Lenders are losing money due to first-party fraud, which isn’t always a theft of identity. First-party fraud can strain relationships with good customers and hinder growth when people who provided accurate information at one point in their customer lifecycle develop fraudulent intent.
One example is fronting – using someone else’s name to obtain credit cards. Another is a misuse of the dispute process, sometimes called friendly fraud.
Unlike third-party fraud, which organized crime groups may commit, first party fraud is often opportunistic. Fraudsters look for opportunities to misrepresent themselves or their intentions for financial gain, and they can do so at any stage of the customer journey. Examples include fronting (when a lower-risk driver is added to an auto insurance policy, leading to higher premiums) and chargeback misuse (also known as friendly fraud), where consumers file false disputes on credit card transactions that were valid when they made them.
Detecting these types of fraud can be difficult, mainly when fraudsters use legitimate information to spoof their identity. Organizations need a robust ID verification system that ranks and prioritizes consumer identity elements to identify these risks better. This will allow them to spot and investigate suspicious transactions more quickly, and prevent fraudulent activities like bust-out fraud, where a fraudster establishes a credit profile over time, maxes out their credit limit, then “busts out” and disappears before the dispute can be resolved.
First-party fraud can be opportunistic, perpetrated on a small scale by an individual, or highly organized, carried out at a large scale by an organization such as a criminal gang. In both cases, first-party fraud hurts businesses. This includes inventory loss and customer relationship damage when fraudsters successfully claim chargebacks and refunds.
Fraudsters also engage in several different activities such as wardrobing (ordering goods and then falsely claiming that they never arrived or were damaged on arrival), GLIT fraud – returning empty boxes to receive a refund – or friendly copy, when an individual files a fraudulent chargeback against their card.
While it isn’t possible to eliminate all types of fraud, businesses can take steps to reduce the risk. Companies can spot potential fraudsters early by constantly monitoring customers, having chargeback protection, and using big data analytics tools. This helps prevent them from gaining a foothold and committing fraud on a larger scale.
First-party fraud can occur in several ways. It often presents itself as fronting (when a customer lies to obtain a loan or credit card that they won’t pay back), de-shopping (buying items that are then returned to receive the total price), and chargeback misuse (also known as friendly fraud) which happens when legitimate customers dispute transactions on their credit cards but have malicious intent in doing so.
While this type of fraud doesn’t involve stolen identity, it still has devastating consequences for businesses that don’t have anti-fraud solid systems. It leads to financial loss, damages the customer experience, and strains relationships when fraudsters abuse automated tools designed to protect them from fraud. This leads to frustrated customers, who may leave a business or shop elsewhere. The good news is that opportunistic fraud can be prevented by using predictive analytics to assess applications on a granular level.
While many fraudsters are content to run up charges on a stolen credit card and disappear, some criminal gangs are dedicated to playing the long game. These are called bust-out fraudsters. They often apply for several credit cards using a synthetic identity, build good credit by making payments and obtaining credit line increases, then max out their credit cards and suddenly withdraw large sums without intending to repay the loans. Actual chargebacks bleed profits for merchants, issuers, and telcos that offer payment processing or handset financing services.
This type of fraud can be prevented by leveraging an approach that combines natural and fake data to identify inconsistencies in the phony identity. By combining multiple data inputs and using advanced artificial intelligence models to detect predictive patterns, this fraud can be stopped early in the cycle before it becomes too costly. This is a case where prevention is worth a pound of detection.