A useful infographic fact sheet on:

• what is a credit score
• why it matters
• how a strong credit score can help you
• why you have different credit scores
• what impacts a credit score
• what does not impact your credit score
• how you can improve your credit score

At first, the process of converting strategies to use newscores can seem overwhelmingly complex. Generic riskscores have become deeply embedded within strategiesand often strategy design is contingent upon the scoreperformance.  In reality, there is just one central question that must beanswered for successfully converting a strategy to use anew credit score:

What is the value of the new score (NewScore) that represents the same default rate orpopulation volume designated by the previous score(OldScore)?

All conversion processes revolve around answering this question and essentially follow the same steps. The analytic and resource requirement for each step in the conversionprocess is determined by the complexity and magnitude ofthe specific strategy. Furthermore, the process must befollowed when converting from one version of a score to anew version or converting from one brand of score toanother brand.

To take advantage of the strengths of VantageScore 4.0, lenders should conduct a score conversion process to determine how to incorporate the new score into their credit strategies. Such model conversion processes cover allcredit scoring models, such as converting VantageScore 3.0 to VantageScore 4.0.

Download the study to learn more. 

This whitepaper was newly updated August 2020.

VantageScore’s whitepaper “The Dynamic Relationship Between a Credit Score and Risk: How to Correctly Interpret a Credit Score During an Economic Downturn,” provides lenders and other users of credit scores transparent details about how the score-to-default risk  relationship changes over time, and notes the importance of timely and active credit risk management in order to make proper portfolio adjustments and credit score cut-off recalibrations in response to shifts in the economy.

In this article, we provide insights on the key factors impacting a consumer’s VantageScore credit score and walk through some specific actions that can lead to changes in the score. We demonstrate how a score changes using some examples of consumer profiles, representing different levels of experience and histories with credit products.

This 2020 report represents the third annual model performance assessment ofVantageScore 4.0 (which was launched in April 2017). The model is the first and only tri-bureau credit scoring model to incorporate for superior performance both trended credit data and leverage machine learning.

Trended data supplements the static borrowing and payment activity which has been historically recorded in consumer credit files and used to develop risk models. Trended data captures the trajectory of borrower behaviors overtime, thereby allowing additional insights into consumers’ credit risk profile.

As part of the annual assessment, VantageScore 4.0 performance results are compared to the performance results of credit scoring models that were developed earlier and contain only static credit attributes. VantageScore 4.0 also scores approximately 40 million consumers who cannot obtain credit scores when conventional scoring models are used. Machine learning techniques were utilized in the development of scorecards for this “Newly Scored” segment. During the annual assessment, performance of the model in this segment is compared with that of the earlier VantageScore 3.0 model.

UPDATED to include the latest 2020 innovations:

From 1987, when the first generic credit scoring models were introduced, through 2005, the credit scoring industry was dominated by asingle company with little incentive to innovate. The arrival of VantageScore Solutions in 2006 brought competition between developers thathas fueled innovation and benefitted both lenders and consumers.

Global management consulting firm Oliver Wyman examined how many VantageScore credit scores were used in a 12-month period between 2018 and 2019, as well as by which types of consumer lenders.

Overall, the study found that 12.3 billion VantageScore credit scores were used, a 20 percent increase in usage over the prior year. Since June of 2015, VantageScore usage has grown approximately 20% per year.

The Oliver Wyman analysts found that usage was widespread across credit loan categories, including credit card, auto finance, and personal loans as well as in functions where credit scores are often used, including pre-screen, marketing, origination, and underwriting and portfolio management.

Usage of VantageScore credit scores is widespread across all major consumer lending categories with the notable exception of the mortgage market, where FICO scores are currently required by both Fannie Mae and Freddie Mac during the initial screening of mortgage applicants when using their automated underwriting processes.

Among Oliver Wyman’s other findings include:

• Credit card issuers and banks and thrifts accounted for over half of all usage of VantageScore credit scores.
• Over 3 billion VantageScore credit scores have been provided to consumers to empower them to practice good credit health habits, and nationwide most consumers use VantageScore credit scores as a proxy for how a lender might interpret their creditworthiness.
• Nine of the 10 largest banks and 29 of the 100 largest credit unions used VantageScore credit scores in one or more lines of business.

Approximately 12.3 billion VantageScore credit scores were used in a 12-month period by 9 of the top 10 largest banks and by more than 2,500 unique users (2,200 of which are financial institutions). Oliver Wyman,a global leader in management consulting, conducted this study using data reflecting usage from July 2018through June 2019.

A recent study conducted by global management consulting and research firm Oliver Wyman concludes that usage of VantageScore credit scores is deep and mainstream, and that it occurs in all credit categories except the mortgage market.

12.3 BILLION VantageScore credit scores were used in the timeframe of July 2018 – June 2019. Read more about VantageScore credit score usage by clicking on the image below.

Download this helpful Q&A to learn how credit score competition can help the mortgage market – for consumers and lenders alike.

This white paper first presents an overview of what trended credit data is on a credit report and what it provides.  Second, this paper examines how direct credit management behaviors over time become a stronger indicator of credit risk versus static behavioral snap shots. Finally, a comparative case study using two credit scoring models, one containing only static attributes and the other incorporating trended credit data, highlights how insights drawn from trended credit data improve the view of a consumer’s risk profile.

A visual breakdown of the next steps following the FHFA’s final rule on the validation and approval of credit score models by the GSEs – Fannie Mae and Freddie Mac. Click below to enlarge and download the image.

A quick guide to the properties of the VantageScore 4.0 credit scoring model as it applies to the mortgage market.

An overview of the most predictive, consistent and inclusive generic credit scoring model available.

A key element of sound model risk management practices is to ensure that models continue to perform as expected over time. To that end, model developers and users perform periodic assessments of models to assess model performance against expectations, using recent observations and objective performance criteria.

To that end, model developers and users perform periodic assessments of models to assess model performance against expectations, using recent observations and objective performance criteria.VantageScore Solutions annually assesses the performance of VantageScore credit score models at each of the national Credit Reporting Companies (CRCs) – Experian, Equifax and TransUnion.

To promote transparency and aid in model governance, VantageScore Solutions publishes the results of these assessments, along with updated odds/performance charts. 

This 2019 report represents the second annual model performance assessment of VantageScore 4.0 (launched in April 2017).  The model is the first tri-bureau credit scoring model to incorporate trended credit data and leverage machine learning for superior performance.

Lenders, be prepared. Headwinds may be ahead. After enjoying a strong consumer credit market for a decade following the Great Recession, the lending industry faces a growing set of challenges. With a much anticipated credit downturn looming on the horizon, it’s time for lenders to sharpen their pencils. 

Changes in the marketplace

Consider the changing marketplace. With a growing U.S. economy, a 50-year low in unemployment, and interest rates remaining near historic lows, Americans are carrying more consumer debt than ever—with particular growth in riskier areas such as credit card balances and unsecured personal loans. 

Total non-mortgage household debt exceeded four trillion dollars at the end of 2018. While new originations have been healthy, there are signs of an uptick in delinquencies, for instance in credit card and auto loans. 

The competitive landscape is changing, too. Nontraditional players, including a new class of fintech platforms, are gaining strong momentum and taking market share away from banks. According to TransUnion, in 2018 fintech lenders originated 38% of all personal loans, up from just 5% in 2013. 

The rapid rise of fintechs comes in part from their speed and flexibility. They tend to offer more favorable terms to near-prime and subprime borrowers, with quicker approvals and funding of loans. Millennials are an obvious target for these lenders. This new competition has put more pressure on traditional lenders’ bottom lines. 

Adapting to stay competitive

In this environment, lenders need to continue to innovate and adapt to changing consumer behaviors to remain competitive and grow their portfolios, while maintaining a laser sharp focus on credit risks. As always, the fundamental task is to target well-qualified prospects from a competitive universe and match them with the right products, safely and profitably. 

That means lenders need to understand and accommodate changes in credit use patterns and attitudes when designing products. They need to leverage the best available data and consumer insights in underwriting, pricing, and portfolio management strategies. And in doing so, they must fully satisfy risk and compliance expectations. 

Having the right analytical tools, including an effective credit scoring model that can accurately assess risk for a broad population of consumers, is as critical as ever. 

The VantageScore 4.0 credit scoring model offers significant performance benefits over other credit scoring models by utilizing better data, including trended credit data, and by taking advantage of more advanced modeling techniques. The model also allows lenders the ability to assess a significantly broader population of credit-eligible consumers, creating opportunities for borrowers and lenders alike. 

An ever-growing number of lending institutions offer VantageScore credit scores to consumers. 

VantageScore 4.0 offers distinct advantages to lenders in addressing the challenges and opportunities the current marketplace presents. First, the model can accurately assess previously “unscoreable” consumers, making the credit markets more accessible to creditworthy consumers while creating a corresponding opportunity for lenders. Secondly, the model provides superior predictive performance across all credit segments, driven by deeper insights on credit behavior. 

How to expand the opportunity

According to the latest census, there are 252 million people over the age of 18 in the U.S. Conventional credit scoring models can score only 201 million of them, leaving 51 million deemed “unscoreable” because they do not meet the traditional criteria of having an update on their credit file in the past six months and having an account that is at least six months old. 

When using VantageScore 4.0, however, 241 million consumers are now scoreable—including 40 million who could not be scored by other conventional models. 

Who are these previously “unscoreables” hiding in plain sight? Which consumers aren’t getting scored by conventional credit scoring models? 

Many are young to credit, with accounts less than six months old: young adults starting their careers or newly arrived immigrants. Then there are dormant customers, with no credit activity in the past six months, but previous credit updates; and the no-trade segment, who’ve lost access to credit and have external collections, public records, and inquiries on their credit files. 

The newly scoreable represent a full 16% of the adult population in the U.S. overall, and as much as 20% in the South. 

Conventionally unscoreable consumers represent a nationwide opportunity. 

The opportunity is particularly significant in two key demographic groups: 

• Millennials, who represent 23% of the U.S. population, yet one in five of them cannot be scored by conventional models. 
• African Americans and Hispanics, less than 72% of whom have conventional credit scores. An additional 12.2 million African Americans and Hispanics are newly scoreable using VantageScore 4.0. 

While not all of the newly scoreable consumers will be ready to take on the responsibilities of managing credit, many are well qualified for certain loan products. In fact, about 10 million of the newly scorable consumers, or a quarter of them, will have credit scores greater than 620. 

Leveraging modern modeling techniques, such as machine learning, VantageScore 4.0 can effectively separate consumers with a clear track record of unfavorable credit behaviors from those who are simply starting to develop, or re-develop, credit histories. The result is a model that accurately scores more people, opening up pockets of opportunity for consumers and lenders alike. 

Millennials: Assessing risk with limited data 

According to Pew Research, Millennials will soon become the largest segment of the adult U.S. population, with their numbers expected to reach 73 million this year. They use credit far less than prior generations; fewer than 60 percent use credit regularly, and 21 percent have “thin” files, meaning they have fewer than three credit accounts, even though they have similar or greater income and assets as compared with their “thick file” contemporaries. 

Remember, this generation now shoulders 46% of all $1.5 trillion of U.S. student debt outstanding. No wonder they’re shy about taking on more obligations. That’s also why Millennials have a disproportionately low share of America’s credit card (14%) and personal installment loan (12%) balances. 

As a result, conventional models which tend to look at depth, breadth, and tenure of credit put millennials at a disadvantage. 

VantageScore 4.0 reduces the emphasis on these conventional factors while bringing in other, more predictive attributes related to trends in the consumer’s credit management activity. These refinements make it possible to deliver highly accurate assessments of risk. 

Two not-so-secret weapons 

VantageScore 4.0 brings two cutting-edge innovations to the table: Trended credit data, and attributes driven by machine learning techniques. 

Among the many new data sources becoming available, perhaps none holds greater promise than trended credit data. Trended credit data captures the trajectory of borrower behaviors over a period of time, as opposed to the typical snapshot at a point in time, revealing additional insights about the consumer’s credit behavior. 

As an illustration, consider two consumers, Connor and Jim: 

Who’s the bigger default risk? 

The conventional “snapshot” view shows that their incomes are similar. But Jim has a thicker credit file with three times as many accounts as Connor, and a longer credit history. Connor’s bankcard utilization rate is 27%, compared to Jim’s 46%. 

You might conclude that Jim, who’s never missed a loan payment, is the better risk. 

However, a review of the trends in credit behaviors over time tells a different story. Connor has been focused on paying down debts, including his student loans, and lowering his utilization of credit, while Jim has been ramping up his balances and utilization. In fact, the data suggests that Jim may have higher volatility in his need for credit, with utilization reaching 75% at one point within the last year. 

As it turns out, Jim is more likely to default over the next 24 months. But without trended credit data, you might never know. 

With VantageScore 4.0, the trended data related to a consumer’s credit management activities become a more significant contributor to the credit score, driving superior predictive performance. 

Machine learning delivers new insights 

While there seems to be a lot of attention on machine learning recently, the truth is that machine learning (ML) has been in use in the financial services industry for many years. 

Recent advances in computing power and ML algorithms provide significant efficiencies in data science, making it possible to look for deeper insights in data by exploring multi-dimensional relationships across many data elements. Rather than simply looking at individual attributes such as available credit, age of collections, and so on, the model can identify relationships between disparate pieces of data that strongly correlate with a borrower’s likelihood of default. 

Identifying those meaningful relationships is only a first step. Building credit scoring models is as much as art as science; it requires significant domain knowledge. In building VantageScore 4.0, the company’s data scientists applied their expert judgment to convert those newly discovered data relationships into attributes that can be understood and interpreted. This development process ensures that the resulting model is intuitive, explainable, compliant with all regulatory requirements, and generates the necessary adverse action logic. 

The insights provided by machine learning algorithms have particular value in cases where the available data on a borrower is limited, such as consumers with sparse credit files, or whose recent credit activity has gone dormant. 

Predictive accuracy: Separating the good risks from the bad

Innovation is one thing, performance as well as safety and soundness is critical. A common way to assess the value provided by a more predictive credit scoring model is through what’s called a “swap set analysis.” For a given risk level cutoff, the swap set analysis compares the Accept/Reject decisions provided by two competing credit scoring models. 

Such an analysis was performed for VantageScore 4.0 versus another commercially available generic credit scoring model, to demonstrate how the VantageScore model’s superior ability in order to assess risks translates to better decisions in credit card acquisitions. 

For a similar risk cutoff set at 1.5% cumulative defaults, VantageScore 4.0 allows an incremental 5% of the prospects to be accepted. A comparison of the swap sets highlights that the population “swapped in” by VantageScore 4.0—those borrowers accepted by VantageScore 4.0 who would have been rejected by the other model—had 45% lower risk compared to the population “swapped out” (i.e., rejected by VantageScore 4.0 but accepted by the other model), based on actual performance in the subsequent 24-month period. 

Further analysis of the credit attributes of the swap-in and swap-out populations reveals a significantly more favorable pattern in utilization and balances for swap-ins despite a slightly shorter credit history and more blemishes in payments historically. The swap-outs exhibit the opposite behaviors, with balances and utilizations ramping up to nearly twice those of the swap-in consumers. 

Opening up the black box 

VantageScore Solutions is the only credit score model developer to publish detailed results of its annual model assessments, along with whitepapers, user guides, and other communications aimed at model users and consumers. 

VantageScore firmly believes in the value of transparency and the importance of knowledge sharing to enable model users to gain comfort with how the model works and performs and to complete model governance and other approval processes. 

In light of the changing consumer credit marketplace and the fierce competition among lenders, this level of transparency is vital. It enables the users of credit scores to find a scoring model that offers the greatest performance and leads to the most successful business decisions. Such a model has the potential to open up new lending opportunities among previously unscoreable populations. It also allows lenders to take smarter risks across an entire portfolio. 

Anything but Conventional: By Leveraging New Modeling …

This research analyzes the following: There are tens of millions of …

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2018 VantageScore Market Study Report

A 2018 report authored by Oliver Wyman, a global leader in management …

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VantageScore 4.0 Fact Sheet (2018)

An overview of the latest VantageScore 4.0 credit scoring model.

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VantageScore – 2018 Innovation Timeline and ‘”First to …

  From 1987, when the first generic credit scoring models were …

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2019 Model Performance Assessment of VantageScore 4.0

A key element of sound model risk management practices is to ensure that models …

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that was established in November 2014. David believes that the sophisticated risk management, capital management, and regulatory exposure management communities have an unmet need for a holistic, enterprise-wide approach toward risk assessment in a transparent, dynamic and open forum fashion. 

David has a unique background as a veteran senior financial services analyst. Over his 30+ years on the Wall Street buy & sell sides and as a senior member of the independent research company CreditSights he has covered investment & financial risk advisory from both the corporate debt & equity research disciplines. 

On the buy-side he started his career at New York Life Insurance Company. On the sell-side for 15 years he worked at various firms including: Drexel Burnham Lambert, J.P. Morgan Securities, UBS Securities, Smith Barney Inc., & Credit Suisse First Boston. David made the Institutional Investor All-Stars from both the debt (1995) and equity sides (2000).

We are grateful to David for taking the time to share his insights on past economic issues and how it impacts both the present and future economy.

1) There is much speculation about whether the economic recovery has peaked and we are in for a cycle change. In your estimation, where are we in the consumer credit cycle?

VRA: The consumer credit cycle is long in the tooth as they used to say. So, there has not been a major consumer default cycle since the last credit crisis, which mostly bore out in the mortgage and subprime mortgage loan markets. The credit card cycle has not really been tested since the early 2000s and somewhat so during the mortgage crisis, but not as bad as it could have been if the Fed had not hyper-eased its monetary policy and bailed out the banking system as well as most of the capital markets and financial system. The auto loan cycle has not been bad since the late 1990s after several mono-line subprime/near prime lenders and other bank car lenders/lessors hit the wall with all kinds of losses in new and used car finance. Then the biggest consumer loan market, the student loan market, continues to have the highest delinquencies of the five major consumer groups (home mortgages, credit card, auto loans, student loans and unsecured consumer). Student loan borrowers have taken out more debt than the credit card or auto loan sectors as colleges have become unaffordable for the middle class, thus the lending binge.

So, to wrap up, we are in the later innings of the consumer credit cycle as the Fed’s easy money policies and thus lower borrowing rates have lessened the burden of carrying all the debt accumulated. We are at or near the all-time highs of debt to disposable income, so any catalyst (higher rates, slower economy leading to higher unemployment) can tip the Humpty Dumpty consumer over, and then fall to pieces.

Then there is the millennial generation and their untested-through-a-cycle behavior in repaying debts which are concentrated in credit cards and student loans. Millennies, as we call them, are a breed apart in their consumption of luxury items at such a young age (travel, restaurants, experiences) and have lower savings rates as a result given their “live for today” generational anthem. So, lots of different potential inflection points that could coalesce at the same time and lead to a more brutal consumer default recession than most prognosticators have factored.

2) Before the economic crisis in 2007-8, you issued a research statement warning about artificially inflated credit scores. What role did that play in ensuring defaults?

VRA: Fake mortgage company credit scores were a problem for many a large mortgage lenders and securitizers like Countrywide Credit, Washington Mutual and others not with us in the mono-line subprime originator business. It was not as much of a factor in the credit card business as it was a different set of borrowers in the prime and super prime sectors that did not need to “prime up” their score. With the GSEs, Fannie Mae & Freddie Mac, still in conservatorship, the conventional, smaller size mortgages have not ramped up at the big banks. Big banks have focused on the jumbo mortgage market of high prime to super prime mortgage borrowers. And these higher net worth borrowers are credit seasoning low and well. Some mortgage banks like Quicken Loans have stepped into the conventional space as originators that quickly securitize to the institutional investor market. The big banks have ceded this turf since conventional mortgages have onerously high capital charges on mortgage servicing assets. And the higher retained interests under the Dodd-Frank Act and the old bad memories of mortgage put back costs that have caused the big banks to underplay conventional mortgages. So no wonder the mortgage market is underproducing relative to demographic growth and demand even under an adjusted level to remove NINJA (no income, no job) and other “scratch and dent” unjustified loans of the mid-2000s mortgage craze.

So, to wrap it up, the fake credit scores did egg on the mortgage crisis and defaults, but much of that has been blunted off in this go-round as discussed above. More likely the next credit default crisis will be with credit cards, student loans, unsecured consumer, and auto loans as terms have lengthened towards 60 months and loan sizes have almost doubled with all the newfangled radar/GPS mapping/ultra stereo systems and other electronic gadgets that can add another $5K to $10K+ to a car’s price. 

3) What economic factors are you watching closely today?

VRA: Unemployment rising to higher levels, 6-9 percent+, is always the negative indicator. But it is a huge lagging indicator. So, Millenny analysts looking for that will be way behind the action curve. We at VRA look at lagging net charge-offs in cards with a 12-month to 18-month lag. If those figures pierce 4 percent to 5 percent, then we are in for a load of credit default trouble. Usually once it heads that way, it is a one-way stop to 6 percent+ in losses on a coincident basis. At 6 percent, the credit card math starts to break down and the securitization of card pools by bank issuers and other issuers are not as easy to palm off on institutional investors. Lagged charge-offs will be linchpinned by the factors cited in the previous question. So higher short-term rates and/or slowing economics. With the presidential race almost in progress with less than two years to go, this may be delayed as the the Fed seems to be on presidential elections monetary policy hold. 

4) Has regulation effectively guarded against a repeat of the overly risky practices that led to the mortgage crisis?

VRA: To some extent, yes. As noted, higher retained interests on securitization, higher capital charges on mortgage servicing and other high-risk I/Os and other residual interests have put somewhat of a squash on the near-prime to subprime mortgage lending business. But banks and especially Wall Street banks are highly creative especially in arbitraging the trifecta of company concerns as how to optimize or minimize: a) capital impacts, b) tax consequences and at the same time c) preserve or improve NRSRO ratings. This coup can be highly lucrative for high fees for the underwriting capital markets banks, and a good spread business for the bank originators that eventually hold in a packaged securitized form.

Right now we at VRA believe there is much more risk in the unsecured consumer loan market that is bootstrapped by smartphone/instant lending finance and lending. And this is going on in the commercial area with regards to direct lending by shadow banks including hedge funds/private equity companies/BDCs (business development companies) and other non-bank banks that are being warehoused and lent to underwritten by the big bank and other financial player communities. This is related to the rise in leveraged lending arenas that US bank regulators have their antenna up on, but have not specifically identified the problem or problem banks and shadow banks. We at VRA have some early thoughts on the culprits and the eventual problem banks, but that may be for another Q&A.

5) What is the state of independent financial research in today’s market?

VRA: Like other market players, it is an evolution for independent financial research. The business has been traditionally a subscription-based payment business. But with MiFid II standards required in the European region for buy-side firms to pay for all research (including broker produced), the available budgets to sell into for the independent providers has shrunk, which was an unforeseen consequence. Low rates have made it more difficult for fixed-income money managers/insurance companies/pension funds to pay for research, as that has also put a huge damper on their management fees. So the pressures are formidable.

We at VRA have a healthy subscription business. But the real opportunity is in the bespoke consulting business, where clients’ core challenges in various parts of risk management can be serviced in a unique/value-added/business-useful approach. Independent research needs to be positioned as a trusted advisor with custom-delivered features. A traditional subscription approach to research reports is too impersonal, difficult to justify and thus mostly a relic of the credit crisis aftermath.

Viola Risk Advisors seeks to be the most business-useful risk management consultant for global companies interested in a holistic approach to stakeholder stack analysis focused on the capital structure stack goals of debt and equity investors. And business-useful for the risk-exposure management needs of counterparty risk/enterprise risk management and supervisory authority/regulatory risk management clients. Only by addressing these interrelated concerns in a clear and transparent way in writing can companies optimize their overall risk management goals.

Viola Risk Advisors, LLC is driven to be the best research provider of holistic, enterprise-wide risk management and capital structure investment views focused on “Risk Advisory on Global Companies for Global Companies.” As part of this endeavor we will focus on key risk exposures including: counterparty, regulatory, and investment securities & other financial instruments owned by debt & equity investors.  

These risk exposures are of most concern to our worldwide customer base that spans various communities including: counterparty/enterprise risk management, institutional investment managers, governmental and regulatory authorities, corporate strategy, and ultra-high net worth investors.  As pragmatic thought-leaders, our analysis and reports will be “business-useful” by providing easy-to-use, leading-edge, unique, value-added, and actionable trading & exposure management views.  In combination, this research will allow risk managers, investors, regulators, and corporate strategists the ability to make the best risk-investment-strategic decisions and reap the best outcomes.

Tune in, and please do share amongst your colleagues in your credit circles.

VantageScore Podcast Series

Last month, VantageScore launched its own podcast series that showcases open conversations with industry influencers. These podcasts help cement VantageScore as a thought leader in the field and drive the conversations around credit scoring for the purpose of continuing to increase consumer and industry awareness of the VantageScore brand.

The first podcast guest was Dara Duguay, CEO of Credit Builders Alliance and one of the key leaders helping consumers establish credit in the United States. Her work has helped millions of consumers achieve their financial goals and become empowered users of credit.

Just recently launched, the second podcast features well-known credit expert John Ulzheimer, who has more than 25 years of experience in the consumer credit industry. You probably know him from the “New York Times”, “Wall Street Journal”, CNBC, “The Today Show” and even in our very own Score newsletter.

Links to the most common podcast platforms are as follows:

iTunes

GooglePlay

Spotify

Consumer Credit Podcast Series

In partnership with “American Banker”, we just began a four-part series focused on credit scoring in the credit card industry. Aimed at those who have (or have yet to) attend the Consumer Credit Summit at Card Forum, these podcasts highlight leaders in the credit scoring industry who have impacted the cards market and their thoughts on what is to come.

The first podcast in the series “The Changing Dynamics of Consumer Behavior and Performance in the Card Market” features Nidhi Verma, vice president, TransUnion who discusses the demand for healthy credit and greater access to it with Penny Crosman, editor at large, “American Banker”.

To listen to their discussion, visit: https://www.paymentssource.com/conference/cfe-2019/podcasts

Next up is Kevin Yuann, vice president and general manager, at NerdWallet, so stay tuned!

Competition for FICO would foster a more sustainable housing system

Roll Call op-ed, April 8, 2019

by James Lockhart, first director of the FHFA and senior fellow at the Bipartisan Policy Center

OPINION — Housing policy is suddenly back in the news. The Senate Banking Committee held hearings on housing finance reform recently and the Trump administration wants federal agencies to draft reform plans for mortgage securitization giants Fannie Mae and Freddie Mac. But after 10 years in conservatorship, winding down these government-sponsored enterprises and restructuring the mortgage market will be Herculean tasks.

We can start by revisiting a proposed regulatory rule on credit scoring.

Fannie Mae and Freddie Mac require institutions that either sell them loans or service their purchased loans to use credit scores produced by the Fair Isaac Corporation, or FICO. These scores provide a metric to predict borrower loan performance. The Federal Housing Administration also requires lenders seeking their mortgage insurance to use FICO scores, effectively creating a credit-scoring monopoly for nearly every mortgage application in the United States.

While FICO was once widely relied on as the only provider in town, alternative credit scores are on the rise. Some include rental housing, utility and other payment data, resulting in more credit information and, for many consumers, potentially higher scores.

The Federal Housing Finance Agency, as Fannie and Freddie’s regulator and conservator, has long been exploring the use of such alternatives. After decades studying newer scores (and a major housing crisis), there must be something better. Congress thought so. To end the existing monopoly and hasten the use of newer models, Congress passed a law last spring that included a provision with a simple and straightforward title: credit score competition.

Regretfully, FHFA ignored Congress’ intent in its proposed rule-making, currently out for comment. The agency proposed restrictions that would effectively preserve the current outdated models and a government-sanctioned monopoly, including the prohibition of “an Enterprise from approving any credit score model developed by a company that is related to a consumer data provider through any common ownership or control, of any type or amount.” This restriction effectively put the brakes on FICO’s main competitor, VantageScore, although they compete in other credit categories.

In all my years of federal service, I have never seen a regulation, or even a proposed rule, that presumed to dictate such targeted exclusion from what should be a competitive market.

There are and will be other financial technologies that will want to enter the mortgage credit scoring market. Yet the proposed rule’s overly restrictive business assessment requirements, coupled with a strange statement that the selected scoring model will not be reviewed for seven years, may prevent more predictive and inclusive models in the future.

FHFA didn’t just ignore Congress — it also snubbed the Treasury Department, which serves on the agency’s advisory board. In 2017, the Treasury released a major report noting the potential for these new approaches “to meaningfully expand access to credit and the quality of financial services.” Of course, adopting these new models will require time and money from Fannie, Freddie and other market participants. There are serious implementation and operational challenges to overcome that should not be taken lightly.

However, Fannie Mae and Freddie Mac’s key missions are to provide liquidity and stability to the mortgage market and to promote affordable housing. New credit score models and competition could lead to more consumers with scores, safely expanding access to credit. With more granular data, new models may also more accurately assess one’s ability to repay mortgage debt.

Mortgages for affordable housing must be sustainable. A decade ago, the failure of Fannie and Freddie caused hardships and foreclosures for millions of Americans. The newer models should help to guard against these mistakes of the past by empowering lenders with more accurate and predictive measures of loan performance.

It’s time for a fresh start on many fronts, which includes ending the conservatorships of Fannie and Freddie and restructuring the mortgage market. An easier but effective move is to withdraw this misguided proposed rule. FHFA needs to write a new rule that truly promotes innovation and competition in credit scoring to foster a more sustainable housing system.

James B. Lockhart III was the first director of FHFA and is now a senior fellow at the Bipartisan Policy Center.

The Bipartisan Policy Center is a D.C.-based think tank that actively promotes bipartisanship. BPC works to address the key challenges facing the nation through policy solutions that are the product of informed deliberations by former elected and appointed officials, business and labor leaders, and academics and advocates from both ends of the political spectrum. BPC is currently focused on health, energy, national security, the economy, financial regulatory reform, housing, immigration, infrastructure and governance. Follow BPC on Twitter or Facebook.

Lenders, investors and regulators would be wise to read and internalize the Moody’s report, however they should also consider a number of underlying factors.

Firstly, the notion that average credit scores are rising should come to no one’s surprise. If scores weren’t improving, there would be something wrong with the models.

Credit scoring models are based on the premise that past credit behaviors are indicative of the likelihood of a future default. The models include factors related to a consumer’s use of credit, payment history, level of indebtedness and availability of credit, and credit seeking behavior, among others.

With the Great Recession in the rearview mirror, many of the negative marks such as delinquencies, foreclosures and even bankruptcies, are farther back in consumers’ credit history with a smaller impact on credit scores, or have dropped off the credit files entirely.

Unemployment is at a 50-year low, home prices have, on average, recovered after hitting rock bottom, and cost of borrowing remains low; all positively contributing to a strong credit performance. At the same time, post-crisis, consumers have been more prudent in their use of credit, and credit underwriting standards have improved (the ability to repay requirements with most mortgages is one driving force of this trend). Collectively, these all point to higher credit scores.

In short, credit scores are pro-cyclical in nature. They are derived from historical data and respond to it. Credit scoring models are designed to reward a consumer for recent positive credit behavior. With the passage of time, recent behavior overshadows a past financial misstep or challenge. Conversely, as the credit cycle eventually shifts downward, credit scores will respond and begin to reflect the changes in consumers’ behaviors.

Secondly, it is imperative to remember that the objective of a credit scoring model is to “rank order” the total pool of scoreable consumers based on their risk. The score is only a relative measure of risk. In other words, we are individually compared with the total scoreable population. The meaning of the score, or the actual level of risk, changes in response to the overall level risk in the system.

For example, the default rates experienced for loans with a specific credit score would have been significantly higher in 2008 compared to what it was in 2018, reflective of the very different points in the credit cycle and the level of risk in the system. Well-built models maintain their ability to “rank order”, with lower credit scores resulting in higher levels of risk compared to higher scores. However, these models are not designed to capture the changes in the actual risk levels.

Therefore, lenders must be diligent, anticipate and react to changes in risk levels, adjusting credit score cut-offs as well the broader underwriting criteria (Debt-to-Income, Loan-to-Value, etc.) to manage a portfolio’s expected performance under changing economic conditions.  Lenders should validate models regularly and closely monitor performance of their portfolios.

The OCC provides extensive supervisory guidance on model validation best practices.[1] In particular, the OCC notes that validations of credit scoring models should occur at least annually if not more often, and that, “where models and model output have a material impact on business decisions, including decisions related to risk management and capital and liquidity planning, and where model failure would have a particularly harmful impact on a bank’s financial condition, a bank’s model risk management framework should be more extensive and rigorous.” If volatility were to increase rapidly, lenders will consider model validations more frequently than annually.

Importantly, this work must be done for models developed both internally and by third parties, such as models built by VantageScore.

There always will be some bad apples in the bunch when it comes to risk management. A consumer lender who underwrites solely on credit scores will and should be exposed because credit scores are not intended to be a substitute for prudent underwriting criteria. Scores should only be used as an integrated component of sound underwriting standards.

The bottom line is that yes, credit scores are improving alongside the broader economy and that is to be expected. Lenders must refrain from complacency or loosening standards too much, even as volatility remains low, because we all witnessed what can happen should stress be introduced into the economy.

Barrett Burns

CEO and president, VantageScore Solutions

(1) Supervisory guidance on model risk management, OCC 2011-12, 2011

1. FinTech has only become a household word in the last 10 years. Was there a particular moment or company that made FinTech a commonly understood and relatable concept?

The short answer is yes, but it’s important to give it some context. The date we chose for the book is 2008, which was significant for a number of reasons. First, there was a little thing called the 2008 financial crisis; banks, as we understood them, were put under an enormous amount of pressure. Stand-alone investment banks like Bear Stearns and Lehman Brothers went under, while others were was forced to merge. This was coupled with exponential oversight in financial services through Dodd-Frank and the formation of the Consumer Financial Protection Bureau (CFPB). Quickly, innovation became a dirty word inside banking because it was associated with complex derivatives that nobody could understand.

At the same time that banks were retrenching, Facebook had just hit a billion users and launched its app store. Meanwhile, Apple had sold 100 million phones and launched the app store. It was those inventions that spurred much of the tech, and subsequently FinTech, world as we know it today. That combination of a banking vacuum coupled with rapid tech innovation created the conditions for FinTech to thrive. This is when PayPal goes mobile and really takes off, later buying Venmo. It’s when Credit Karma and LendingClub launch. There was, of course, FinTech before 2008, but if you had to pick a moment in time and a place in history, it would be the coinciding of the financial crisis and the explosion of innovation.

2. In those 10 years, financial technology has advanced and taken off significantly.
How has it changed since first being introduced to the mass market?

It’s changed in a myriad of ways. I’d say the most obvious answer would be to talk about how 2008 created an explosion of banking, both literally and figuratively. By this, we mean things that you traditionally went to a bank for were being split into separate apps. So, if you’d previously gone to the bank to access your checking account, or your savings account, to get a mortgage, or to look at lending options, you could now do those things individually through apps. It’s a bit of an oversimplification, but when the app store came out, that “app mentality” migrated to finance, and over the last 10 years, it’s been all about unbundling the bank. All of those services above that a bank offers were siloed and there were dozens of mobile apps to help you invest or save, or cut down on credit card debt, among other services.

I think what’s changed in those 10 years is that now, after totally dis-assembling a bank, FinTech is trying to put the bank back together. So the next 10 years will be defined as the rebundling of the bank. Apps like Robinhood, MoneyLion, Elevate…they’re all offering different parts of that continuum.

3. To the point above, there have been some FinTechs like Venmo that have been wildly successful, while others have fallen flat. In some ways, the “unicorns” have been identified — so what’s next for the industry?

Last year was a record investment year for FinTechs. Over $40 billion of venture capitalist money was invested in FinTech, but it was also at a five-year-low for early-stage investing. If you put those two stats together, it shows venture capitalists have basically decided that we’ve run out of new areas to discover, and it’s time to start backing your winners and consolidating your markets.

FinTech is good at stripping the costs out of banking. However, it also means FinTechs are operating on razor-thin margins and have to achieve scale in the longterm in order to be successful. This is what will drive greater and more rapid consolidation and, as we were discussing before, the “rebundling” of the bank.

4. Despite the success we looked at above, there’s been a very low rate of acquisition of big banks buying FinTechs. Why do you think that is?

I think there are quite a few possible reasons, the first being that banks’ instincts have been to just build these apps themselves. Much like the robo-advisors through Schwab and Morgan Stanley, I think there are plenty of financial institutions who believe it may be a better cultural fit for their company to create their own as opposed to acquiring a different one. Secondly, many banks have chosen to partner with FinTechs, which is affording them the benefits of innovation without the costs of acquisition.

Finally, I think it could be strategic on the banks’ part. They could just be holding out to see who the “winners” are before making their moves. I think we’ll see more activity after this period of consolidation. So for now, it’s a bit of a waiting game.

5. For the big banks that have acquired financial technology platforms, is there an opportunity to use this as a way to shift their culture?

Absolutely. Is it a coincidence that Goldman Sachs — which bought Honest Dollar and Clarity Money — recently changed its dress code? I think FinTechs are helping banks appeal to a younger demographic as both a service and as a potential employer. I think FinTechs can help traditional financial institutions push the envelope in reimagining their brand and image, and we’ll see more of that going forward. 

Using trended credit data allows more modern credit scoring models like VantageScore 4.0 to analyze credit behavior over time, providing deeper insights into borrowing and payment patterns. By leveraging machine learning, VantageScore 4.0 also has been able to strengthen its performance levels and provide it with the ability to score approximately 40 million more consumers than conventional models, without sacrificing predictability.

Let’s see what this breakdown of consumers looks like below.

Based on the U.S. Census, the aggregate U.S. population was 325 million people in 2018:

Of which, 252 million are credit-eligible, meaning they are 18 years old or older:

However, conventional scoring models can only score about 201 million consumers. Meaning 51 million consumers are currently unable to get a score from conventional models because they do not meet conventional scoring criteria (i.e., these consumers have not had an update or report on their credit files in the past 6 months and they do not have an account that is at least 6 months old):

Are you getting the picture? The universe of borrowers is getting smaller and smaller, especially when they are scored with credit scoring models that simply haven’t gotten with the times.

On the other hand, by using modern credit score modeling methods, VantageScore can accurately score 40 million more consumers, expanding the scoreable population of consumers to 241 million:

The point of this visual representation is to show potential lender portfolio gain when using the right credit scoring model (and loss when using the wrong one). If that didn’t sell you, let’s put it another way:

40 million consumers — who can be scored using more innovative scoring methodologies — is about the same amount of the entire population of California…the most populous and biggest state in the U.S. Is your business willing to give up that many people?

Below is a detailed infographic of who is defined as a “thin-file millennial” and the opportunities for credit within this population. If you’d like to share this infographic amongst your constituents, social media channels, etc., please contact us on LinkedIn. 

That was one of the key messages from Dr. Emre Sahingur, VantageScore Solutions’ Senior Vice President of Predictive Analytics, at a standing-room-only breakfast presentation at CBA LIVE, the annual meeting and conference of the Consumer Bankers Association (CBA).

We are proud, longtime sponsors of the CBA. Many of its members are users of our credit scores.

Emre’s message keyed in on the fact that while recent years have been kind to the consumer lending industry, it would be wise to acknowledge the possible headwinds the industry is likely to be facing in the future.

Americans are carrying more consumer debt than ever before, with non-mortgage debt expected to top $4 trillion in 2019 and mortgage debt to exceed $10 trillion.

Credit card balances continue to grow; having surpassed $1 trillion in 2018. Unsecured personal loans have shown a significant resurgence, with balances estimated to be around $160 billion by the end of 2019.

While new originations have been healthy, we also have observed that originations have been moving toward riskier segments in recent periods, and that trend is expected to continue.

The impact of new competition from FinTechs is another important factor. I attended the Marketplace Lending Association CEO meeting in Washington, D.C., and came away highly impressed with their ability to nimbly adopt new underwriting and platform approaches.

As of late 2017, FinTechs had about 30% of the market, up from 3% just 5 years ago.

Such offers have become a significant alternative to credit cards, for debt consolidation or other purposes, with millennials being an obvious target.

And then there’s the possibility of a cycle change. It is not a question of “if.” It’s a question of “when” the next credit downturn will occur.

In this environment, lenders need to focus on credit quality, find safe and profitable business opportunities and have the ability to respond to changes in credit use behaviors.

As Emre stated during his CBA LIVE presentation, the key to remaining competitive in this environment will be to use better data and predictive analytics to target the best borrowers and fine-tune credit decisions. Emre closed with a question that I posit to you:

Are your current risk assessment tools addressing these needs and realities?

VantageScore 4.0 scores more consumers with greater accuracy. The model expands a lender’s universe of borrowers and fine-tunes lending decisions with trended credit data.

We’d be happy to provide the proof. Just ask!

Regards,

Barrett Burns

CEO and President, VantageScore Solutions

The infographic below is a visual and simple breakdown of who makes up this pool of consumers and makes a case for why lenders should expand their portfolio using modernized risk assessment tools. If you’d like to share this infographic amongst your constituents, social media channels, etc., please contact us on LinkedIn. 

Coming off a successful SFIG Vegas, we were able to gather his thoughts on his new role and the latest conference.

1. Last month’s SFIG Vegas conference had record-setting attendance. Why do you think there was a surge this year?

There is a lot of energy around SFIG right now. The credit cycle has gone on for a long time, and that means, for a lot of folks, business has been good. But people are wondering “what’s around the corner?” I think the securitization industry is pretty well-prepared for a slowdown, but everyone is certainly doing their due diligence. Public policy remains a big question mark as well, and so members are engaged on that front, too. Finally, the industry is definitely interested to see what the future holds for our organization. 

2. At the conference, you mentioned that housing reform is on the horizon yet still not set in stone. There have been many who have voiced recommendations for reform, including a proposal from one of your former posts at Milken. What recommendations are you leaning toward, especially from the securitization industry’s perspective?

Housing finance reform has so many difficult issues. Everyone should be concerned with questions such as how do we safely ensure that all communities have access to safe and decent housing, especially during a time of great demographic change? And, of course, what is the right role for the taxpayer? At SFIG, my job is to advocate for a system that can work through all business cycles and that, ideally, has clearly defined roles for the government and for private capital.

3. As the newly minted president of SFIG, what are your priorities for the coming year?

Making sure we are seen as a resource for policymakers and as a leader in our industry. If the securitization industry is willing to fully embrace and appreciate how essential its role in the economy is, and take that responsibility seriously, we can completely change the way financial services serve the real economy. And we can have very productive discussions with policy-makers along the way.

4. SFIG is uniquely positioned to weigh in on issues with a holistic viewpoint that is comprehensive of the secondary market. How might this be leveraged to make policy changes and improvements?

We need to both talk to and listen to policy-makers.  Industry participants have one perspective, and they are responsible for a set of stakeholders. Policy-makers have an entirely different set of stakeholders.  But, at the end of the day, voters and the people in the real economy that we serve are the same people. If we just work a bit more to understand that, we can better connect with the public and with policy-makers in Washington.

5. What do consumers and laypeople need to know about the securitization industry and what your members do?

That if we do our job well, we add tremendous value to the economy. Conversely, when we make mistakes, those mistakes have real consequences. We should hold ourselves accountable, in part so that others don’t have to.

Mr. Bright has extensive hands-on experience as a policymaker, practitioner and leader across all aspects of the securities industry. He joins SFIG from Ginnie Mae, where he was executive vice president and chief operating officer, managing all operations for Ginnie Mae’s $2.0 trillion portfolio of mortgage-backed securities. He brings a track record of policymaking achievement from his time on the staff of U.S. Senator Bob Corker and the Senate Banking Committee. Prior to joining Ginnie Mae in 2017, Mr. Bright was a director at the Milken Institute’s Center for Financial Markets, where he led the institute’s housing program. He was previously a member of BlackRock’s financial advisory unit.