What is a false decline?

A false decline is an online transaction that gets rejected because it is wrongly identified as fraud. It occurs when a merchant is trying to be cautious about fraud, but over-declining good customers comes at a high cost. 

Studies show that $331 billion were lost to false declines in 2018 and they estimate that this cost merchants $443 billion in 2021. Meanwhile, credit card fraud cost merchants $40 billion in 2018, which means that eCommerce merchants are losing 8 times more to false declines. One reason for this gap/difference is that higher-value orders tend to raise more red flags, meaning the value of declined orders tends to be 1.6 times higher than the average approved purchase.

Of course, over-declining means that not only do merchants leave money on the table, they also potentially damage relationships with loyal customers, negatively impacting their future lifetime value. One in three customers will completely stop shopping with a merchant after experiencing a false decline. 

Common reasons for false declines

Traditionally, manual fraud review teams have leveraged a rules-based approach to order decisions. This means that they determine certain factors that could indicate fraud vs legitimacy based on the order’s details. Once these factors become decisioning rules, some customers will be wrongfully declined because they don’t fit this rigid criteria. Some of the most common reasons for false declines can be summed up according to specific consumer profiles, as outlined below.  

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5 types of consumers who experience false declines

  1. The gift sender: When billing and shipping addresses don’t match, this is a clear signal to fraud detectors that this could be foul play. But of course, when a customer is sending a gift to family and friends (especially multiple items, such as during the holidays), this appears to be risky behavior and the transaction may be wrongfully declined. 
  2. The office shopper/shipper: While this behavior has declined post-COVID-19, many customers prefer to receive packages in the office, especially in urban areas. This type of shopper frequently experiences false declines because their IP address and shipping address don’t align with their billing address.
  3. The expat: People who live in foreign countries are often subjected to false declines if they still use a credit card from their country of origin. Since international credit cards are seen as a risk, someone placing an order from an Australian retailer with a Japanese credit card may be automatically declined.  
  4. The tourist: Similar to expats, foreign travelers who place online orders while traveling to purchase goods at a lower cost (think Nike in the US vs Germany, or Burberry in the UK vs China) will experience false declines. Aside from the international credit card issue, these customers may be seen as placing a “cross-border” order and that is also seen as a risk in fraud detection.
  5. The college student: Plenty of students go off to college with their parents’ credit card in tow. When they start ordering things online to be sent to the dorm, it can prompt not only a billing vs shipping mismatch, but also a customer vs credit card holder name mismatch. The pattern of many orders under different names placed from different IP addresses that are delivered to the same shipping address can look like fraud.

Strategies for preventing false declines

Everyone has probably, at some point in their life, exhibited one or more of the behaviors outlined above. Given how common these types of situations are, you would think that fraud prevention solutions would be able to better navigate these use cases. And yet, the average merchant can lose up to 3% of their revenue to this error, which makes reducing false declines a top priority. Here are some strategies that merchants can implement to prevent false declines:

  • Update your automatic decisioning rules: Some of these rules will automatically decline an order with a billing/shipping mismatch, but in all four customer use cases, this was the case and the customers were legitimate. Catch-all rules like this don’t account for nuances in consumer behavior and lead to high rates of false declines. 
  • Use behavior analytics: A customer’s behavior during a shopping session can be a huge indicator of legitimacy. Fraudsters tend to go straight to checkout, whereas legitimate customers take time to shop around and compare goods. These sorts of indicators can enrich your decisioning process and reduce false declines. 
  • Stop using blocklists: Consumer behaviors change constantly, and so putting any sort of behavior variable or location on a blocklist can limit your ability to approve more good orders. For example, express shipping used to be seen as a fraud risk, but during the pandemic, this became a common practice for customers looking to receive supplies quickly.