In 2018, Australian online retailers are forecasted to pay $240 million in chargebacks, representing roughly 2 per cent of the $12 billion in anticipated eCommerce sales. But it doesn’t end in lost profit. High chargeback rates can also lead to mandatory enrollment in card networks’ excessive chargeback programs, which entail higher processing fees, fines, and even mandatory risk audits.

Many retailers simply accept these risks as a cost of doing business. But as online verticals become more competitive and margins shrink, preventing those losses is a necessity.

There are two primary ways to fight chargebacks: one is to prevent them from ever occurring, with accurate fraud detection. And on the flip side, many chargebacks that occur as a result of chargeback abuse can be overturned if merchants follow best practices for disputing them.

Chargeback prevention powered by machine learning technology

Many retailers manually review suspicious-looking orders for fraud, to decide whether or not to approved them. Over the last few years, however, machine-learning technology in this arena has advanced to the point where human eyes simply can’t process orders with the same accuracy as automated tools.

A machine-learning fraud solution can process and weigh thousands of data points, including cross-checking against any order it has reviewed before, in milliseconds. Furthermore, it can identify patterns and trends that a standard fraud detection solution can’t, distinguishing legitimate shopping behavior from fraud. In addition to improving review accuracy, which will slash chargeback rates, automated solutions are also scalable – meaning online retailers utilizing these systems don’t have to worry about growing their fraud teams as they expand.

Effectively dispute and overturn chargeback abuse

Many chargebacks are a result of fraudsters using stolen credit cards, and posing as the rightful card owners. But a significant portion – as high as 25 per cent – of direct fraud losses are due to chargeback abuse – so called “liar buyers” who order with their own cards, then falsely report that they didn’t make the purchase, and collect the refund.

This type of fraud is extremely tough to catch and decline at checkout, because the shopper’s identity really does match the card. The key to fighting these chargebacks is to provide card issuers with compelling evidence that the order was in fact authorised and received by the true card owner. The more quality evidence a merchant can provide to support the legitimacy of a transaction, the better the chance of overturning the chargeback. It’s critical that throughout the shopping journey, merchants collect relevant data to use in case the order is charged back and looks like a candidate for dispute.

For example:

• Does the shopper have a legitimate shopping track record using the same payment methods and devices?
• Is the IP location (the customer’s geographical location) in close proximity to the billing address?
• Does the email address itself contain the same name provided in the billing or shipping details?
• Is the email address linked to social profiles that match the customer’s name?

Conclusion

Fraudsters aren’t going anywhere, and effectively detecting suspicious orders will be key to staying competitive. In addition to efficient fraud prevention at checkout, it’s important that online retailers have the infrastructure in place to successfully dispute chargebacks that occur as a result of chargeback abuse, to recoup valuable revenue, and to stop these “liar buyers” in their tracks. Riskified’s guide has more information about best practices for disputing chargebacks, and can be downloaded here for free.