets once more. Retro on the outside, but substantially improved on the inside. However, not all historical revivals turn out this way. In some pockets of auto leasing, something is being passed off as new that is nothing more than a resurrection of the bad practices that caused a meltdown in auto leasing nearly 20 years ago.
In 1999, the market was saturated with leases, which accounted for nearly 26 percent of car sales. In order to make their product offering more attractive to dealers, lessors got into the habit of “enhancing” (i.e., inflating) residual values. This, among other bad practices, resulted in a near collapse of the leasing market and left many investors wondering how they were so exposed.

The mechanics of leasing

The terminology related to lease financing is different from an installment loan, but the components of how the lender makes money are essentially the same. The lender/lessor acquires a vehicle, and structures a note with the customer designed to produce a certain return on the capital that is put to work. The risks come in the form of customer non-payment and the costs associated with re-acquiring and disposing of the collateral.

In leasing, the amount financed is referred to as the net capitalized cost. The net capitalized cost minus the estimated residual value is the gross depreciation fee, which is divided by the term to form the basis of the customer’s monthly payment. A finance charge (referred to as a money factor) and sales tax are added to comprise the total monthly payment.

For the lessor to make money, they must fit all of their expenses into the finance charge plus whatever return they need to make. Some of the key costs they must account for are:

Residual loss – This is the difference between what the lessor estimated the value of the car to be at contract, and what they actually receive for it.

Turn-in rate – In prime leasing, approximately 10 to 15 percent of consumers buy the car. This equates to an 85 to 90 percent turn-in rate. The higher the turn-in rate, the higher the residual risk.
Non-payment – A certain portion of consumers will break the terms of the lease. The early payoff penalty is equivalent to the net loss on a standard installment loan.

If the lessor does not accurately account for these costs, they risk the stability of their entire platform – and anyone who has their money tied up in it.

Unconventional lending

I often get incensed at the lazy comparisons made between the subprime mortgages that led to the last downturn, and traditional subprime auto lending. With the exception of longer terms, the auto loans today look the same as they did 20 years ago. Likewise, performance has fluctuated within a very consistent range across multiple credit cycles.

The toxic mortgages, on the other hand, were the result of twisting conventional structures into forms that had never been seen before. No docs, adjustable rates and inappropriate credit risk were justified based on a false estimate of what the collateral would be worth. Proforma yield estimates appeared tremendous, and Wall Street could not get enough of them.

Today, leasing accounts for over 30 percent of vehicle sales, summing to over $200 billion in annual originations – much greater than the saturation point in 1999. While the overwhelming majority of leases are to prime consumers on new vehicles, Experian reports that 35 percent of non-prime and nearly 27 percent of subprime consumers are choosing to lease. That figure equates to over $50 billion, a number significantly larger than what is held in subprime bonds.

Several headwinds in the market make these statistics a cause for concern. First, vehicle demand has leveled off and dealer inventories have increased. Second, used vehicle values are declining and they are likely to continue to do so as a record level of off-lease vehicles flood the market. Finally, lessors have to work harder to maintain or grow volume, which opens the door for the stretching assumptions on lease economics.

Who is at risk?

Prior to the great recession, there were trillions of dollars in mortgage originations. Nearly $700 billion of that was subprime, and more than 75 percent was securitized. When these loans imploded, they took the banks and the rest of the economy with them. In contrast, the auto lease market is significantly smaller.

While a spike in lease defaults will send negative signals to the capital markets and regulators, it is unlikely to disrupt the broader market. The exposure is largely limited to companies that predominantly originate auto leases, their equity investors and debt providers. Executives, investors and credit committees will want to look for three red flags that signal potential performance issues. These are:

•    Credit creep – Every credit default carries with it certain costs for the lessor. These costs go up exponentially as credit creeps down the spectrum. Unrecovered early termination fees, collection expense and lost collateral value (repossessed vehicles are worth far less than market for a similar unit) all must be paid for by the finance charge. When lenders and lessors in higher credit tiers feel pressure related to volume, it is not difficult to dig deeper in a way that cannot be detected by looking at credit score alone. Missed credit assumptions can easily take a budget of 100 basis points in annualized default expense to 300 or even 500 basis points. Many will point to the fact that a lease allows the customer to get in a lower payment than a loan, which suggests they default less frequently than loan customers do. That might be true, except for the fact that consumers are sold on a payment size, and typically pack as much into that payment as possible. The payment-to-income ratios for loan and lease customers are very similar, so that argument does not hold water. Investors and debt providers should look to early payment default rates, and 30 plus days past due delinquency in the first six months on books. These figures should be compared to delinquency rates of loan portfolios with similar credit score distributions to ensure that the credit assumptions line up with reality. Make sure that if the business model calls for near prime consumers, the portfolio is not filled with consumers who are near prime in score only.

•    Collateral mismatch – The biggest miss lessors have historically experienced comes from playing games with estimates of residual values. There is a moral hazard present in that a higher residual estimate means a lower customer payment, which makes it easier to get volume. The increase in the number of nonprime and subprime leases may indicate another issue, that there is a complete mismatch between the vehicle and the financing instrument. For a lease to make sense the vehicle has to be worth something at the end of the term; otherwise, the customer would be financing the entire vehicle without ever owning it. A collateral mismatch occurs when the quality of the vehicle must be inflated to fit a lease scenario in the first place. To make matters worse, those contracts are usually laced with out-of-market incentives to dealers in order to get them to close mismatched deals. Not only are the losses on such leases unsustainable, but they also create serious compliance issues related to predatory lending. To protect against collateral mismatch, capital partners must have a direct line into the vehicle values. This goes beyond merely acquiring periodic data files from the company, but running independent outside checks by submitting vehicle identification numbers to third party data providers like ALG, Black Book, NADA and others. Those vehicle values should be compared to the statistics of other lease portfolios in similar credit tiers (available from credit bureaus and other marketing data sources). If the company’s collateral and lease structures do not line up with what should be considered the peer group, there may be a problem with collateral mismatch.

•    Pseudo controls – Credit creep, collateral mismatch and other serious problems are borne by companies lacking real controls. Great leaders maintain proper controls and foster a culture committed to supporting those controls. Bad leaders simulate a tight control culture, and can go on at length citing many checks and balances that do not actually exist. Bad operators see controls as a nuisance that prevent them from having the latitude to properly run the business. When portfolio performance becomes a problem, a poorly controlled entity turns to cooking the books. In those types of organizations, it is not uncommon for the internal auditor, compliance manager, risk manager or other control person to be overpaid, and several levels above what their resume would warrant – in other words, a patsy. Investors can protect against this by having activist board members with direct connections to the control functions. Regular control audits must be conducted from independent parties. For debt providers, do not rely on covenants to protect you, or you may be surprised at how weak your security interest is. Make certain that the numbers in the system were not fabricated in order to fit something into the warehouse facility. Insist on periodic I.T. audits, conducted by independent forensic data specialists.

Conclusion

Unlike the steady stream of articles on subprime auto, I am not predicting a time bomb waiting to go off in the auto leasing space – nor am I grouping all operators together. There are many excellent auto lease originators, particularly among the captive finance companies. The latter are usually run by strong leaders with excellent control cultures. Furthermore, they have vast amounts of data that show how their vehicles hold up in a variety of economic environments. There are also many independent companies with long histories of proven performance. That being said, the practices that led to problems in the past still exist today.

In the subprime mortgage crisis, assumptions meant for conventional loans were applied to highly atypical structures. Investors fell for it, and consumers flocked to it – sold on the idea that they could access financing that was formerly only available to the top tier. In some pockets of auto leasing, the same thing is happening today. What the market will find is that what is pitched as new is actually the same old song and dance. By keeping an eye on credit, collateral and controls, capital partners can make sure they avoid seeing history repeat itself.

Daniel Parry is co-founder and CEO of TruDecision Inc., a fintech company focused on bringing competitive advantage to auto dealers and lenders. He is also co-founder and CEO of Praxis Finance, a portfolio acquisition company, and co-founder and former chief credit officer for Exeter Finance Corp. For questions or inquiries, please email danielparrynaf@live.com or through www.trudecision.com.

Will a Data Warehouse Solve My Data Problems?

A two-part series to help you identify and solve your data needs

Have you seen the funny YouTube video, It’s Not About the Nail? It starts with a close-up of a woman talking about this relentless pressure she feels and how she’s afraid it’s never going to stop. Then the camera angle changes to reveal she has a big nail coming out of her forehead. Her boyfriend patiently listens and finally states the obvious solution. Remove the nail.

Too many businesses approach data problems the same way as the couple in the video. They have a data problem (the nail in their head), but they go about identifying symptoms (headaches, pressure) instead of just recognizing the problem and solving it. So they go down a rabbit hole chasing solutions that don’t address their obvious problems.

This week a customer called me in the throws of building a data warehouse. He was feeling the pain of the implementation process and wanted to know if that was normal. Like all of us, he just wants the data he wants at his fingertips to make faster and smarter decisions for his business. And, he wants the data to be accurate!

In my 20-plus years of experience, I have never seen anyone fly through a data warehouse project like it was as easy as making a PB&J. But I wondered why it’s so difficult. To find the answer, I posed the question to some of the smartest people I know. My team at defi SOLUTIONS. (Thank you Brandon Burns, Jose Salinas and Rob Dufalo for your input!)
So here’s what I learned. Data warehouses are difficult to build because they’re often the wrong solution. It just seems like the thing to do. Say you need some quick, accurate, operational metrics and your CIO says the solution is to build a data warehouse, because that’s what he/she did at another company. Or your colleagues at a conference tell you about their data warehouse projects, so you assume you should be building one too. This scenario can happen with any technology need.

The pain of data warehouses could also be the result of how it’s designed. A data warehouse can be over engineered out of fear or vague expectations. Businesses have to have a clear definition of success before they build a data warehouse.
Speaking of expectations, this article isn’t going to tell you how to know if you need a data warehouse or how to successfully implement one. That would be jumping the gun. I want to focus on the critical first step to creating any technology solution – step back and assess what you’re really trying to solve. You’ll save yourself a lot of pain if you do.

Now let’s dig in.

The importance of data is not a new concept. Neither is pulling data together and placing it at our fingertips. It’s old school. Really old. Ancient, in fact. Over 2,000 years ago, the Library of Alexandria was said to contain all the important data of the time under one roof. The very first data warehouse!

Data gives us the ability to learn from the past and the present so we can have a better future. A great example is IBM’s supercomputer named Watson. It gets smarter and smarter as it learns from the past.

I think we can all agree data is important. And it’s true that building or buying a data warehouse may even help you improve your use of data. However, before embarking on such a large project, first assess your pain points. Here are six examples of typical pain points. Please don’t limit yourself to this list. Make it your own.

1. Operational metrics
• Are you missing operational metrics? Why aren’t you getting them? They’re not available? No one has time to get them? They don’t exist anywhere? For instance, the other day I asked for a piece of data. My team said we didn’t have it. I might infer that I need a data warehouse to get it. But in fact, we just had never spent the time to calculate the numbers.
• Are you getting all the metrics you need, but you’re constantly dealing with inaccuracy?

2. Scalability
• Do you have tons of data and maybe you can find it all, but it’s just not in a place or in a way that will scale as you grow? For instance, I used to keep defi sales data in five spreadsheets. I knew exactly where to go to get it, but I couldn’t easily pull it together. There was no common format and if I sent it to anyone else, they wouldn’t be able to make heads or tails of it.

3. Timing of getting data
• Are you on old legacy systems that don’t allow for near real-time data access? Are you always trailing behind? Maybe you have the data and it seems accurate and built in a way that scales, but it’s only available at a certain time during the day, week or month?

4. One system of record
• Do you struggle with getting data across departments to match? Does every team have its own version of the truth?

5. Reporting vs. analytics
• Do you have data housed in a format that’s conducive to reporting, but makes data exploration a challenge? Do you have an environment that can efficiently mine, explore, and quickly present/visualize (if needed) data elements, which may not be currently used for reporting, etc?

6. Self-service business intelligence
• Don’t want everyone on your team to have to put in requests to get analysis and data? Need the data exposed so various users can visualize and construct their own report? Our team faced a similar challenge, but our data warehouse wasn’t built for it.

Seriously, write down your pain points. Don’t assume a data warehouse is the answer or the answer right now. What are you trying to solve or fix? Ask your team to compile a list of the true problems you’re facing and prioritize them. You want it all and you want it all now, but you should figure out what is most important and why.

defi just went through a similar analysis to build out our configurable reporting platform. For us #2 and #6 were key factors. We had to spend a lot of time really honing in on what we were trying to solve. We didn’t start with the assumption that the configurable reporting platform would need a data warehouse. We started with pain points we needed to solve.

Once you have your needs identified, hold on to them and tune in next time as we dig into potential solutions (including data warehouses) and go through an exercise of scoring each pain point against the solution. We’ll also layout a smart implementation approach to minimize your pain, assuming your analysis says the data warehouse is the right path for you.

Stephanie Alsbrooks is the CEO, founder and chief data evangelist of defi SOLUTIONS, the most configurable loan origination platform on the market. Email Stephanie at salsbrooks@defisolutions.com.