Friendly fraud is anything but friendly to a merchant’s bottom line. Retailers lose $11.8 billion annually because of this phenomenon. The FBI has identified friendly fraud as one of the biggest e-commerce threats that merchants face. Unfortunately, it is a problem that retailers are forced to deal with.
Friendly fraud occurs when a customer makes a legitimate purchase using a credit card that they own. However, what happens next is anything but legitimate. The customer then makes a fraudulent claim to have the charge cancelled for one of the following reasons:
- They never received the item they ordered
- The item they ordered did not match the description
- They don’t remember making the purchase
- They cancelled the purchase
- They returned an item back to the merchant
In these situations, the merchandise is often never recovered once the customer has received a chargeback. What makes friendly fraud so difficult to deal with is that it is hard to prove. Customer’s claims are often hard to disprove. However, 86% of chargebacks are actually fraudulent . A good amount of investigation work is needed to be able to prove that a customer is doing something fraudulent. What’s more is that retailers actually risk turning off customers if they mishandle chargebacks and challenge the claims of honest, legitimate customers. Largely, this is why retailers usually opt to simply accept the loss in many cases. Merchants have a lot more to lose than just money when friendly fraud strikes. A merchant runs the risk of being shut down by Visa or Mastercard if more than 1 percent of their charges are reversed. This could spell the end for some small businesses.
How to Stop Friendly Fraud Using the Right Information
So what can merchants do to fight the fire of friendly fraud? Big data offers several channels for spotting and preventing friendly fraud. In fact, big data can be implemented across the board to reduce the likelihood of this type of fraud from occurring at all. Information is at the core of the solution. Retailers and enterprises need a way to access the data behind every transaction. This requires a big data platform that is able to take in, store and analyze the information surrounding all sales transactions.
Customer profiles are a helpful tool that can be used to identify friendly fraud or spot habitual returns. These profiles can be automatically created and managed using big data. What’s more, any returns or purchase activities labeled as suspicious could be automatically detected using a series of preset rules. For instance, a customer with a history of chargebacks resulting from merchandise that was never received could be quickly identified by a system after a predetermined number of returns. An enterprise or retailer could then take appropriate steps to investigate the situation more thoroughly and decide what the resolution should be.
Transactions that result in a customer requesting a chargeback can be automatically analyzed based on certain pieces of criteria that indicate red flags in real time. This means that an enterprise could define signs that place a chargeback claim at high risk for being fraudulent. The big data platform in use can then essentially make a judgment call based on all of the information that is available regarding a specific order. The list of red flags built into a system could include things like purchases that were made via proxy servers or sent to mailing destinations known for high levels of fraud. In addition, details like shipment tracking and delivery confirmations can also be utilized as part of the overall formula that a platform uses to determine if a chargeback is likely to be legitimate.
The amazing thing about using a big data platform is that it can use past instances of friendly fraud to detect future ones in real time. This can help to lower the chances of allowing what appears to be a fraudulent transaction from going through and benefit the merchant enormously in the long run.