Trended credit data: a game-changer that will impact lenders and their decisioning.
Consider the way professional baseball players' performance was traditionally evaluated and how the Oakland A's front office significantly improved on that methodology by using more analytical gauges, as was detailed in the Michael Lewis' 2003 book, Moneyball: The Art of Winning an Unfair Came. In 2015, the book became a popular major motion picture that further showcased Oakland's unique and winning approach.
Essentially the book and movie demonstrated that by using a more rigorous statistical analysis, the A's determined that on-base percentage and slugging percentage were better indicators of offensive success than were the traditionally valued qualities of contact and speed. Furthermore, these qualities could be obtained for less money.
The statistics that had been used and relied upon for the last century, such as batting average, runs batted in and stolen bases, were outdated--even flawed. So, by using more analytical assessments of player performance, the Oakland A's were able to field a team that could more effectively compete against teams with much deeper pockets, such as the New York Yankees. Oakland essentially created a system that identified value in undervalued players. And it proved itself successful as the A's made it to the playoffs in both 2002 and 2003.
Fast-forward to 2016, and switch industries from Major League Baseball to credit. A very similar evolution is taking place in the evaluation methodology used to approve or deny individuals applying for mortgage loans. Trended credit data has entered the scene, and it promises to shake up the way lenders assess applicants, ultimately impacting their decisioning.
The new analytics
The traditional credit reports that have been used for decades captured just a snapshot of a person's accounts at a precise moment in time. However, this new trended data includes a full 24 months of balance, scheduled payment and actual payment information on all qualifying tradelines, providing lenders the ability to gain a deeper level of insight into a consumer's likelihood of default.
Spending patterns, past balances, payment history and credit utilization will all now influence lending decisions. Trended credit data is just that--data that can identify a trend: Is the applicant paying down debt or gathering it? The new trended credit data reports will be able to answer that question.
Trended data offers lenders greater insight, finer distinctions between applicants ...
The new data provides a higher level of granularity within the consumer's payment history, which gives lenders the opportunity to make much more informed lending decisions. Borrowers, on the other hand, will be evaluated not just on having good credit, but now the amount of their monthly payments, the length of time they carry balances and other information not typically included in underwriting standards will be factored into the equation.
According to Fannie Mae's Desktop Underwriter[R] (DU) Version 10.0 Frequently Asked Questions (https://www. fannie mae.com/content/faq/do-du-release06252016-faqs.pdf), consumers who did not exceed their credit limit on any of their existing credit cards in the last 12 months are 75 percent less likely to become delinquent on their mortgage loan. In addition, Fannie Mae found that consumers who pay off their total credit card balances each month are 60 percent less likely to become delinquent than those who only make the minimum payments.
Some key insights into borrowers' credit patterns that trended data offers lenders include, but are not limited to:
* Revolving utilization: Average of highest use--Having at least one revolving account consistently near the credit limit is associated with financial stress.
* Revolving credit capacity: Average available credit--Those who consistently use a lot of credit every month without paying it off are more likely to default; those with more capacity are less likely to default.
* Retail trades: Revolver behavior--Consumers who tend to revolve on more retail accounts are more likely to default.
* Bankcard payments: Slope of scheduled payments-- Increasing scheduled payments mean a consumer is more likely to default; decreasing scheduled payments mean a consumer is less likely to default.
By knowing how much and often borrowers spend money, whether their financial behavior is improving or declining over time, and how often they open and close accounts, lenders are better equipped to make more accurate assessments of their applicants--thereby reducing their risk. At the same time, it will probably become easier for some borrowers to get approved for a low-interest loan, and harder for others.
... and offers hope to certain segments of borrowers
Certainly, borrowers will be impacted by the advent of trended data as well. For prospective borrowers who exhibit transactor behavior (consumers who pay off their revolving debt monthly), trended credit data increases the likelihood of them being approved for a mortgage as they statistically pose a lower risk to lenders. Rewarding transactor behavior will help attract new and qualified entrants into the mortgage ecosystem.
Atlanta-based Equifax, one of two major credit bureaus that provide trended data, performed a study on a 10 percent random sample of all consumers with a credit file and an active credit card. Of those consumers studied, Equifax identified that of the individuals with a FICO[R] score between 561 and 640, 13 percent of them also exhibited transactor behavior. Additionally, Equifax found that 20 percent of consumers within the 641-680 FICO band also exhibited transactor behavior. Transactor behavior was also concentrated among consumers with at least five years of credit history on their credit file.
Fannie Mae has indicated that it doesn't anticipate the availability of trended credit data will affect the percentage of people who are approved or denied for loans. In other words, the number of people who will get approved who would otherwise be denied will be offset by the number now being denied who might otherwise have been approved.
However, it is becoming clear that there is a potential pool of borrowers who exhibit transactor behavior in the marketplace and may now have a higher recommended approval rate than what has historically been the case. Yet, the net new number of applicants that exhibit transactor behavior is difficult to quantify, as many of these prospective borrowers may have never applied for a mortgage loan.
For example, if an applicant has shown considerable improvement in his or her total debt utilization by making payments for the past 24 months two to three times the minimum required amount, yet the applicant's credit score is still a few points short of being approved and he or she has never owned a home before, that applicant's consistently lower credit utilization may now override his or her credit score for an approval. Hopefully, with some thoughtful guidance from Fannie Mae, lenders will now be able to reward those who deserve it with the credit they have earned.
It's a whole new ball game
Just as more detailed analytical assessments helped the Oakland A's identify value in undervalued players, trended credit data can help lenders identify previously overlooked transactors who may have little credit but are good risks, and welcome them into their pool of approved applicants. Indeed, it's a game-changer for lenders who, up to this point, were operating with limited--even flawed--information, given the lack of credit history.
Trended credit data is for lenders what rigorous statistical analysis was to the A's front office. Running the numbers more ways and different ways--be they baseball stats or credit data--can yield different, more accurate evaluations that can produce winning results.
Greg Holmes is national director of sales and marketing at Credit Plus Inc., Salisbury, Maryland, a third-party verifications company serving the mortgage industry. He can be reached at firstname.lastname@example.org.
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|Comment:||Trended credit data: a game-changer that will impact lenders and their decisioning.(SERVICING)|
|Date:||Oct 1, 2016|
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