How to Prevent Chargebacks? Stop Them at the Source with Machine Learning

Chargebacks are costly but machine learning fraud detection can help.

Chargebacks are among the most costly issues enterprise businesses face. Research estimates that they can cost as much as two to three times the initial transaction amount when you account for fees, wasted time, loss of goods, and the loss of revenue. 

What’s worse is that chargebacks often appear from seemingly nowhere. Customers are more likely to contact their bank to get instant resolution rather than reach out to the business to dispute the charge. And to further complicate matters, the burden of responsibility falls on the business to prove that a charge was, in fact, valid, thus starting a long, complex process of due diligence and fact-finding.

DataVisor expert Sean McDermott weighs in on why traditional chargeback management is no longer enough to combat fraud and how machine learning fraud detection provides a superior solution.

Q. What’s Involved in Traditional Chargeback Management?

DataVisor: Chargebacks are like forced refunds, and you usually have 45 days to dispute them. Typically, there’s a manual element to reviewing chargebacks, and the process is subject to the discretion of the person reviewing the chargeback. 

To do this well, businesses must first know they have a chargeback in need of attention, but this often isn’t immediately apparent. This takes away precious time from the process and shortens the gap in which you’re able to look into each situation in detail.

You’re also responsible for providing proof that a chargeback is unwarranted. This could include tracking down a receipt with a signature, a timestamp, transcript, or some form of authentication that you collected from the customer. But not just any evidence will do — merchants must submit compelling evidence related to the specific chargeback code. 

In many cases, a lot of merchants simply let the chargeback go, thinking that it isn’t worth the fight or that they will end up losing more than the chargeback by investing time in disputing it. Maybe that makes sense for one chargeback, but enterprise companies that may have hundreds or even thousands per year are losing too much revenue to simply ignore it.

Q. How is Machine Learning Fraud Detection More Impactful for Merchants?

DataVisor: Machine learning fraud detection software can help companies recognize potential chargeback issues that are actually fraudulent claims. Machine learning’s ability to work with large data sets at scale in real-time can develop deeper insights and connections between data points, which can be useful in sniffing out fraudulent behaviors and patterns. 

With this information, merchants are better positioned to eliminate fraud-related losses, and the revenue leaks they create. DataVisor has a 99%+ accuracy rating for detecting and blocking fake accounts, payment abusers, and actions performed by device farms and bots.

And machine learning does all of this without the intervention of manual review processes.

As a result, cases assume a binary labeling approach as either fraudulent or authentic, with little or no context surrounding them.

Combating Fraud with Machine Learning

DataVisor leverages unsupervised machine learning (UML) that doesn’t require rigorous data training and can review larger data sets at scale to better identify fraud. This gives financial organizations a chance to act on fraud in real-time, resulting in fewer false positives so that banks and merchants can provide a better experience to their customers. 

To learn more about UML and machine learning fraud detection, click here to schedule a free demo.