The pandemic of 2020-21 created a perfect storm for fraud to flourish. Heightened fear, anxiety and economic hardship resulting from the pandemic led some consumers to perpetrate fraud in varying degrees in pursuit of financing, and in turn, to place greater risk for losses on credit unions among other lending organizations. The ensuing economic turmoil caused an immediate and dramatic rise in unemployment, increasing some people’s willingness to misrepresent their current incomes – even with the best intention of paying back the loan once approved.
2020 set a new record for fraud losses — $7.3 billion in exposure for the U.S. auto lenders – yet income misrepresentation was just one of many forms of fraud to affect auto lenders, with other forms representing an increasingly complex range of sophistication. Fraudsters are experts at finding vulnerabilities in processes and exploiting them for gain. They take advantage of technology and adopt strategies that go far beyond employment and income misrepresentation.
Historically speaking, credit unions could afford to concentrate fraud management efforts away from identity-related fraud, such as identity theft and synthetic identities. This advantage stemmed from a “membership shield” that insulated credit unions from fraud perpetrated against the general public. In 2020, identity-related fraud represented only 20 percent of US fraud exposure – with the balance attributed to first-party fraud schemes. So in reality, many credit unions, especially those growing aggressively and with looser membership criteria, are likely facing a very similar range of first party fraud, just like smaller banks and monoline automotive finance organizations. Only the most selective credit unions are immune to income fraud, employment fraud, straw borrower fraud, and collateral fraud.
Image 1: 2020 U.S. Auto Finance Fraud Incidents – Income & Employment Misrepresentation
Source: 2020 Annual Auto Loan Fraud Report (Point Predictive, 2021).
Last year, there was marked uptick in income and employment misrepresentation (see Image 1). As the lockdowns began, lenders were suddenly slammed by a 100-percent year-over-year increase of falsified income and employment claims on auto loan applications, a level of risk which remains elevated to this day. Data science teams have detected thousands of bogus employers which continue to be used to falsify employment on auto loan applications each month. Far too many of these loans are funded nonetheless perhaps because of a lack of detection.
Trends That are Driving Loan Fraud Exposure Higher
Online commerce presents risk to lenders. Online auto lending channels generate higher rates of synthetic identity, identity theft, and straw borrower attempts since the lack of face-to-face interaction with the lender and dealer directly increases risk. It is reasonable to assume that the rate of fraudulent online loan applications is several hundreds of basis points higher than the fraud rate over the dealer channel since fraudsters can submit many applications with different information in rapid succession. Lenders should be on the alert for higher fraud rates on all their digital and online channels.
Credit washing is the systematic dispute of anything derogatory on the credit report regardless of the accuracy of the record, and there is a thriving industry for these services. In 2020, such credit washing incidents spiked by 12 percent. The line between legality (credit repair and resolving identity theft) and fraud (credit washing) is not always clear to a lender and essentially boils down to ascertaining borrower intentions. Is credit repair part of an effort to turn over a new creditworthiness stone? Or is credit repair merely to engage in more deception? The truth is very difficult to determine. Advanced modeling technology, however, can now detect this fraud risk by identifying borrowers whose credit scores have rapidly increased, while simultaneously identifying a rapid reduction in the number of tradelines appearing on subsequent credit bureau inquiries.
Verifying employment in a “gig” economy, is a challenge for lenders. It is challenging enough to verify employment of a borrower who works in Corporate America. Some of these corporate employers report employee information to screening services. But since roughly only 30% of employment is reported, this traditional approach simply doesn’t cover the majority of workers. A self-employed applicant is even more difficult to verify than a wage-earning applicant. Furthermore, their reported incomes may widely and legitimately vary over time, whether they are small business owners or rideshare drivers or freelance podcast producers, or part of the jobsite construction crew. It only takes a little misrepresentation to be consider a lot of fraud when car loans are at stake.
Synthetic Identity Fraud occurs when real and fake information are combined to create a new identity used to obtain fraudulent loans and credit. The use of some real data makes it particularly challenging to detect. Synthetic Fraud causes more than $1 billion in losses each year for U.S. auto lenders. At some lenders, such fraud makes up as much as 20% of total fraud losses. With technology analyses, however, applications can be scored for high risk of synthetic identity and flagged for further action.
Fraud and the Coasts | Fraud and the Ghosts
Lenders in coastal states as well as major metropolitan areas are more likely to experience auto loan fraud. Florida tops the nation in suspect applications, with 2.86 percent of all loan applications there tainted by fraud. Massachusetts ranks next, followed by Illinois and then Hawaii using the same measure. Among cities, Miami, Chicago, Atlanta, Orlando are all among the top 10 U.S. cities with auto finance fraud – with Richmond, CA, topping the list.
Dealer fraud is also in the growing fraud mix. Price inflation results when the dealer presents a significantly higher sales price to the lender on a vehicle than the borrower is actually financing — and the actual sale price of the vehicle is much lower. Such occurrences are a red flag for potential powerbooking or for ghost deposits, which are paperwork gimmicks that inflate the value of the collateral to appear like either more car is being sold or a larger down payment was made.
Credit unions are more likely to experience fraud on affordable and practical vehicles since professional fraudsters know that they are not likely to convince a credit union to lend to a stranger for a high-end sportscar purchase. The Ford F-250, the Nissan Titan, two GMC sport utility vehicles, and the modest Infinity G34 are examples of frequently-seen collateral for assumed fraud (early payment default).
Credit Unions Can Do More to Prevent Fraud
To avoid exposure to the nationwide increase in loan fraud attempts, credit unions should look carefully at both borrowers and co-signers. For instance, when co-borrowers are removed from a second loan application, it suggests an attempt to avoid disclosing the true owner/driver of the vehicle. This major red flag is nearly impossible when these two applications are submitted to two different financial institutions, although many lenders are joining an anti-fraud data consortium. Lenders that collaborate to alert each other about fraud can detect and squash risky applications without allowing those lenders to “see” any application information other than their own. Credit unions receive warm welcomes to anti-fraud communities like this.
Credit unions are adept at anticipating economic trends, so it is time to prepare. Fast money leads to fast fraud and there is still a lot of fast money in the system. The industry still has yet to discover how much fraud is hidden in pandemic-related forbearance programs and deferred payment agreements.
It is worth reviewing the performance of new members, your dealer relationships, and early payment defaults to understand the risks to your portfolio and the new fraud prevention measures that are necessary to continue to grow membership, better serve existing members, and keep innocent-looking fraud at bay. Cross-industry visibility and predictive data science can be leveraged for competitive advantage against the big banks, captives, and independent auto finance companies.
(This article is based on the 2020 Annual Auto Loan Fraud Report: An Analysis of Key Trends, Insights & Predictions for Lenders authored by Point Predictive, Inc. The report encompasses research on more than 100 million applications sourced through a secure and private data science collaboration among 50 of the largest US auto and mortgage lenders. www.pointpredictive.com.)
Frank McKenna is the Co-Founder and Chief Fraud Strategist at Point Predictive. He has worked with more than 100 banks, lenders and companies throughout the world, designing strategies, solutions and operational practices that helped them to reduce costs. Feel free to email Frank directly at firstname.lastname@example.org.