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.

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.

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.

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 visit


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 visit

Choosing a Lender

by Scott Brackin

creditmailIn the presence of fierce competition and razor thin margins, it’s more important than ever for dealers to look for lenders who are engaged in innovative ways to protect risk, while providing the best in class service levels for the dealership and the consumer.

Dealers understand non-prime consumers are on extremely tight budgets. Would-be customers walking into the showroom already know exactly what monthly payment they can afford.  To accommodate these potential buyers, dealers rely on lenders to help provide the right interest rate and term. Historically, lenders have relied primarily on the Big 3 bureaus, however those bureaus have limited information on non-prime consumers. Most dealers know that millions cannot be scored. Per the CFPB, 45 million U.S. adults are living without credit scores due to no credit history, limited data or out of date credit data with the Big 3 bureaus. There are also nearly 90 million adults with scores below 650 and that number is just going up.

Top lenders are innovating and learning alternative data is shedding more light on these thin-file, ghost consumers to help them better assess the risk and offer more flexibility in pricing. To be competitive, dealers must work with those lenders that employ all available information —specifically alternative data—to provide a more complete picture. By utilizing lenders that have access to alternative data on those consumers, dealers will sell more cars, minimize buybacks and help to build better customer relationships.

passtimeWhat is alternative data?
Just as the label implies, alternative data falls outside the scope of the Big 3 credit bureaus and includes public and private records procured from governmental and institutional sources and is accessible via third-party aggregators. Alternative data sources could include records from public files, utility and telecommunications companies and others.

From public records, one may access an immense amount of data regarding a consumer’s property ownership, bankruptcies, liens or judgments and relationships. Public records provide asset and adverse action information that may be modeled. Another source of alternative data is tradeline information that offer access to application inquiries, loan performance and consumer stability details. Alternative tradelines provide past loan payment behavior and an understanding of outstanding debt. At the end of the day, what you are looking for is a lender that has a complete the picture of someone’s ability to afford the automobile.

Move more metal
Lenders that leverage alternative data have deeper information on consumers’ buying behavior. These lenders will provide a pricing advantage over those who are not accessing alternative data.

For example, a lender who uses short-term tradeline data will have information on whether or not the consumer has taken out or paid off any short-term loans. Paying off short-term loans correlates with reduced risk. Specifically, borrowers with three or more paid off short-term loans in the prior year have a 33% lower than average auto loan bad rate – an improvement three times as large!

This information of a lower default rate translates directly into pricing power and the ability for dealers to close more sales.

automotive-personel-200x300Better buying experience
Lenders using alternative data have information enabling them to quickly verify information online, reducing some of the manual verification process. Such services include identity verification and information on the consumers’ stability that correlates directly with risk. Often lenders will access alternative data to verify income, employment and ACH events. When successfully implemented, these services make the funding process more cost-effective for the dealer.  As a result, the experience is also faster and more efficient for the buyer.

Alternative data providers have built a business around collecting unique quality data from a variety of less traditional sources.  The buzz is this data is quickly becoming more mainstream with auto lenders. When choosing a lender, find out if they are using alternative data, what types of data and how they are using the data to help them more accurately assess risk and price the loan.

Lenders leverage alternative data to take a “closer look” at prospects and build a win-win for the dealer and the consumer. Not only will it allow dealers to be more competitive, resulting in more sales, but it will also allow the consumer to be able to afford the vehicle their credit really deserves. By meeting their needs quickly the first time, this will undoubtedly bring about repeat business.  A true win-win-win for the dealer, the consumer and the lender.

Scott-BrackinScott Brackin is the automotive practice segment leader for FactorTrust. Scott has more than 24 factortrust-200x300years of experience in financial services, credit risk data, and technology. Since joining FactorTrust in 2013, he’s overseen the company’s rapid growth in the automotive segment as part of his vision to become the premier alternative credit reporting agency (CRA) and analytics business for the automotive finance industry.