False declines: A guide for ecommerce merchants

What is a false decline?

A false decline is an online transaction that gets rejected because it is wrongly identified as fraud. A Statista survey revealed that in 2023, 56 percent of U.S. shoppers had their payment declined when purchasing online. This was the highest value compared to other countries like China or the United Kingdom.

False declines typically occur when a merchant is trying to be cautious about fraud – which is a good thing – but over-declining good customers comes at a high cost. False declines were expected to generate a $157B loss (Nuvei) for US merchants alone in 2023. Globally, false declines were set to exceed $443B (Aite-Novarica), far outweighing the projected $48B loss globally from actual ecommerce credit card fraud (Statista).

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. False declines can cost companies customers, with 41% of consumers globally saying they’ll never shop with a brand after 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 versus 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:

5 types of consumers who experience false declines

• 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.
• 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 typically seen as a risk, someone placing an order from an Australian retailer with a Japanese credit card Bank Identification Number (BIN) may be automatically declined.
• The tourist: Similar to expats, foreign travelers who place online orders while traveling to purchase goods at a lower cost (think Nike products in the US vs Germany, or Burberry items in the UK vs China) will often 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.
• The college student: Plenty of students go off to college with their parents’ credit cards in tow. When they start ordering things online to be sent to the dorm, it can prompt not only a billing versus shipping mismatch but also a customer versus 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 5.5% of their annual revenue to this error (per Riskified research), 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.
• Limit blocklists: Consumer behaviors change constantly, 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 very high fraud risk, but leading up to key holidays, this has become a more common practice for last-minute customers looking to receive supplies quickly.
• Embrace machine learning: Human analysts may not be as effective at discerning big-picture fraud patterns. A machine learning fraud solution though, is equipped with a rich history of thousands of data points, including IP and device, to refine decisioning based on the most influential data combinations of an order at any given time. The result is a more accurate and adaptive understanding of shopping patterns.
• Appraise digital identity: With a more nuanced understanding of digital identity, you can assess risk on an individual basis, and discern more than just names and addresses. AI-driven technology like Identity Explore will help broaden your view of a customer’s identity beyond their single profile equipping your fraud team with thousands of data points into account across transactions, cookies, emails, and phone numbers. Empowered with these real-time insights, merchants can differentiate the good customers from the bad at checkout.

Frequently asked questions