#55 - Is it 1st or 3rd party fraud? (and why it matters)

Nov 29

When you get hit by a fraud attack, your first question should be "How bad is it?"

But the second question should be: “What kind of fraud am I dealing with?"

The difference between 1st-party and 3rd-party fraud isn't just technical–it fundamentally changes how you respond, what tools you deploy, and ultimately, how successful you'll be at stopping it.

Yet I'm constantly amazed by how many Fintech leaders can't tell the difference, or worse, don't think it matters.

So today, I'm going to break down how to identify which type of fraud you're facing and why getting it right is critical to your business.

The Fundamental Difference

Let's start with the basics:

3rd-party fraud happens when someone steals payment methods or identities (or creates synthetic identities) to defraud your business. The fraudster pretends to be someone else entirely.

1st-party fraud happens when real customers use their actual identities but have no intention of honoring their commitments. They are who they say they are, but their intentions are fraudulent, whether they commit chargeback fraud, returns fraud, or any other form of policy abuse.

Sounds simple on paper, right?

The problem is that in the real world, the lines get blurry fast.

What to Look For

After years of working with fraud teams across dozens of Fintechs, I've noticed distinct patterns that separate these fraud types. Here's what you should be looking for:

For 1st-party fraud:

Fraud cases that are notably NOT connected by "online assets" (devices or IP networks)
Early chargeback maturation, especially in the first week after transaction
Absence of traditional "fraud signals" like geo-mismatches or bad links
Higher transaction velocity is often the only suspicious signal
Established account history with no ATO indicators

For 3rd-party fraud:

Strong connections between fraud cases via shared devices or IP networks
Abnormal shared behavioral patterns (like identical email conventions across supposedly different people)
Suspiciously new (yet clean) identity assets like emails and phone numbers
Geographic mismatches (foreign country IP addressing your US service)
Unusually high issuer decline rates

The difference is stark once you know what to look for.

Yet I regularly encounter teams using the wrong detection methods for the fraud type they're actually facing.

Side note: As I mentioned above, in some fraud topologies the lines get blurry. This is often the case with money mules, money laundering, and collusion fraud. The world is never black or white, sorry.

Why Does it Even Matter?

Here's what I keep seeing in the industry: companies implementing the wrong solutions because they haven't properly identified what they're up against.

It happens so frequently that I'm starting to think it's the rule, not the exception.

And it costs these companies millions.

Why? Because fraud tools are built for specific problems. Use them on the wrong problem, and you're essentially throwing money away.

Let me give you some real examples I've encountered:

A Fintech dealing with obvious 1st-party fraud (early chargebacks, established accounts) decided to invest heavily in:

Advanced KYC verification
Multi-factor authentication
Device fingerprinting upgrades

Unsurprisingly, their fraud rates kept climbing. These tools verify identity–something 1st-party fraudsters already have legitimately.

Then there's the opposite scenario:

A SaaS platform hit by classic 3rd-party fraud (connected devices, new email accounts) focused on:

Transaction history analysis
Credit risk scoring
Account tenure rules

Meanwhile, they completely missed that 80% of their fraud was coming from the same three IP networks.

There's also a critical insight here that is worth highlighting:

3rd-party fraud is actually easier to fight effectively.

Why? Because fraudulent behavior patterns are distinctly different from legitimate user behavior. When someone is using a stolen identity, they behave differently than the real account owner would.

These differences create detectable patterns that separate good users from fraudsters with high accuracy.

1st-party fraud is trickier because the user's behavior often appears perfectly normal until the moment they decide not to pay.

And sometimes, that decision can happen even after a payment was made.

Different Approaches for Different Threats

As you can learn from the examples above, how you prepare and react to these two different threats is, unsurprisingly, different as well.

Here are the hallmarks of good fraud prevention for each fraud type (make sure you can tick most boxes):

For 3rd-Party Fraud:

KYC, identity & document verification
Device fingerprinting and IP intelligence
Velocity counters and network analysis
Behavioral biometrics
Identity intelligence
2FA/MFA

For 1st-Party Fraud:

Consortium data
Dynamic returns/refunds policy
Chargeback dispute management
Device fingerprinting (account sharing and promo abuse)

See? What works for one fraud type likely won't work for the other. And that's why it's so critical to know what you're actually facing.

The Bottom Line

The fraud prevention industry was built primarily around 3rd-party fraud detection. That's why so many teams struggle when facing 1st-party fraud–they're using tools designed for a completely different problem.

Before investing in another solution, make sure you've correctly identified what you're up against. The telltale signs are there if you know what to look for.

But here’s the thing: most businesses face both types simultaneously.

And the most successful fraud teams deploy targeted approaches for each, rather than trying to find a one-size-fits-all solution. It simply doesn't work.

What are you seeing more of in your business right now? 1st-party or 3rd-party patterns? Hit reply and let me know–I'm genuinely curious to hear what's happening in your world.

In the meantime, that’s all for this week.

See you next Saturday.

P.S. If you feel like you're running out of time and need some expert advice with getting your fraud strategy on track, here's how I can help you:

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Chen Zamir