Why is up to 30-90% of fraud often classified as friendly fraud?

As strange as the name sounds, there's nothing particularly friendly about it.

It usually refers to cases where a legitimate cardholder later disputes a transaction, claiming it was unauthorised or unrecognised.

Depending on the industry, this can be anywhere between 30% to 90% of all fraud cases.

Which makes it a very different kind of problem, because now you're not trying to detect fraud before it happens, you're trying to prove what already happened.

The dispute process

That's where the dispute process comes in - compiling evidence, pulling transaction data, device signals, behavioural patterns, delivery confirmation. All to demonstrate that the transaction was legitimate.

The challenge is how fragmented and manual the process is. Even for teams that understand it well, it can take time, attention, and consistency to do it properly.

Why we built FraudPulse

This is one of the areas that led Yaniv Hayun and me to build FraudPulse.

Instead of just flagging a chargeback, the idea is to go one step further.

If something looks like friendly fraud, the system can pull the relevant data, structure it, and generate the full compelling evidence document automatically.

Beyond that, it also tells you what to do next and answers:

• What's happening?
• Why it's happening?
• How to reduce it going forward?

The idea is to make fraud easier to deal with. If you're working with chargebacks like this and it's taking up too much time, Book a Demo.

How do I tell friendly fraud from real fraud on Shopify chargebacks?

Friendly fraud is a real customer disputing a legitimate charge; true fraud is stolen cards, testing, or takeover. Shopify reason codes and order context help, but mixed queues need classification. FraudPulse classifies chargebacks by type (including friendly fraud vs other types) and ranks Shopify Protect / Radar rule changes with estimated impact - prevention, not representment.

Look at reason codes, delivery, and whether the customer is known - then classify the pattern. FraudPulse classifies chargeback types from your history so rules match the mix, not a single anecdote. We do not win friendly-fraud cases like Chargeflow or Justt; we help you change rules so fewer of those chargebacks keep happening.

How to fight friendly fraud on Shopify

“Fight” friendly fraud on Shopify means prevent repeats (clearer descriptors, delivery evidence, and rules) and optionally represent individual cases. FraudPulse focuses on prevention: classify friendly-fraud chargebacks and rank Protect/Radar changes with estimated capture and false-positive rates. Recovery apps fight the case after it files - we do not submit representment packets.

Tighten the rules and ops that let it repeat; use Shopify and network evidence for cases you fight. FraudPulse ranks prevention rule changes from your classified chargebacks and does not submit representment packets. See how it works, pricing, and the FAQ.