Fraud for Need vs. Fraud for Greed

Fraud for Need vs. Fraud for Greed

Synthetic identities have been all the talk from a fraud risk perspective for lenders for decades , but not all synthetic identities result in losses for lenders. It begs the question, why would someone create a synthetic identity to finance a car when they have every intention of paying the loan as agreed?

That question brings us to the topic of fraud for need versus fraud for greed. There are different types of synthetic identities and different levels of risk associated with them. Unfortunately, just knowing that an identity is synthetic, while posing greater risk than true borrower identities, is not enough to accurately assess their propensity to default. Understanding the different types of synthetic identities, explained below in order from riskiest to least risky, can help lenders better differentiate them and treat them appropriately.

Fraud Ring or True Synthetics

These are the worst kind of synthetic fraud and are often what keep fraud managers awake at night. These are groups of people or individuals that create and groom entirely fictitious identities with the intention of busting out by simultaneously purchasing multiple cars with no intention of repaying the lender. Stealing cars is much easier when the dealership simply hands you the keys without thinking anything is amiss.

Recovering these vehicles is seldom successful even if it’s caught early in the process because the cars are often sold abroad for a quick and lucrative profit. Fortunately, this is one of the least common types of synthetic identities, but it can be devastating for lenders when they slip through.

At Point Predictive, we help lenders identify this type of synthetic because they almost always use fake employers, and will often recycle addresses, phone numbers, employers, and emails across multiple identities. Our proprietary data repository allows us to identify these trends across multiple lenders early before substantial losses occur.

Credit Washers

Credit washing is its own issue independent of synthetic identities, but some individuals find it easier to start with a clean slate by creating a synthetic identity. Generally, these identities are a blend of the perpetrator’s true identity and falsified personally identifying information often using “CPN’s” (credit privacy numbers) – social security numbers (SSNs) that don’t truly belong to them.

Credit washers often intend to pay for what they purchase but are looking to qualify for loan amounts or terms they otherwise wouldn’t. This presents significant additional risk to lenders

that wouldn’t extend credit or such favorable terms to the individual if they were aware of their true history.

This type of synthetic will often bolster their fake credit histories similarly to the true synthetics: using fake trade lines, authorized tradelines, and piggybacking on trade lines to build a strong credit score. While not as risky as the first population, the risk of default is still high.

The underserved

This type of synthetic is often created by recent immigrants who don’t have a real SSN. Even if they have an individual taxpayer identification number (ITIN), they often find it harder to obtain credit using traditional methods. To simplify the process, they obtain or invent an SSN to use.

It is quite common for these individuals to use real information for everything except their SSN, and they commonly repay their obligations successfully. While they do carry greater risk than typical prime borrowers, many near prime and subprime lenders have seen strong performance out of these populations.

Accidents and Ignorants

Typos are certainly the greatest cause of synthetic identity creation. A nickname, new address, and misremembered or mistyped SSN is often all that is needed to inadvertently create one of these.

Grouped with typos are those who are duped into believing that they are protecting their own identities from being stolen by using fake information. Even though these accidental fraudsters present little additional risk over the general population, there is some unknown when working with these individuals.

Determining the difference

On the surface, it can be very difficult to determine which type of synthetic identity you’re working with once you’ve determined that you are dealing with one. At Point Predictive, we find that the right combination of data, machine learning algorithms, and expertise not only helps lenders to sniff out fraudsters, but also determine accurately the risk associated with the applicant.

If you’re interested in better understanding what happens when individuals in the “fraud for need” camp encounter high levels of inflation, watch for Lisa Verdon’s upcoming paper documenting the trends we’ve seen in the data repository.