In our first article in this series, we discussed the magnitude of the student loan debt. Over 43 million student loan borrowers. Over $1.5 trillion in federal student loan debt. (1) A 17% jump in monthly debt commitments that could translate into an extra $244 that borrowers owe each month.(2)
Student loans will present a significant economic hardship for many borrowers.
For lenders, as well as telco, insurance, and energy firms, it is time to take a good hard look at their student loan analytics. They will need to evaluate how well prepared they are to understand the potential impact on their portfolios.
Questions to consider:
Do you know which of your customers have student loans?
Do you know how much student debt these customers owe?
Can you calculate the percent of student loan payments to income?
Can you assess how the resumption of student loan debt will impact customers’ overall debt to income?
What practices do you have in place to assess risk from student loan debt today? Will they still be sufficient a year from now?
How quickly can you respond to changes in your customers’ financial situation once student loans are added to the picture?
Do you monitor changes in your customers’ credit behaviors outside your own firm? Would you know about a delinquency for a loan held by another lender?
Looking back, if you knew the magnitude of a customer’s student loan debt when you were underwriting a new loan for that customer in the last few years, would you have still approved that loan?
Risk from student loans will escalate. Prepare now.
Let’s explore some ways that lenders and other companies can boost their risk analytics and account management practices. That way they can better address the student loan challenge.
Start with the most relevant credit insights.
Lenders need a focused view of the impact student loan debt will have on their customers. A good place to get started is with the Student Loan Attributes Package. This is a collection of consumer credit attributes designed to shed light on the impact of student loan payments on your customers. With this, lenders can discover which of their customers have student loans and what are their balances and payment history.
Student loan credit attributes can help analysts form the basis of their assessment of which current customers might be most impacted by student loan commitments. We worked with one large lender to discover that millions of their customers hold student loan debt creating significant risk exposure for the firm.(3)
To enhance risk assessment, lenders can supplement student loan attributes with income and total debt data. Then, they can gain updated calculations for important metrics, including:
student loan payment to income (PTI)
and consumer debt to income (DTI).
Gain a broader view of consumer finances.
Lenders that rely on credit metrics alone should take their analytics even further. Why? Because typical credit and income measures may not reveal which customers that have student loans are most likely to be able to pay all their debt commitments.
To answer that, more insight is needed – like estimated affluence, non-wage income, discretionary spending, and other components of the consumer wallet. Together, these financial characteristics provide an indicator of financial durability. Namely, how well can you estimate whether consumers can meet their financial obligations and keep spending, even when they are under financial stress (such as stress from a new monthly student loan bill). Financial durability (a non-FCRA measure) can help lenders get guidance on how to answer the question: Do my customers likely have the financial resources to pay all of their loans?
Financial durability is important to assess, even for your historically best customers: Prime consumers with the lowest financial durability have up to 3x the bad rate of those with the highest durability.(4) That means that there is a segment of your strongest performing customers that both hold student loans and are much more likely to become delinquent. Use financial durability to help identify them before delinquency strikes.
In an analysis conducted for large lenders, we looked at which of their customers had non-deferred student loans in accommodation off them. We found that 17-25% of those loan holders in prime/super prime score bands actually have low or very low estimated durability, indicating that despite their credit scores, they may not have the financial resilience to meet financial obligations during unstable times.(5) Some of these customers may have strong credit scores due to no student loan delinquency reporting or other forbearances since Q1 of 2020. Either way, that’s quite a lot of “good” customers that lenders should be concerned about.
Lenders can also consider exploring consumer-permissioned bank transaction data to gain a better view of how customers manage their money. They can integrate specialty finance data into their risk assessments to better understand which borrowers use these services and if they reliably meet those commitments. In addition, it might be a good time to re-verify income and employment data for customers that you identify as holding student loans.
Review your customer accounts frequently.
With the coming onslaught of student loan bills, annual customer account reviews may not be enough to catch significant changes in your customers’ credit situation. Instead, shift to quarterly or monthly reviews so you can become aware of changes faster.
Here are some of the data points you would want to assess on a regular basis for student loan borrowers (and all borrowers) in your portfolio: Have your customers’ loan balances changed? Has their utilization increased? Do your customers have new delinquencies? Student loans are going to impact many of your customers’ loan behaviors – better to know sooner than later which of your customers present increased risk. And don’t wait – for every month that a lender skips a review, loss mitigation efficacy degrades by 50%.(6)
Track changes away from your firm with up to daily alerts.
If one of your customers that you have identified as having a student loan becomes delinquent on a credit card account or auto loan that they hold at another lender, how quickly would you know? How about if they opened a new loan account or significantly increased their utilization? Lenders can reduce these types of blind spots by receiving alerts of changes in consumers’ credit behaviors even at other firms via daily, weekly, or monthly notifications. Then, you can respond fast to customers that may be struggling to meet not just their student loans, but all of their loans.
Stay on top of student loan analytics and account management to protect your business from coming changes.
Not all of your customers with student loans will present increased risk. But many will.
With increased insight into your customers’ finances, coupled with regular account reviews and alerts to changes, lenders can better segment customers to identify those with student loans that may present increased risk. Then, you can take action to help those customers’ manage their debts, plus adjust models, refine credit lines, address potential delinquencies, and better prioritize collections efforts.
For assistance with how student loan debt will impact your portfolio, ask for a free consultation with our risk advisor experts by emailing riskadvisors@equifax.com. Listen to our podcast to learn more insights from our experts about student loan related risk. We can help you answer questions such as:
Which of your customers will be impacted by student loan debts?
What is the balance of these loans?
How much risk do they present to your firm?
Learn more about our consumer attributes for student loan related risk. Plus, explore how financial durability measures can help you better segment your customers. Discover more tips to enhance your account management in our eBook. Feel free to contact us for more information.
1 Equifax Credit Trends, July 26, 2023
2 The student loan crisis white paper, Shur℠, Equifax, and VantageScore®, July 2022.
3 Equifax analysis.
4 Equifax analysis.
5 Equifax analysis.
6 Equifax analysis.
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