November 25, 2025
Friendly Fraud: The Fastest-Growing CX Problem in Digital Commerce
It might be tempting to dismiss the phrase as a paradox: “fraud” that’s “friendly”? Yet for merchants, it is anything but benign. Friendly fraud (sometimes tagged as “chargeback fraud” or first-party misuse) refers to situations where a customer makes a legitimate purchase and then later disputes the charge with their card-issuer, often claiming the transaction wasn’t authorised, or the goods weren’t received, even though they were.
At its root, friendly fraud includes anything from genuine misunderstanding (for example, a customer forgetting about a purchase) to intentional abuse (keeping the product while disputing the charge).
The Drivers Behind the Surge
According to one source, friendly fraud may account for up to 70% of all credit-card fraud. On top of that, 62% of businesses experienced an increase in friendly-fraud incidents over the previous two years.
Adding to this, a recent press release from ACI Worldwide, a global payment technology firm, reveals that friendly-fraud incidents are expected to rise by 25% between Thanksgiving and Cyber Monday this year, based on analysis of billions of global e-commerce transactions.
According to ACI Worldwide, friendly-fraud losses have become a material cost centre for retailers, reaching $103 billion in 2024. ACI’s analysis shows that the problem is intensifying during peak shopping windows. The company expects the average transaction value for friendly-fraud cases over the holiday period to be 21% higher than last year, indicating that disputes are increasingly tied to higher-ticket items rather than low-value impulse purchases.
The wider Black Friday to Cyber Monday window is also set to bring structural pressures that make friendly fraud more likely. ACI projects 27% year-on-year growth in transactional volume and a 30% increase in mobile-device shopping, a channel where customers often check out faster and are more prone to disputing unfamiliar charges.
In parallel, the company anticipates a 98% fraud-decision approval rate, exceeding the typical market average of 95% — a sign that merchants using more advanced decisioning models can easily separate legitimate disputes from abuse in real time.
Where Friendly Fraud Starts in the Customer Journey
Friendly fraud often begins with a breakdown in communication or expectation. A common trigger is when the name shown on the customer’s bank statement doesn’t match the brand they bought from, leading them to think the transaction isn’t theirs and file a chargeback.
Customers may forget a subscription they signed up for. They may feel the return policy is opaque or find the refund journey too tedious, leading them to file a chargeback rather than engage with the merchant.
Intentional cases are more sinister, in which customers buy high-value goods, then file disputes to effectively get them for free. Some analytics suggest repeat offenders may account for a significant share of disputes.
As a result, merchants grow frustrated, increase dispute-handling costs, lose inventory, and potentially provide poorer service for honest customers caught in anti-fraud measures.
What Should Organisations Do to Tackle Friendly Fraud?
Given the magnitude of the issue, companies cannot treat friendly fraud like a fringe problem. Here are some key actions aligned with a customer-experience mindset:
- Clarify billing descriptors and transaction communications. Make sure customers immediately recognise purchases on their statement and in confirmation emails, which reduces the “I don’t remember buying this” excuse.
- Offer transparent returns and refund journeys. When customers can self-serve a return or refund in a straightforward way, they’re less likely to jump directly to a chargeback.
- Track fulfilment and delivery convincingly. Having delivery-proof or tracking in place gives merchants stronger ground to contest illegitimate disputes.
- Leverage intelligent fraud-prevention systems. As the ACI data emphasises, solutions that combine real-time monitoring, identity profiling, network data-sharing and machine learning make a difference.
