After a 2018 that had its highs and lows, what might 2019 have in store from a credit risk management standpoint? Here are four key developments in credit scoring that we will be keeping an eye on in the new year: Consumer-Contributed Data Takes Center Stage Momentum is high in the consumer-contributed data space: consumers are getting more comfortable with sharing their data, provided they are presented with clear benefits for doing so. Mandates, such as the Revised Payment Services Directive (PSD2), are ushering in the era of Open Banking around the globe. Additionally, developments, such as the recent launch of the Financial Data Exchange (FDX), point to increasing collaboration between financial institutions and data aggregation vendors (such as Finicity, Plaid, Quovo, and Envestnet | Yodlee) to facilitate secure and efficient transfer of consumer-permissioned financial data. In 2019, enhanced credit underwriting via digitally contributed-consumer data will hit the mainstream. With... [Read More]
“There are three kinds of lies: lies, damned lies, and statistics” – Attributed to Benjamin Disraeli, British PM (1874-1880) In assessing the predictive value of new scores or new data, significant weight is given to model performance measures. Examples of these measures — which quantify the effectiveness of a predictive model in distinguishing between the outcomes being predicted — include the Gini coefficient, KS (Kolmogorov-Smirnoff statistic), and ROC Area. These measures are appealing because they allow a straightforward means of comparing the relative effectiveness of two or more models. Think of them as scoring the scores — the score or data with the highest “score” looks like the best one to buy or use. Score and data vendors often structure their validations in a way that produces a more compelling case for their product. And sometimes their efforts fall into the grey area of gaming — producing misleading results by tweaking... [Read More]
Greetings from sunny (and humid) Miami! I had the pleasure of speaking on a panel at ABS East yesterday, entitled “Traditional vs Non-Traditional Underwriting, Does Machine Learning Teach Us Anything New?” The panel primarily focused on the opportunities and challenges associated with the use of Machine Learning (ML) in credit underwriting. I called out some highlights from FICO’s recent white paper on this subject. (We will dive into this further in future blogs so keep an eye out.) On the ‘opportunity’ side, I cited: The speed to powerful insights that ML offers, making it ideal for R&D efforts aimed at assessing new analytic challenges, and/or the potential of new data sources to add incremental lift. The great strides we’ve made at FICO as far as developing explainable artificial intelligence (AI)/ML and how that enables us to understand better than ever before what the key variables and risk patterns are that... [Read More]
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]
UPDATED on September 24, 2018: The average U.S. FICO Score has now hit 704. To read more about it please go here. 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... [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]
