Student loan debt in the United States has reached an all-time high. Student loan debt can also be a part of a consumer’s financial journey as they look at buying a car, a house and even into retirement. With April being Financial Literacy month, FICO is exploring the credit behavior of young people with a student loan actively in repayment to determine the behaviors that are driving FICO® Score increases and decreases. How many are exhibiting positive behaviors and driving score increases? How many are exhibiting less favorable behaviors, leading to score decreases? Using a nationally representative sample, we identified 10 million scorable consumers age 18 to 30 who had a student loan actively in repayment as of October 2016. Among those within this profile, we found 22% had a significant increase in their FICO® Score 9 between October 2016 and October 2017. We defined a “significant increase” as those with... [Read More]
In a previous post, we noted that the majority of consumers who had a 7-year-old delinquency purged from their credit file saw improvements in their FICO® Scores. Now let’s look at whether these consumers’ credit-seeking behavior changed after the delinquency was purged and their score recovered. Were they more likely to apply for credit? Get approved and open new accounts? To assess this, we looked at the proportion of the “delinquency purge” population (those that had a delinquency removed from their credit report between May 2016 and July 2016) that had a new inquiry or opened a new account in the three months following the purge window (August through October 2016). In Figure 1, we compared those values to the same period a year earlier, to avoid capturing seasonal changes in credit habits. The data showed that there was a minor increase in the percentage of consumers that had a... [Read More]
FICO regularly tracks the national FICO Score distribution as an important gauge of US consumer credit behavior. When I last blogged about this topic based on data from April 2016, the key takeaway was “the beat goes on.” US consumers continued to show improvement in managing their debts, which began shortly after the bottoming out of the economy in 2009-2010. We have pulled the latest FICO Score distribution information based on a snapshot of millions of US consumers’ credit data as of April 2017, and can report that consumer credit health and responsibility continue to be strong! For the first time since we’ve been tracking these stats, the average national FICO Score reached the 700 threshold — some 10 points above what it was just prior to the recession in October 2006. Let’s dig a little deeper by examining the score distribution over the past 10+ years. The movement in... [Read More]
In the depths of the Great Recession, tens of millions of consumers had lapses in meeting their credit obligations. Some seven years down the line, those missed payments are being purged from credit reports in accordance with the Fair Credit Reporting Act, and these consumers may now be looking at a clean (or at least cleaner) slate. To find out how the FICO® Scores of these consumers might be impacted by this negative information being purged, FICO conducted research on a random representative sample of the 28 million US consumers who had a serious delinquency (defined as 90 or more days past due) between 2009 and 2010. This sample was divided into two groups: Those who had a delinquency removed from their credit report between May 2016 and July 2016. We’ll refer to this group, which numbers about 6 million nationally, as the “delinquency purge” population. Those who did not... [Read More]
The student loan crisis in the US is getting much worse — student loan debt is over $1.3 trillion and is increasing by more than $2,700 per second. Lenders cannot ignore the impact of that debt on individual borrowing. Our latest research shows that: The number of US consumers aged 25-34 with student loan debt of at least $50,000 doubled from 2005 to 2015. During that same time, the average student loan debt across all age 25-34 consumers also doubled — by comparison, average credit card debt and mortgage debt for this population actually fell. While the number of consumers age 25-34 with student loans grew from 2005 to 2015 (from 27% of this population to 40%), there are fewer 25-34 year olds with mortgages or credit cards than 10 years ago. In fact, our data shows that people with active student loans are far less likely to have... [Read More]
When I last blogged about the national distribution of FICO® Scores a year ago, US consumer credit quality was continuing to climb upwards, though with some potential indications of change ahead. At the end of that post, I asked: has the 7+ year upward trend started to level off? 12 months later, the answer is a resounding “no”! The number of consumers scoring in the super-prime range of 800 or above has continued to grow. In October 2015, this figure surpassed 20% of the national population for the first time since we’ve been tracking this metric, dating back to pre-recessionary 2005. In the subsequent six months, the figure continued to climb, reaching 20.4% as of April 2016. Inversely, we see fewer consumers scoring in the lowest score ranges. As of April 2016, just 20.7% of the population score 600 or below. This figure peaked shortly after the Great Recession, at... [Read More]
Many vendors are promising to score significantly more US consumers than ever before. Before you believe the hype, be sure to ask these three vital questions: Are the credit scores predictive? Do a significant number of consumers score high enough to quality for credit? Will these consumers gain an onramp into the credit mainstream? The goal should not be to just generate more credit scores—but to generate scores that enable lenders to safely and responsibly extend credit to more people. I’ve been blogging about how this can only be done by analyzing alternative data along with credit bureau data. This approach goes beyond making more consumers scorable. It generates meaningful, predictive scores that reveal many creditworthy individuals who would otherwise still be stuck in a credit catch-22. It also is the only means to assess nearly half of the credit invisibles, the more than 25 million consumers that have no... [Read More]
What’s the secret to scoring more of the consumers who don’t currently have FICO® Scores? Supplementing credit bureau data with alternative data, which—as I disclosed in my last post—enabled us to accurately score more than 50% of previously unscorable credit applicants. A key reason there’s an opportunity to score more consumers today is the growing number of alternative data providers that have entered the market in recent years. But not all provide equal value for credit scoring. Consider telecommunications payment data. It has many similar qualities as data reported in traditional credit files. In fact, telecom companies occasionally report customer account status to credit bureaus. Yet this information is present in less than 10% of bureau files—and when it is present, it often reflects negative payment history. More complete telecom data is available from alternative sources—and it includes positive as well as negative information. That’s important for expanding credit access... [Read More]
In my last blog post, I showed why using credit bureau data alone is insufficient to safely measure the creditworthiness of the millions of US consumers who don’t currently have FICO® Scores. To reliably assess risk for these “unscorable” consumers, we must fill in the partial or missing picture of current financial behavior available from credit bureau files. Can alternative data fill in these gaps? We recently completed research to find out. It showed that with the right alternative data, we can accurately score more than 50% of previously unscorable credit applicants. The scores were significantly more predictive than using bureau data alone and designed to demonstrate repayment odds consistent with traditional FICO® Scores. As part of our study, we built a research model and scored New to Credit consumers using bureau data only (which generally consisted of only one or more credit inquiries). We then compared the model’s performance... [Read More]
