Demystify Fraud

Demystify Fraud Informed

The definition of fraud in consumer lending is contentious because lenders define it slightly differently. Here are a few definitions I have heard:

  • Straight through charge off with no payment ever made
  • Straight through charge off and no contact with the customer (changed phone number or not answering calls etc.)
  • Charged off in the first few months and no contact with the customer (changed phone number or not answering calls etc.) In the case of auto lending, the vehicle cannot be located. 
  • Charged off in the first few months and proof of some misrepresentation. In auto lending, the vehicle could have been power booked

However, all of these are lagging indicators of fraud rather than predictions, since the events have already happened. In these cases, the borrower never intended to pay back the loan and likely intentionally misrepresented some information. The natural next step is to figure out what information they misrepresented and how they were successful. This helps in understanding loopholes in the process and fortifying them so that a similar occurrences do not reoccur. 

For the purpose of this article, we’ll define ‘fraud’ as intentional misrepresentation of information. We’ll dive into the ways information is misrepresented during the origination process. The main focus of this post is automotive lending but there is overlap between automotive lending and any other consumer lending. 

Fraud or intentional misrepresentation of information can be categorized on two axis: the type of information misrepresented and the person misrepresenting the information. First we’ll look at the type of information that gets misrepresented most often and why.

  • Income/Employment information – This information is the most misrepresented since one of the key drivers for offering credit is the borrower’s ability to repay the loan. Payment to Income Ratio (PTI) is an indicator that holds significant value in loan underwriting and is calculated as monthly loan payment divided by total monthly income. By misrepresenting income/employment information, one can artificially reduce the PTI and thereby be approved for a loan structure they wouldn’t otherwise qualify for.
  • Other consumer information – Includes the borrower’s identity, residence address, number of years at that address, phone number or insurance information. The identity of the borrower is misrepresented if they’re using a stolen or synthetic identity (we will delve into that later). Residence and contact information can indicate the borrower’s stability and by misrepresenting that information, the lender’s ability to collect (or repossess) from the borrower reduces significantly. 
  • Vehicle misrepresentation – The most prominent use case here is powerbooking – artificially inflating the vehicle’s price by including options not actually on the vehicle, during the vehicle financing process. The other use case is misrepresenting the condition of the vehicle as clean when it has known mechanical or other issues. In both cases, the price of the vehicle is inflated, reducing the LTV (loan to value) and results in a lower APR, not accurately covering the risk associated with the deal. 
  • Deal structure misrepresentation – these are less common but happen in indirect auto lending when the dealer misrepresents the cash down payment amount by artificially inflating it and increasing the sales price of the vehicle. This results in perceived higher equity in the vehicle and lower APR, or even approving a deal that would not otherwise be approved by the lender. The other edge case is when the dealer packs VPPs (voluntary protection products) into the sales price of the vehicle rather than including them in the back end of the deal structure.

Now onto the categories of bad actors who misrepresent the information.

  • First party – When the borrower intentionally misrepresents information in the credit application or during the origination process. The borrower may misrepresent income/employment information or residence/contact information. This happens mainly in lower credit spectrums (subprime to deep subprime). 
  • Third party – When a third party represents themselves as the borrower, often using stolen identities. The most lethal are fraud rings when multiple stolen identities are used to originate loans, leading to massive spikes in fraud losses. This happens across the spectrum but predominantly in the higher credit spectrums (super prime to near prime) due to easier access to credit.
  • Synthetic Identity – The fraudster synthetically creates a new identity, often by combining fake PII data with real SSN. These synthetic identities won’t have a perfect match on the credit bureaus or other data providers but could potentially have partial matches. 
  • Second party/Dealer – When a party other than the borrower intentionally misrepresents information (outside of stolen or synthetic identity). In indirect auto lending it is the dealer and in personal digital lending, this could be a lead generator. The dealer misrepresents the borrower or vehicle information to get financing approval in order to profit on the sale. This is more likely to happen in lower credit spectrums (subprime to deep subprime).

There are other types of misrepresentation done by either the borrower (first party) or the dealer.

  • Straw purchase – When a borrower uses a “straw buyer” (could be a family member or friend with good credit) to secure financing and either pays for the vehicle via the straw buyer or defaults on the loan, impacting that straw buyer’s credit score. 
  • Credit washing – When the borrower makes a false claim of identity theft. When the negative tradeline is removed from the credit bureau, the credit score temporarily goes up and the borrower uses that opportunity to qualify for better loan terms. In some cases, the borrower might also “buy” tradelines to boost their credit score.
  • Credit stacking – When the borrower takes multiple loans simultaneously from different lenders (same day or within a few days) before the initial loan shows up on the credit report. Once the loans hit the bureaus, lenders will see that there is outstanding debt, and so their Debt to Income ratio will be higher and the borrower might not qualify for the second loan. 

Reach out to learn how our Industry Data Consortium helps detect fraud.

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Kartheek Veeravalli Head of Product
Kartheek Veeravalli is the Head of Product, Auto. He has more than 15 years experience in building successful fintech products in auto and consumer lending at defi SOLUTIONS, Cox Automotive and FICO.

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