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auto loan delinquency rates

Auto loan delinquency rates will always be a challenge for lenders, who must balance acceptable risk with profitability. As an indicator of ongoing appetite for both, interest rates have increased over the past few months. According to Federal Reserve data, the average new car rate for a 48-month loan is above 5 percent.

Recent economic uplift has consumers feeling more optimistic and more willing to take on greater debt, despite existing student and mortgage debt. This is reflected in buyers’ preferences for light trucks and SUVs making up approximately two-thirds of vehicle purchases. Total loans outstanding are approximately $1,125 billion, according to the Federal Reserve Bank of St. Louis. With loan terms often extended to as many as 84 months, lenders have tried to make monthly payments more bearable. Yet, a TransUnion study finds a correlation between longer-term loans and delinquencies. Some argue that lenders have just lowered the standards to gain market share.  

With car sales seeming to have leveled off, after pulling out of the great recession, how do lenders maintain profitability and minimize delinquencies and their portfolio impact? Since loan application volumes, underwriter experience, and higher application fraud rates are making it harder than ever to identify and eliminate risk, how can lenders do so? The answer is to make better decisions about loan applications. Analytics, alternative credit data, and decision rules help you make those better decisions.

Fintech Functionality Auto Loan Delinquency Rates

Recent advances in fintech make it possible for lenders to reduce the impact of delinquency, if not prevent it at the outset. Powerful analytics, new sources of detailed consumer data, and decision rules let lenders:

  • Identify applications with a high probability of delinquency or outright default;
  • Have greater confidence in making sound lending decisions by obtaining a more accurate picture of applicant financial strength;
  • Reduce (if not eliminate) variability in lending decisions; and
  • Proactively identify loans likely to result in delinquency.

Fraud Recognition Doesn’t Let the Car Leave the Lot

Auto loan fraud is a growing threat to the lending industry. Fraud puts the keys in the hands of the thief. When the first or early payments go delinquent, there’s a high probability you’ll never repossess that car.  

Machine learning has been used to analyze millions of fraudulent auto loan applications to detect subtle and overt indications of fraud—fake pay stubs, false identity and employment, and straw borrower, as well as identify high-risk dealers. Analysis of an application returns a risk score and a reason code that lenders can then use as a threshold to determine applicant credibility. Auto fraud analytics is the immediate way to reduce the negative effects of delinquency on your portfolio.

Better Quality, Well-Informed Lending Decisions

You’ve filtered out fraudulent applications, now how do you make the best possible lending decisions for those remaining? New data sources enable better-informed evaluation of creditworthiness, while decision rules ensure credit policies are applied consistently.

The internet, e-commerce, social media, and mobile technologies generate volumes of consumer data (utility, cell phone, and rental payments, real estate investments, bankruptcies) that are captured, aggregated, and provided as a service for lenders to augment traditional bureau data. Using a combination of these “alternative credit data” and bureau data lenders gain a more detailed and accurate view of an applicant’s creditworthiness. Bureau attributes combined with alternative credit data can enable more confident decisions at every credit score range.  

Decision Rules Consistently Apply Credit Policies

Credit policies can be transformed into decision rules that are consistently applied to loan applications as part of the loan origination process. The table below provides just a few examples of how decision rules can be employed for efficiency and consistency:

Decision Rule Example Explanation
If FICO score < 580, then auto-decline application and initiate adverse action notification workflow For credit policies that avoid subprime loans, decision rules automatically drive adverse action process to eliminate manual processing.
If FICO score >= 580 and < 650, then pull alternative credit data to evaluate risk. For credit policies that consider applicants with fair credit scores and use alternative credit data for final determination of creditworthiness.
If bureau data = no-hit, then pull alternative credit data to assess creditworthiness. Applications with thin or no credit file (one of the millions of “unbanked” or “underbanked” households) may nonetheless be creditworthy and a profitable opportunity.
If FICO score < = 580 and override = true, then send the application to the subprime review queue. Decision rules ensure that all overrides are tracked and reviewed to minimize risk.

Decision rules remove the inherent variability of “manual” underwriting decisions that may introduce risk. They can also ensure that even complex lending decisions such as auto-structuring can be executed accurately and consistently.

Analytics Proactively Identify Potential Delinquency

With fraud analytics, consumer data, and rules to support well-informed lending decisions, you’ll significantly reduce delinquency rates and their impact on your lending portfolio. Invariably, however, a subset of borrowers across all ranges of credit scores will be subject to financial headwinds that affect ability to pay. A change of address, change or loss of employment, or even a change of phone number can be an early indicator of a reversal in financial strength, potentially leading to delinquency. Analytics can help mitigate potential losses in three ways:

  • Recognize changes in borrower attributes or  profiles that highly correlate with delinquency;
  • Evaluate alternative data sources to uncover details that provide insight regarding changes in financial position; and
  • Conduct a regular portfolio analysis of delinquencies and defaults to identify attributes and credit policies that contribute to delinquencies. Use this information to tighten credit policies and procedures to further reduce delinquencies.

Early identification of potential delinquency lets lenders proactively contact the borrower (in compliance with local collection regulations) and offer options to help keep the borrower in their vehicle.

Use Fintech to Minimize Auto Delinquency Impact

Today, there’s no reason to accept auto loan delinquency rates as a given. Fintech capabilities of fraud analysis, alternative credit data, decision rules, and analytics give lenders the power to eliminate potential delinquency at the front door, make well-informed lending decisions, and head off problems before they start.

Getting Started

defi SOLUTIONS lending software experts welcome the opportunity to discuss how our solutions can help reduce your auto loan delinquency rates. We’re a leading fintech provider exclusively focused on auto lending. Contact our team today or register for a demo of defi LOS and defi Analytics.


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