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Are lenders missing out on Millennials?

One of the fundamental truths about credit score models is that they must periodically be refreshed in order to maintain a peak level of predictiveness. Not only does the data available to modelers get better and model building techniques improve, but the mix of credit products changes, as do consumer behaviors and the way they treat their financial obligations.

For example, let’s take a look at the massive increase in student debt. Models built prior to the Recession didn’t factor in how consumers with large student debt obligations would influence consumer credit behavior.

For this reason, our data scientists took a deep dive into the credit usage patterns of different generations and they layered on extra factors indicative of financial health (e.g., income and assets). By doing this, we found that there may be a paradigm shift that methodologies underpinning older scoring models are not considering.

Included in this newsletter is a deeper dive, but I’ll topline some of the important findings:

  1. Historically speaking, the assumption was that those with higher income and assets were associated with thicker credit files and more credit usage (i.e., thick file consumers have three or more credit accounts reported and thin file consumers have two or fewer credit accounts). Relatedly, lenders often viewed thin file consumers as more risky than those with thick files and they are often placed into the highest risk products (think: high interest rates and modest loan limits).
  2. However, what we are now seeing is that Millennials with thin files – unlike any other generations before them – on average have income and asset levels consistent with their thick file counterparts.
  3. Notwithstanding this, conventional models and lending strategies might actually be penalizing them simply because that’s historically how thin file consumers have been treated.
  4. Accordingly, users of credit scores for lending decisions should carefully assess whether they should reconsider models based on legacy beliefs.

This is an opportunity for lenders to lean into the Millennial generation and get a firmer understanding of how they are handling their credit health. From a credit scoring standpoint, it also speaks to the importance of trended credit data.

Here’s why: trended credit data examines the longer term trajectory of credit behaviors as opposed to a snapshot or single point in time from the prior month. A model that uses trended credit data (like VantageScore 4.0) is better able to understand more recent credit behaviors and relies less on some of the more conventional attributes used by models that focused on the tenure, breadth and depth of credit usage – which, by definition, negatively impacts those who are new to credit.

In other words, the richness of trended credit data allows our data scientists to extrapolate predictive behaviors from consumers who choose to open less credit accounts.

We’ll share more of these types of insights in the coming weeks and months. For now, we’ve highlighted some insights here, but I also encourage you to follow us on LinkedIn where we can continue the conversation.

As in prior years, we won’t be putting out a December newsletter so this will be the last one for 2018. It was a great year for VantageScore in our 12th year of continuous growth, and we thank you for supporting our mission and reading our newsletter.

Happy holidays to all,

Barrett Burns

CEO and President, VantageScore Solutions

Millennial credit habits: a major shift  

Entire lending strategies and credit scoring models have typically been built assuming that those with thin files (consumers with less than 3 credit accounts) are higher risk than those with thicker files.

This stood to reason because consumers with deeper experience with credit, tended to exhibit other healthy credit behaviors and have more income at their disposal.

This is not the case with Millennials. Recent data shows they are writing their own story when it comes to using credit.

The first major indicator of a credit behavioral shift is that thin file Millennials actually, on average, have income levels similar to their thicker file counterparts, which runs contrary to every generation before:

If you peel back the onion, you will see that the dominant presence in a Millennial’s credit history is their student loan account. While this isn’t surprising, thinner file Millennials (who again have displayed relatively high-income levels) choose to limit the number of credit accounts they open – presumably because they want to pay down their student loan debt.

This is a very healthy credit decision on their part, yet older models and lending strategies penalize them simply because they haven’t opened up new loan accounts.

That’s where the power of trended credit data comes into play. More recent credit behaviors over a time series are taken into greater consideration with trended credit data. And the age of a person’s credit accounts is less important than a point-of-time reading methodology employed by most scoring models.

A comparison between VantageScore 4.0, which includes trended data attributions, and VantageScore 3.0, which does not use these attributes, shows how newer factors become more influential in the calculation of a person’s credit score.

Trended data attributes change the focus of credit scoring models to better understand actual credit management behaviors over time versus static snapshots, where higher value is placed on tenure and types of credit used.

Obviously, in the sub-prime population segment, payment history and high utilizations continue to be the dominating characteristics to determine poor creditworthiness.

However, once these issues are cleared (i.e., no payment history issues on the consumer), a credit scoring model using trended credit data considers a consumer’s recent credit management behaviors as major factors in determining credit risk.

Conventional models, or static models, never had the ability to see past the present state of accounts and can only determine the most recent signs of performance on a consumer.

The newest users of credit, Millennials, choose to use credit more prudently. Are they being treated fairly by older static models and by lenders who often only give them credit cards with low limits and high interest rates?

Or, should more emphasis be placed on credit behaviors and recent changes in credit management to determine the risk of these consumers?

To be sure, lenders have a unique opportunity to better understand these consumers and be their champion. By showing them you’re “not their Mom and Dad’s lender,” financial institutions can achieve higher levels of brand loyalty and product satisfaction.

Security freezes and credit scores  

In May 2018 President Trump signed into law the Economic Growth, Regulatory Relief, and Consumer Protection Act. The Act, among other things, amends the Fair Credit Reporting Act (FCRA) and provides consumers with a substantial expansion of their rights as it pertains to protecting themselves from identity theft and credit fraud. One of the primary changes to the FCRA is that as of September 2018 consumers have the right to add a security freeze to their credit reports at no cost.

What’s a Security Freeze?

A security freeze, also called a credit freeze, is the process whereby the credit reporting companies (CRCs) will restrict access to your credit reports and will not allow lenders with whom you do not have an existing relationship to pull them. Metaphorically speaking, your credit reports will have been taken out of circulation when they’re frozen.

The consumer has control over when to unfreeze or “thaw” their credit reports and put them back into circulation. This will generally happen prior to a legitimate application for credit or some other benefit that will generally result in a credit inquiry. Once the credit report has been procured by the lender or service provider it can be re-frozen by the consumer, at no cost. The benefit to freezing credit reports is fraudulent credit applications can’t result in new accounts being opened because the lender cannot access any credit reports or credit scores.

But What About My Credit Scores?

One of the most common questions that come out of any meaningful change in credit reporting practices is, “how will this impact my credit scores?” Specific to this recent amendment to the FCRA the question is likely, “what happens to my credit scores if I freeze my credit reports?” The answer to the question is: “not much.”

Credit scoring models do not consider whether or not your credit reports are frozen. And, credit freezes don’t prevent lenders and debt collectors from reporting information to the credit reporting companies. As such, freezing your credit reports does not increase or decrease your credit scores. What it will do to your credit scores, however, is restrict their access.

When addressing the benefits of security freezes most of us describe them as restricting access to credit reports, rather than credit scores. While it’s true that security freezes will not have any impact to your credit scores, they will most certainly prevent them from being shared with a 3rd party. In fact, that’s the intent of the security freeze, which is to prevent an unauthorized lender from accessing any credit-related information that can lead to a newly opened fraudulent account.

Can A Lender Still See My Credit Scores?

Because a security freeze prevents unauthorized access to your credit reports and, because credit scores are entirely dependent on credit report data, your credit scores also will not be shared with a 3rd party while your credit reports are frozen. The exception to the rule is if one of your existing creditors wants to see your credit scores. Existing lenders will still have access to your credit scores, just like they will still have access to your credit reports.

So, if you freeze your credit reports and you want to apply for a new loan you’ll have to thaw your credit reports. That will allow the lender to have full access to your credit reports and credit scores. Once you’ve completed the application process, you can re-freeze your credit reports, which will also restrict access to your credit scores but will not change them by even one-point.

Credit scoring challenges & solutions

VantageScore 4.0 Offers Solutions to 3 Common Consumer Credit Scoring Challenges
as published on TransUnion.com on November 8, 2018
by Gene Volchek

At TransUnion, we believe that consumer access to multiple credit scores is key to expanding economic inclusivity. Earlier this year, we looked at how the use of additional credit scoring models in the mortgage market could address unequal lending access among disparate economic and ethnic groups. We also examined the explosion of consumer engagement with a variety of credit scores via direct financial institution relationships, freemium sites and the credit reporting agencies (CRAs), ushering in a new era of consumer credit-savvy and empowerment.

These developments are generating exciting innovations in credit scoring, with the potential to help more individuals and families gain access to capital and improve their standards of living. One of the more promising movements is the incorporation of trended data into credit scores. Trended data, also known as historical or time-series data, illustrates more than a view of consumer debt at a single point in time. By using trended credit data, lenders can incorporate up to 30 months of account history on loans and credit accounts, exposing patterns that can help them make better-informed decisions.

For example, the VantageScore 4.0 credit score leverages historical credit data like changes in balances and actual payment amounts. This information allows lenders to see, for example, whether a consumer is paying off credit cards each month or carrying a balance — and whether that monthly balance is trending downward or upward. VantageScore 4.0 credit score also offers solutions to three specific challenges that traditional models present to lenders and consumers alike:

Challenge #1: Consumers who are new to credit or have limited credit histories are often outside the reach of conventional scoring models, resulting in lost opportunity for businesses and individuals.

Solution: Using trended data, VantageScore 4.0 enables lenders to uncover risk value hidden in plain sight, providing the confidence necessary to offer financial products and services to an expanded universe of credit-worthy consumers. In turn, more than 30 million additional consumers gain access to relevant financial products and services that can help them improve their quality of life.

Challenge #2: Traditional credit scoring models can be fairly inconsistent as they are requested through one CRA versus another. This can be confusing for consumers, and depending upon the chosen score, may exclude them from certain credit opportunities.

Solution: VantageScore 4.0 is the first and only credit scoring model with trended data leveled across all three of the major CRAs, offering a consistency that other scores lack. Better alignment in consumer credit scoring allows for more confident lender and consumer decision making.

Challenge #3: Enhancing predictive scoring model performance without creating additional business and market risk.

Solution: VantageScore 4.0 delivers solid predictive risk performance across consumer credit products, including a respective 16.6% and 12.5% performance lift* in bankcard and auto credit lines. The ability to assess default potential more concretely, while opening doors of opportunity to more consumers, is an economic win for all.

As the fourth-generation credit scoring model from VantageScore Solutions, VantageScore 4.0 offers innovation and benefits that serve 21st Century lender and consumer needs.  The model is another example of TransUnion’s investment in, and commitment to, the use of Information for GoodSM to help consumers gain more access to credit, control their financial lives and improve their standard of living.

* As compared with VantageScore 3.0

5 Questions with Andrew Davidson, president, Andrew Davidson & Co.

1. In Washington, there always seems to be a discussion about GSE reform, but the likelihood of it actually occurring is anyone’s“Feb2018” guess. How do modelers and MBS investors prepare for that type of uncertainty?

Over the past ten years, there have been innumerable proposals to change the functioning of the housing finance system and the mortgage secondary market. As modelers, we want to provide our clients with the tools to evaluate the impact of these proposals on their investments and their business. Our approach has been to keep our model focused on what we can see in the data and address potential changes through other mechanisms.

A good example is the many changes to the refinancing incentive through programs like HARP (Home Affordable Refinance Program). When various proposals were presented and implemented, we produced an analysis to show how various levels of take-up of the HARP program could affect prepayment rates. Once data became available we incorporated the change in behavior into our models.

We believe that this is the best approach for our clients: Make them aware of the potential impact of change, but don’t add speculative features to our models.

2. Over the past year, the issuance of non-agency MBS has begun to increase. Some observers speculate that FHFA Director Watt’s replacement could look to accelerate that trend administratively. With credit risk on the table, have PLS investors been quicker to adopt novel valuation and analytical tools? Do you expect that to change?

While we hope for continued increases in the issuance of PLS, that market is still limited. On the PLS side, the greatest growth in credit risk analysis has been in the evaluation of non-QM (Qualified Mortgages). Given the limited history of these mortgages as a separate asset class and the relatively benign housing market environment, it is difficult to assess how these loans will perform in a stressful environment.

On the other hand, there has been a great deal of activity in the (CRT) Credit Risk Transfer market with securities issued by Fannie Mae and Freddie Mac. The growth in these programs has produced a great deal of interest in relative value and other credit risk measures as traders and investors in the PLS market switched their focus to the CRT market. The recent developments in the CRT market are likely to spill back into the PLS market when issuance increases.

3. How will machine learning and AI transform the modeling in the secondary mortgage market? Are there areas where you’re seeing high-impact use cases today?

As modelers, we always welcome new data and new analytical techniques. For us, much of what is described as machine learning and AI are techniques that build upon approaches that we have used for years. As a result, we see the move toward AI and machine learning as evolutionary rather than revolutionary. We have also seen the limits of technology in the evaluation of mortgages and view solutions that seem “too good to be true” as probably “too good to be true.”

In the mortgage market, the greatest impacts so far have been in expanding the datasets used for credit modeling, including the work being done by VantageScore. We have also seen some inroads in using machine learning to identify potential variables that can be added to mortgage models. To date, the full AI models of mortgage performance have not been able to match the accuracy of human-guided models and fall far short in producing explainable and actionable results.

Our biggest concern is that market participants will place an excessive amount of confidence in results driven by new analytical techniques and not remember what we have learned from hard experience in the mortgage market: reliance on any analytical technique should be tempered by judgment.

4. With so few delinquencies in agency pools, some MBS investors have been hesitant to spend money on new data subscriptions or new tools. Do you think that will change? What sources of data or analytical methods hold the most promise?

We don’t wish for an economic downturn, but we do like to encourage our clients to be prepared prior to changes in economic conditions. Despite the relative calm in delinquencies and losses, we have seen firms expand their analytical capabilities as they enter new markets or prepare to meet changes in regulation (such as stress tests) or accounting (such as CECL). The use of these techniques has expanded the market’s use of probability-weighted scenarios, which provide insight not only into expected losses but also the risk of loss in stress scenarios.

One promising area for new data is the ability to combine performance data on mortgages with the credit bureau and other borrower data. For example, Fannie Mae and Equifax are able to link the CAS data and credit bureau data and can provide that to investors and others in a form that protects borrower confidential information. This represents a step forward from prior efforts to combine this data which relied on a statistical match.

5. How might impending demographic changes, inventory shortages and other headwinds impact the secondary market for mortgages?

Over the past few months, we have witnessed some slowing in home price appreciation. This trend may continue due to rising interest rates and reduced tax deductibility of mortgage interest expense. Home prices have also reached high levels due to the high cost of acquiring land and construction in the coastal markets. In addition, generally rising interest rates could lead to losses for mortgage investors and limit their appetite for additional assets.

Mortgage investments have several advantages relative to other asset classes. Improved underwriting since the financial crisis probably limits the credit risk of the mortgage market. In fact, we have seen much better performance of the enterprise-issued CRT bonds than similarly rated corporate bonds. Higher rates also reduce prepayment risk. It is possible that in the next few years, mortgages might provide a degree of stability for investors instead of being the source of market disruption.

 

Andrew Davidson is president of Andrew Davidson & Co., Inc., a New York firm specializing in the application of analytical tools to investment management, which he founded in 1992. He is a financial innovator and leader in the development of financial research and analytics. He has worked extensively on mortgage-backed securities product development, valuation and hedging.

Andrew Davidson & Co., Inc. turns mortgage data into investment insight. The firm created VECTORS® Analytics, a set of proprietary tools including the LoanDynamics Model for credit-sensitive mortgage securities, prepayment and option-adjusted spread (OAS) models for fixed-rate mortgages, adjustable-rate mortgages, collateralized mortgage obligations (CMOs), and asset-backed securities (ABS). Over 150 financial institutions depend on VECTORS® Analytics to help manage risk and value securities.

The company also provides consulting advice to financial institutions in the development and implementation of investment management and risk management strategies. They also work on a variety of fixed-income trading and valuation analyses. Customers of the firm include businesses of all sizes including many of the largest and most sophisticated financial institutions.

Andrew was instrumental in the creation of the Freddie Mac and Fannie Mae risk-sharing transactions: STACR and CAS. These transactions allow Freddie Mac and Fannie Mae to attract private capital to bear credit risk, even as they remain in government conservatorship. Andrew is also active in other dimensions of GSE reform and has testified before the Senate Banking Committee on multiple occasions. Andrew also helped establish the Structured Finance Industry Group and served on the Executive Committee at its inception.

For six years Andrew worked at Merrill Lynch, where he was a Managing Director in charge of a staff of 60 financial and system analysts. In this role, he produced research reports and sophisticated analytical tools including prepayment and option-adjusted spread models, portfolio analysis tools, and was also responsible for the development of trading and risk management systems for the mortgage desk covering ARMs, CMOs, pass-throughs, IOs/POs and OTC options.

Andrew was previously a financial analyst in Exxon’s Treasurer’s Department. He received an MBA in Finance at the University of Chicago and a BA in Mathematics and Physics at Harvard.

He is co-author of the books Mortgage Valuation Models: Embedded Options, Risk and UncertaintySecuritization: Structuring and Investment Analysis; and Mortgage-Backed Securities: Investment Analysis and Valuation Techniques. He has contributed to The Handbook of Mortgage-Backed Securities and other publications.

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