False declines cost merchants $118 billion per year in lost transactions. As high as this number is, it doesn’t include the additional lost revenue that results from irritated customers taking their business elsewhere. In addition, if a customer is angry about their transaction being falsely declined, they won’t be shy about it, and can quickly spread their displeasure across their social media channels. The result? A wealth of potential customers who decide to cross that business off their list.
To reduce false positives, the first step is to know why and how they happen. Then, merchants must take a very close look at their own fraud operations to see how many false positives they’ve had — and what they can do about it.
How False Positives Happen
Many businesses take one of two approaches when it comes to fraud screening: manual in-house screening, or automated fraud screening. Surprisingly, both of these approaches can increase the risk of false positives:
Manual Screening: In-house fraud teams are typically small. Very small. Even in large companies with hundreds of millions in revenue, the average fraud team is about 2-3 people. And typically, these very busy people are primarily judged on the company’s chargeback rate – in other words, teams are evaluated on the amount of chargebacks they prevent.
Unfortunately, this creates an environment where fraud teams are likely to err on the side of caution when reviewing orders, to the point where even the tiniest breath of a red flag can be enough to earn a decline.
On the flip side, less than 30 percent of companies routinely track false positives as a key metric, and 42 percent don’t even know their false positive rate. So, these companies may very well have a high false positive rate, resulting in significant loss of revenue – but they may not even know it.
Automated Reviewing: The other major approach is to turn to technology for e-commerce fraud protection via automated fraud screening services. These services, which use AI/machine learning, can process transactions quickly and can recognize patterns or triggers that identify potential fraud. However, they’re not immune to false declines either. If a customer shops in a different way (buying something while overseas, for example, or entering in a new shipping destination because the item is a gift), the AI may flag it as a fraudulent transaction and decline it. And, if the company doesn’t manually review their declined orders, they may never realize that a good customer of theirs was declined … and may have taken their business elsewhere.
What Merchants Should Do
When it comes to false positives, they key is not to adopt the “out of sight, out of mind” approach. Whether a merchant’s fraud protection relies on George, Lynn, and Tracy down the hall or a massive server across the country, it’s vital to take the time to look at declines, figure out why those declines happened, and determine which ones were done in error.
This is a lot of work, however.
Fortunately, there’s an easier way to handle the threat of false declines.
A hybrid solution uses AI/machine learning to do an initial screening. Then, any flagged transactions are handed off to an expert team of manual reviewers to analyze the flags, figure out what’s happening, and if needed, contact the cardholder to get clarification. This way, transactions are only declined if they’re genuinely fraudulent, not merely unusual.
That’s how we do things at ClearSale. In addition, our fraud experts are not assessed solely on chargeback rates. High standards and multiple KPIs result in a 360-degree view of every transaction.
By using the most effective tools available and looking at fraud from multiple angles, merchants can have the best of both worlds: reduced chargebacks and a lowered risk of false declines.
Want to learn more? Download our ebook, Understanding the E-Commerce Payment Chain: How to Prevent False Declines, Stop Fraud and Maximize Sales