Holiday False Declines

Table of Contents

1. What Are False Declines?
2. Why Are New Customers Likely to Get False Declines?
3. Why Are False Declines So Costly?
4. How Can Merchants Prevent False Declines?

Every time the holiday season rolls around, merchants see a big uptick in sales—and a big uptick in fraud and chargebacks. However, this holiday season could be a special one for e-commerce merchants in particular.

Many customers entered the world of e-commerce for the first time because of the coronavirus pandemic, broadening the customer base for online sales. In addition, many of those who avoided celebrating with their families in 2020 will be especially excited to get together with their loved ones again this year. That could mean more presents than usual, and more sales for merchants.

While fraud prevention tools are a crucial part of any e-commerce business, these tools aren't without their downsides. In many cases, fraud prevention software can be a bit overzealous, especially if it hasn't been customized to the needs of a particular merchant. The software may flag purchases from legitimate customers as potentially fraudulent, which means the merchant will lose out on a sale and a customer.

In addition to missing out on the declined sale itself, the merchant also loses out on any future purchases that customer might have made. Rejected orders don't usually result in repeat customers.

Fraud is a pernicious problem that carries many negative consequences for its victims, but the opposite problem—good customers mistakenly identified as fraudsters—can have financial consequences for merchants that are just as serious. Some analysts estimate that the revenue losses from false declines exceed that of fraud by a factor of 70. False declines won’t lead to downstream problems like chargebacks, but they can have a serious negative impact on your business.

Every year, Christmas shopping always brings in a lot of new customers for merchants, and that’s especially true this year, with e-commerce shopping growing more than 32% in 2020. The trouble is that brand-new customers with no order history can behave in ways that can seem unusual to fraud detection algorithms.

These algorithms are usually created based on a data set of activity from e-commerce shoppers, the majority of which are experienced and exhibit predictable purchasing behavior.

Since customers who are brand new to e-commerce make up a small minority of online shopping activity at any given time, fraud prevention tools may mistake their unusual behavior for a sign of fraud.

To a new customer, a merchant declining an order feels like being told “we don’t want your business,” and that’s the last thing a merchant should be communicating.

The problem is compounded this year by the fact that many customers turned to e-commerce for the first time over the course of 2020 and 2021. Here are some of the reasons why these new customers might be perceived as possible fraudsters by software tools:

• No order history. Fraud tools trust repeat customers, but brand new ones are always a little bit suspect. Right from the outset, new customers are likely to be subjected to extra scrutiny.
  
  
• Unfamiliar devices. Once again, fraud tools trust what they recognize, and new shoppers arrive on new devices that may look suspicious to anti-fraud screeners.
  
  
• Mismatched addresses. Many of those who became unemployed during the pandemic have moved to find work, and not everyone remembers to update their address with their bank. That means that fraud tools are seeing shipping addresses and billing addresses that don’t match, and that’s always a red flag.

While some merchants will manually review flagged orders, the high transaction volume of the holiday season leads many to rely on automated tools that decline orders without approval from a human operator. When systems like that are being utilized, many of the perfectly legitimate orders listed as examples above would be automatically rejected.

One third of all customers who have a transaction falsely declined will never shop from that merchant again, and a quarter of them are likely to vent their frustrations about it on social media.

Worse yet, anti-fraud tools that update their screening algorithms according to past experience may learn bad habits from false declines—which just leads to more false declines.

Of course, the solution to this problem isn’t to turn off your anti-fraud filters. Increases in e-commerce shopping also lead to increases in fraud attempts, as cybercriminals attempt to take advantage of the high volume of transactions merchants are dealing with.

A robust system of anti-fraud defenses is essential for e-commerce merchants, especially those experiencing a high order volume. Unfortunately, there’s no safe way to treat these as “set it and forget it” tools, especially with the pandemic disrupting normal patterns of consumer behavior. Merchants who give AI software a free hand to reject orders and decline transactions run a very high risk of losing significant revenue to false declines.

When caught between a rock and a hard place like this, applying human intelligence is often the best solution.

Manual review of flagged orders can be time-consuming and challenging, but it’s worth the effort when you consider how damaging a high rate of false declines can be.

Automated tools do have their place, especially for merchants who have analyzed their fraudulent transactions and can identify consistent and reliable fraud indicators. However, during this holiday shopping season where many new customers with little experience as e-commerce shoppers are expected to descend in great numbers upon online merchants, it just isn’t safe for most merchants to risk false holiday declines. Neither is it safe to risk angering customers and inviting unwinnable chargebacks by accepting every flagged order.

The best option for merchants is to employ manual review processes informed by experience and good judgment. By studying the instances of fraud that come to you through chargeback analysis, you can make more accurate decisions about when to let an order through and when to allow a decline.

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