Millions no longer credit invisible

Last month the Consumer Financial Protection Bureau (CFPB) released a report summarizing the discussions and conclusions from an all-day symposium it hosted on the topic of credit accessibility. The event focused on a broad spectrum of ideas and challenges associated with responsibly expanding access to credit to those who are underserved.

Obviously, credit scores are one of those factors. First off, the numbers:

The CFPB noted that based on its prior research that, “roughly 20 percent of the adult population have no credit records or very limited credit records with the three Nationwide Credit Reporting Agencies (NCRAs). As a result, these ‘credit invisible’ and ‘unscoreable’ consumers are unable to fully participate in the credit marketplace. This can limit their ability to withstand financial shocks and achieve financial stability.”

It is important to point out that the number of “conventionally unscoreable” consumers, as we call them, varies depending upon what model is used. We define anyone who is “conventionally unscoreable” as one that fails to meet the minimum scoring criteria of the conventional models widely used by lenders and exclusively used by the mortgage industry. The CFPB used one of these conventional models in its research.

These models require at least six months of credit activity or an update to the credit file within the previous six months.

So in essence, according to these models, if a consumer has been inactive for say, seven months, that consumer is so risky as compared to a consumer that has been inactive for six months, that she/he should face the highest possible interest rates and terms or be declined altogether.

This, as they say, doesn’t pass the smell test. You don’t need an advanced degree in mathematics to conclude that one day after the six-month cut-off you are immediately a super high risk consumer.

The VantageScore model was developed using an approach that can accurately score 40 million more consumers compared to traditional scoring models, helping lenders evaluate a larger pool of consumers in risk and marketing decisions.

These include:

Emerging Borrower/Young File Young to credit Consumers that have only tradelines that are  fewer than six months in age
Dormant Infrequent or rare users of credit Consumers that haven’t had an update/reporting on their credit files in the past six months but have had updates more than six months ago
No Trades Have only external collections, public records and inquiries on their file Consumers that have no credit accounts but are scored based on the external collections and public records on their file


A demographic breakdown is as follows:

2018 Unscoreables – All Scores 2018 Unscoreables – Scores 620+
Total 40 million 10.06 million
Black and Hispanic 12.2 million 2.4 million
Asian/Pacific-Islander 1.6 million <1 million
White 25.7 million 7 million

Having said that, we as an industry need to ensure credit is extended safely and in accordance with sound lending practices. That means the score assigned to each consumer needs to be an accurate portrayal of her/his risk profile, or more to the point, the consumer’s propensity to go into default.

So is the score that the VantageScore 4.0 model assigned to these newly scoreable consumers accurate?

Yes! There is a nearly identical alignment of default rates between those with limited credit histories and those who have conventional credit behaviors in the first year of a new account.

VantageScore 4.0 also provides for a significant predictive performance lift over VantageScore 3.0 across all credit categories. And perhaps most compelling of all, almost 2,500 financial institutions use VantageScore credit scores, with the number growing steadily, providing testament to the strong performance of our models when benchmarked against other competing models available.

The CFPB has aimed a bright spotlight on the challenge and plight of the conventionally unscoreable consumer. We need to get behind their leadership and test, validate and adopt new processes so that the bureau’s leadership isn’t in vain. Moreover, we encourage the CFPB to revisit their study on credit invisibles. The data is now outdated and the marketplace would benefit from a better understanding if other models aside from the conventional models used in the original analysis provide a consumer benefit.

Please read on for some great articles this month. Included are features about why and how lenders upgrade to new scoring models; a helpful back-to-school article with credit health in mind; and our “Five Questions With…” guest is Peter Esparrago, Co-Founder & CEO of Finlocker.


Barrett Burns

Why Convert to Newer Scoring Models

The Value of Converting to Newer Scoring Models

Every few years the companies that design and develop credit scoring models will go through the lengthy process of redevelopment. For comparison purposes, think of how Apple or Microsoft will periodically release a new operating system for your Mac, PC or smartphone. The newer version is very similar to the previous version, with some notable improvements.

As with smartphones, banks and other users of credit scores have the option to convert to the newest scoring model once it becomes commercially available. The process can be time consuming given the amount of analysis that must be done to properly adjust underwriting standards for the newer version, as well as the operational changes and governance steps involved. This is partly due to testing for predictiveness and fair lending concerns, which is critical information lenders must analyze before upgrading models. But the benefits far outweigh the challenges.

More Predictive

Newer credit scoring models are more effective than older scoring models and that’s really their primary function: predicting a future default. Changes in consumer behaviors, lending practices and the macro environment are better represented in newer models that are built on more recent observations. Further, availability of new forms of data and more advanced modeling methodologies provide additional firepower to a newly developed model.

There are a variety of empirical methods used to compare the performance of credit scoring models. The Gini coefficient (“Gini”) and the Kolmogorov-Smirnov (KS) statistic are two common metrics used to compare the effectiveness of credit scoring models and their ability to delineate between future “good” and “bad” credit risk populations. Lenders perform such “champion-challenger” comparisons in evaluating new models. Other analyses performed to assess the performance of new credit scoring models relative to an older model compare profiles of approved and rejected borrowers, resulting default rates as well as approval rates given a loss tolerance.

Failing to adopt the newer versions means lenders are making decisions based on less effective tools, leading to suboptimal risk and pricing decisions. And, choosing to continue to use much older scoring systems, like what is happening in mortgage lending because of FHFA policy, is counter to sound underwriting and risk assessment practices.

Captures More Recent Trends in Risk Assessment

A valuable product of newer scoring systems is the ability for the model developers to react to more recent data and research and development findings, and then apply these learning into their scoring models. This is commonly referred to by credit score experts as “following the data.” The underlying premise of a credit scoring model, like any model, is that what is observed in data historically will be indicative of the future. Hence, models that take into account more recent observations are more representative of the “through the door” populations and will perform better. Newer models also benefit from insights gained from data elements that are previously unavailable for modeling.

There are many examples of features included in newer scoring models that are not captured in older scoring models, like the ones used by mortgage lenders. Some of these features in newer models relate to:

  • Changes in borrower behavior post Great Recession,
  • Treatment of smaller dollar collections or collections with zero balance,
  • Treatment of medical collections,
  • Accounting for reduced presence of derogatory public record information due to changing reporting requirements,
  • Use of new data measuring changes in credit behavior over time versus a static snapshot.

 Benefits Consumers and Lenders Alike

As previously addressed, newer scoring systems do a better job more clearly separating lower risk consumers from higher risk consumers. What this means, in practical terms, is low-risk consumers are going to have higher credit scores under the newer scoring models. Think about that: Higher credit scores simply because a lender is using a newer and better scoring model. This translates to higher approval odds for a loan and better product terms, such as lower interest rates and higher credit limits for creditworthy borrowers.

The opposite, of course, is true as well. In older scoring systems, low-risk consumes are going to score lower than they deserve. This means consumers who do business with lenders that use older scoring models may have lower chances of being approved for a loan or face less favorable product terms, compared to what they would receive from lenders that use newer, more effective scoring models.

For lenders, the choice is between growing their business safely and soundly with the help of a newer and more effective scoring model supporting their underwriting decisions, or using an older model that may lead to loss of good business to competition, or worse, originating loans with higher risk of default than desired.

At the end of the day, lenders and consumers both win from using newer credit scores. Matching creditworthy consumers with the right loan products is good business for lenders, and, helps consumers meet their needs while guarding them against getting in over their heads with debt.

No Reason Not to Use Newer Models

There really is no good reason not to convert to the newer scoring systems if this helps business.

Sure, there’s work involved in converting to a new credit score. This may include analysis that needs to be performed to determine the adjustments necessary to underwriting policies and product features, going through required governance processes, such as credit committee approvals and model validation, operations and technology changes, training and communication for staff, among others.

These activities are not uncommon as part of normal course of business and should not deter from implementing a newer and better model. Lenders go through similar steps when introducing a new product, adjusting policies, entering new markets, or adopting new processes or technology. While there is an initial investment, longer term benefits offered by a newer model far outgrow the cost.

Credit and School Shopping

DID YOU KNOW: Credit Mistakes Parents Make During Back-to-School Shopping

By John Ulzheimer

Summer vacation is nearly over. And while parents are largely focused on the looming first day of school and making sure their students are properly geared up for day one, it’s also important that parents gain awareness of the common credit mistakes that they may be tempted to make during the time when they’re getting their kids ready for the school bus.

All students need new school clothes, most likely because they’ve outgrown the clothing they wore last year or they’re trying to stay current with the trends. That means that you’ll probably be spending time with them at the mall visiting a variety of the national retail chains or you’ll be doing some online shopping. Almost every store will be offering a discount on your purchases if you’re willing to apply for a new store credit card. Generally, this isn’t that big of a deal. But, you should be aware of how the application for new retail credit cards may affect your credit scores.

First, every time you apply for a credit card, the lender is going to pull one of your three credit reports. That means a new credit inquiry will appear on one of those reports. Inquiries can lower your credit scores, although the impact is generally minor at worst and negligible at best. But, if your credit scores are already marginal, then every point is important. As a result, a few new inquiries may make the difference between obtaining an approval or a denial, and between a higher or lower interest rate.

Next, when you apply and are approved for a new retail store credit card, it’s only a matter of time before the account, which is formally referred to as a “trade line,” will be reported to the credit reporting companies (CRCs )and find its way to your credit reports. Newly opened accounts will lower the average age of the accounts on your credit reports. That’s a mathematical certainty. This too can lower your credit scores, potentially more so than a few inquiries.

Finally, retail store credit cards almost always have very low credit limits, many times lower than $1,000. This means that even modest purchases can lead to a heavily leveraged credit card account. This too can lower your credit scores because your utilization will be here. This is also referred to as your balance-to-limit ratio, and it’s a very important metric in your credit scores. You want to keep that ratio as low as possible to prevent your credit scores from dropping.

This does not mean that you should avoid buying school clothes, and it certainly doesn’t mean you should avoid taking advantage of the benefits afforded by using a newly opened retail store credit card to make your purchases. But, you should pay attention to the potential negative impact to your credit scores. And, if at all possible, you’d be wise to pay off any newly incurred school shopping debt well before you go out and apply for something like a mortgage or auto loan. That will ensure your credit scores are in the best shape possible at the time of your application.

Disclaimer: The views and opinions expressed in this article are those of the author John Ulzheimer and do not necessarily reflect the official policy or position of VantageScore Solutions, LLC.

“Five Questions With…” Peter Esparrago

THE SCORE is very pleased that this month’s guest is Peter Esparrago, Co-Founder & CEO of FinLocker.

Peter Esparrago is a global technology executive with 25+ years of experience. He is Co-Founder & CEO of FinLocker, a financial data analytics company for the mortgage and banking industry. Peter is also a Co-Founder & General Partner of Cultivation Capital, a technology venture capital firm. He was a Scottrade board of director until it was sold to TD Ameritrade. Peter spent 16+ years at Accenture, the world’s largest technology services company, where he was a Partner. He has an MBA in Finance from Rockhurst University, and a BS in Chemical Engineering from University of Missouri. Peter is a governance fellow member of National Association of Corporate Directors (NACD).

What does FinLocker provide to consumers and lenders in order to make home buying easier and/or less costly?

Consumers want control over their data and are willing to share it but expect to receive personalized offers and recommendations in exchange. FinLocker delivers a reusable financial locker with personal finance and education tools to ready borrowers for a home purchase or refinance, by securely capturing and analyzing borrower data, such as employment, income, assets, credit, real estate and other financial information. Sponsors of the financial locker may provide offers or recommendations to the consumer based on his/her profile and goals. Once a consumer is ready to move forward, data can be shared via the locker with a lender, which streamlines the mortgage application and loan manufacturing processes, shortening the time to approve the loan, and lowering the costs.

How do you handle privacy and data security concerns?

Consumer’s data is strongly encrypted in transit over the internet and in the financial locker. FinLocker only collects information about consumers from external parties with the consent of the consumer. The consumer also provides consent to share certain data with a third party like a bank. FinLocker leverages modern information security strategies to secure data in multiple layers of protection. The type of access to accounts is also restricted. It is read only access, meaning it is only possible for the system to view the data, not change it or transact. In addition, FinLocker is SOC 2, Type II compliant.

What has been FinLocker’s biggest breakthrough to date and what do you hope to accomplish over the next year?

FinLocker has been awarded 3 patents (and more pending) in recognition of our innovative platform as well as differentiating FinLocker from competitors. Patents include: 1) control and data access to sensitive data including digital vaults; 2) Data analytics of sensitive data including artificial intelligence; 3) control and visibility into workflows of potentially sensitive data across different operators. FinLocker will continue to advance its platform in being the automated personal financial assistant to consumers for personalized offers and recommendations.

Which other parts of the home purchase and financing process are poised for the most change due to technology and innovation?

The battle for the mortgage customer is happening much earlier in the process…at point of thought, not at mortgage application time. Lenders are starting to use technologies such as FinLocker to engage consumers at point of thought to get them financially ready and educated, while dramatically streamlining the loan eligibility and approval processes.

What areas of the mortgage market will be the toughest to improve via fintech innovation and why?

We believe that serving first time homebuyers and low-to-moderate-income borrowers can be greatly aided with innovation but still is best when technology and a high touch personal relationship are combined. According to the Harvard Center for Joint Housing Studies, minority household formation will represent 88% of the projected household growth by 2038. Providing these consumers with technology and tools that help them in their journey towards homeownership and mortgage readiness and layering on a personal high touch relationship built on trust best positions this segment for success. FinLocker is proud to partner with lenders, credit unions, housing advocacy groups and counseling agencies to help empower consumers to start, achieve and continue their homeownership journey.

Valued partners:
VantageScore Licensees:
Equifax Experian TransUnion