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auto loan delinquency statistics

The headline of the Federal Reserve Bank of Kansas City’s Macro Bulletin summarizes the Q2 2018 auto loan delinquency statistics: Auto Loan Delinquency Rates Are Rising, but Mostly among Subprime Borrowers. The graph below visualizes the trend. Auto loan delinquency rates for prime borrowers remained relatively stable since March 2011. However, delinquency rates for subprime declined then grew from 12 percent in 2014 to 16.3 percent by Q2 2018.

Auto Delinquency Rates (90+ Days Delinquent)

auto loan delinquency statistics

Sources: Federal Reserve Bank of New York Consumer Credit Panel/Equifax and NBER
Note: Gray bars indicate recessions (as indicated by NBER).

Should you be concerned? That depends upon the makeup of your current loan portfolio. According to the Experian State of the Automotive Finance Market Q2 2018 report, fincos seem to have the greatest exposure to delinquencies, though there has been a slight decline year over year. Banks, captives, and credit unions clearly take a more conservative approach to auto lending.

Fintech Drives Down Auto Loan Delinquency Statistics

The Federal Reserve Data is a report of average point-in-time delinquencies and likely do not reflect delinquency rates of the your specific market segments. Regardless of the averages shown in auto loan delinquency statistics, anything you can do to reduce delinquencies helps your bottom line.

Recent advances in fintech let lenders do more to reduce the risk of delinquencies. New sources of consumer data provide a more detailed financial profile of applicants. Decision rules and workflows replace manual decisioning that is inherently inconsistent in applying credit policies. Analytics can reveal attributes and loan structures that correlate with delinquencies. Machine learning can identify fraudulent applications. Together, these fintech capabilities help prevent lenders from having a negative impact on auto loan delinquency statistics.

Better Data Enables Better Lending Decisions

Lenders who focus on the subprime market can reduce the risk of delinquency by using additional data for a more accurate assessment of applicant creditworthiness than bureau scores alone. Alternative credit data services use utility, cell phone, and rental payment records,  and real estate and banking information to track an applicant’s recent financial trends (whether they’re improving or declining). Using alternative credit data enables lenders to make better-informed loan decisions, offering deals tailored to the borrower’s financial profile and reducing the chance of delinquency.    

Decision Rules and Workflow Bring Consistency to Lending Decisions  

Manual tasks are the bane of the underwriting process; they’re inefficient and subject to the variations associated with underwriters’ skill and experience. Underwriting workflows ensure that process steps are automated and consistently executed. Decision rules translate credit policies into actions. Together they can eliminate variations in lending decisions.

Overrides are a potential source of eventual delinquencies. Decision rules can control the criteria by which overrides are permitted. Decision rules can also ensure that overrides are automatically reviewed and approved, or sent back to the underwriter. Workflow steps and the results of decision rules are automatically tracked and recorded. This allows lenders to analyze overrides and determine if any policy modifications should be made to tighten credit policies to avoid potential delinquencies.

Analytics Identifies the Underlying Causes of Delinquencies

Modern lending software acquires and creates a wealth of data regarding processes and portfolio. Analytics, one of the most powerful capabilities of fintech, give lenders the ability to identify underlying causes of delinquency. Analysis of delinquencies in your portfolio can reveal borrower attributes that are strong predictors of 90 days past due. This insight can then be used to review and revise credit policies. Predictive analysis can also let you identify potential delinquencies (ideally, at 30 days past due), proactively reach out, and offer options to keep the car on the road and the payments on schedule.

Analytics can also reveal where decisions are being made in your underwriting process that could result in delinquencies. Analytics can monitor and report overrides per underwriter, determine when stipulations have been bypassed, or identify “outliers” or discrepancies in deal structures.

Don’t Let Fraud Contribute to Auto Loan Delinquency Statistics

Auto loan fraud should be a concern for every lender. Fraud occurs at every credit tier and is particularly damaging when an expensive new automobile is driven off the lot and payments become delinquent shortly thereafter. By applying machine learning to analyze millions of loan applications, it’s now possible to identify fraudulent applications with a high degree of confidence. Anti-fraud analytics are easily integrated with a cloud-based loan origination solution. Lenders should include fraud detection as a key weapon against delinquencies and defaults.

Lenders Have Powerful Tools to Defend Against Delinquencies

The technology available today gives lenders the upper hand in managing lending processes and policies to minimize auto loan delinquencies. To take advantage of these capabilities, you’ll need a modern, configurable, cloud-based loan origination solution with integrated analytics.


Getting Started

defi SOLUTIONS is a provider of fintech for the auto lending industry. Our loan origination and analytics experts welcome the opportunity to discuss how we can help you minimize delinquencies. Get a first-hand experience of the power of fintech by contacting our team today or registering for a demo of defi LOS.


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