Friendly Fraud – what is it, and how to protect yourself from it?

Table of contents

1. What is Friendly Fraud?
2. Key characteristics of Friendly Fraud
3. The two faces of Friendly Fraud
4. The scale of the problem
5. Behavioural Verification as the answer to Friendly Fraud
6. Summary

In an era of rapidly expanding e-commerce and the digitisation of services, financial institutions and merchants are having to cope with increasingly sophisticated forms of fraud. Whereas there is widespread awareness of traditional types of fraud, such as the theft of card details by third parties, one of the forms most difficult to detect - and one that places a significant burden on businesses - is Friendly Fraud.

What is Friendly Fraud?

Friendly Fraud, also known as chargeback fraud, is a situation in which a customer - the lawful cardholder - disputes a legitimate purchase, claiming that the transaction was unauthorised or that they never received the product or service they ordered. A key issue here is the fact that the purchase was actually made, and that the transaction was initiated by the cardholder or somebody close to them, such as a family member.

Key characteristics of Friendly Fraud

• Perpetrator: the cardholder or another member of their household.

• Action: rather than reporting the problem to the merchant and going through the standard refund procedure, the customer contacts the bank directly to initiate a refund (chargeback).

• Motivation: may be unintentional (for example, forgetting about a subscription or failing to recognise a charge) or deliberate (obtaining a refund while simultaneously keeping the goods or digital service).

Friendly Fraud falls within the category of so-called first-party fraud - that is, fraud committed by customers themselves. The growing scale of this type of fraud undermines trust in digital transactions and generates enormous losses across the entire ecosystem. This is why it is so important to implement advanced mechanisms capable of distinguishing between a genuine mistake and deliberate fraud.

The two faces of Friendly Fraud

Friendly Fraud is insidious, as by its very nature it blurs the boundary between customer error and deliberate fraud. To counter it effectively, it is essential to understand its dual nature.

Mistake vs deliberate fraud

Friendly Fraud can be divided into two main types, which differ in the customer's motivation and intent:

Unintentional behaviour

In this case, the customer's actions are the result of a mistake rather than bad faith. The customer is convinced that the transaction has been mistakenly attributed to them because:

• the bank statement shows an abbreviated or different business name (for example, the name of a payment gateway instead of the merchant), leading the customer to fail to recognise the charge,

• the customer forgets about a purchase made some time earlier or about an active, automatically renewed subscription,

• the transaction was made by a child or other member of the household using the card, and the cardholder disputes it, having forgotten that authorisation was given.

Deliberate behaviour

This type of conduct constitutes genuine fraud and involves bad faith and a clear intent to defraud. The customer deliberately uses the chargeback mechanism to obtain a refund while retaining the goods or digital service. This is a form of digital theft, in which the customer:

• claims that the physical product never arrived, despite the existence of delivery confirmation,

• disputes in-app purchases or payments for digital services in order to obtain free access or goods.

The scale of the problem

The scale of Friendly Fraud is mushrooming, as confirmed by the latest data. According to statistics, so-called first-party fraud (of which Friendly Fraud is a subset) accounted for as much as 36% of all reported fraud in 2024. This represents more than a doubling compared to the 15% recorded in 2023. Experts associate this rapid trend with economic pressures, such as inflation and the rising cost of living, which prompt some consumers to deliberately abuse dispute and chargeback procedures.

The impact of Friendly Fraud on the ecosystem

• Financial losses: merchants not only lose goods, but also incur processing fees for handling chargebacks.

• Operational burdens: managing disputes is time-consuming and requires resources to gather evidence and communicate with banks.

• Reputational risk: repeated disputes and high chargeback rates can lead to reputational damage among payment operators and, in extreme cases, even to fines.

Behavioural Verification as the answer to Friendly Fraud

In the face of the snowballing scale of fraud and the challenge of distinguishing deliberate fraud from genuine mistakes, static methods of protection are becoming insufficient. This is precisely where advanced tools based on behavioural verification come into play.

The Behavioural Verification Platform uses innovative technology that monitors how the user interacts with their device - whether a computer, smartphone or tablet - and the online platform. It generates a unique "behavioural profile" for each customer, based on parameters such as typing speed, cursor movement, screen-touch patterns and navigation fluidity. Even if a criminal obtains authentication credentials, their behaviour will almost always deviate from that recorded in the profile of the legitimate user.

How BIK's Behavioural Verification supports the detection of Friendly Fraud

The systems developed by BIK, based on behavioural verification, allow merchants and financial institutions to gather evidence that effectively prevents false claims or enables them to be successfully challenged.

Below is an overview of the key mechanisms through which this technology limits or detects cases of Friendly Fraud.

Mechanism	How it works	How it may help address Friendly Fraud
Anomaly detection	The system compares the user's current behaviour with a historical, unique behavioural profile.	If a customer claims a transaction was made by "someone else", it is possible to verify whether behaviour during the session (e.g. click speed, touch pattern) was consistent with the customer's usual behaviour. If not, this may indicate account takeover; if it was, there is a greater likelihood of deliberate fraud.
Continuous session verification	The system monitors behaviour not only at login, but throughout the entire transaction session.	It can detect the moment when "something changes" - for example, the legitimate user logs in, but the behavioural pattern shifts during the transaction, which may indicate that control has been taken over by someone else or that the customer is acting with premeditation.
Alerts / additional authorisation in the event of suspicious activity	When behaviour deviates significantly from the established pattern, the system may require additional verification, such as SMS confirmation, a call with the customer or password entry.	The requirement for additional verification acts as a barrier. A person who later claims that "it wasn't me" will face difficulties, because the system has recorded that the unusual behaviour was additionally confirmed by the customer.
Building stronger evidence	Behavioural data can be used as part of an investigation and as detailed documentation in a dispute (chargeback).	The merchant or bank can demonstrate that behaviour during the session was consistent with the customer's profile, thereby effectively countering false Friendly Fraud claims. Moreover, robust behavioural data serve as strong evidence in civil proceedings against a customer who deliberately simulates unauthorised transactions.

Summary

Friendly Fraud is currently one of the most serious challenges for digital commerce. Its scale is growing rapidly, generating significant losses and operational burdens for businesses. As traditional verification methods struggle to distinguish between a genuine mistake and deliberate fraud, it has become necessary to implement advanced mechanisms.

This is precisely why BIK has prioritised behavioural verification - a technology capable of creating unique user profiles and detecting even the slightest anomalies in how users interact with the platform. This ensures not only prevention, but also the gathering of strong evidence in chargeback disputes, including the possibility of using such evidence in civil proceedings.