47 min
When you get hit by a fraud attack, the second question should be what kind of fraud am I actually dealing with. First party fraud vs third party fraud isn’t just a technical distinction. It fundamentally changes how you respond and what tools you deploy. I’m constantly amazed by how many teams either can’t tell the difference or don’t think it matters.
This episode is about fixing that.
What you’ll hear in this episode:
- The core definitions of first party fraud and third party fraud
- The specific signals that separate the two in practice
- Two real, costly examples of teams deploying the wrong tools
- Why third party fraud may be easier to detect than most people assume and why first party fraud may be harder
- The specific tool stack each fraud type requires
- Why most fraud prevention tooling was built around third party fraud by default
You should listen to this episode if you:
- Are trying to diagnose a rising fraud rate and aren't sure whether you're facing first party or third party fraud
- Have invested in KYC identity verification or device fingerprinting fraud tools without seeing the results you expected
- Are responsible for account takeover detection and want a clearer sense of which signals actually matter
- Need to make the case internally for fraud prevention ROI by matching the right tool to the right fraud type
- Are dealing with money mule detection or collusion fraud cases that don't cleanly fit either category
Episode notes & key takeaways
The core distinction between first party fraud vs third party fraud
Third party fraud means someone stole a payment method or an identity, built a synthetic identity fraud case from scratch, and are pretending to be someone they are not. First party fraud means a real customer, using their real identity, simply has no intention of honoring what they agreed to. Whether that shows up as chargeback fraud, returns fraud, or promo abuse. ON paper that sounds simple, In practice the lines blur fast. Getting the distinction wrong is one of the most expensive mistakes a fraud team can make.
What to look for
First party fraud tends to show up as cases with no connection across devices or IP networks. There are early chargebacks within the first week of a transaction, an absence of typical fraud signals like geo mismatches, and established account history with no account takeover detection flags at all. Third party fraud looks almost the opposite. There are strong shared connections across devices and IP networks, suspiciously clean but brand new identity assets, abnormal patterns like near-identical email conventions across supposedly different people, and geographic mismatches paired with unusually high issuer decline rates.
Two expensive examples
A fintech was dealing with clear first party fraud. They responded by investing heavily in stronger KYC identity verification, multifactor authentication, and device fingerprinting. Their fraud rate kept climbing. Those tools verify identity, something first party fraudsters already have. On the flip side, a SaaS platform was facing classic third party fraud. They focused energy on transaction history analysis, credit risk scoring, and account tenure rules. All while missing that 80% of its fraud was coming from just three IP networks the whole time.
The easier one to catch
Fraudulent behavior built on a stolen or synthetic identity looks distinctly different from how a real account owner behaves, which creates detectable patterns with high accuracy. First party fraud is harder because the person’s behavior looks completely normal right up until the moment they decide not to pay, and sometimes that decision only happens after the payment has already gone through. This makes it nearly impossible to catch at the point of transaction. Not every case fits cleanly into one category. Money mule detection and collusion fraud are the clearest examples where first party and third party fraud overlap and are genuinely hard to separate.
The right toolkit
The fraud prevention industry was built primarily around third party fraud detection. That’s a bigger part of why so many teams struggle the first time they face a serious first party fraud problem. They may be reaching for tools built to solve a different problem entirely. Most businesses deal with both types at once. The teams that succeed build targeted approaches for each rather than hoping one tool stack covers everything.
Final takeaway
The single costliest mistake I see fraud teams make isn't choosing a bad tool. It's choosing the right tool for the wrong problem. First party fraud vs third party fraud isn't an academic distinction, it's the first diagnostic question that should shape everything that follows, which signals you look for, which tools you deploy, and ultimately whether your fraud prevention spend actually pays off. Most businesses are facing both at once, which means the real skill isn't picking one approach. It's knowing which one to apply, and when.
Resources & links
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Connect with Chen Zamir | LinkedIn
Host of The Saturday Fraud Strategist
Helping fintechs build smarter fraud defenses
Co-author of “The Fraud Fighter’s AI Playbook”
Episode transcript
Chen Zamir
00:06
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 first party fraud and third 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 teams 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 so critical to your business.
Chen Zamir
00:44
Let's start with the basics. Third party fraud happens when someone steals payment methods or identities or creates completely new but fake synthetic identities with the intention to defraud your business. The fraudster pretends to be someone else entirely. First party fraud happens when real customers use their real 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, promo abuse, 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.
Chen Zamir
01:26
After years of working with fraud teams across dozens of fintechs, I've noticed distinct patterns that separate these fraud types. So, let's talk about what you should be looking for. Starting with firstparty fraud, fraud cases that are notably not connected by online assets, device IDs or IP networks. Early chargeback maturation, especially in the first week after transactions. Absence of traditional fraud signals like geo mismatches or bad links. Higher transaction velocity is also often the only suspicious signal and established account history with no ATO indicators. Now, to be clear, I don't mean that you need to see all of these signals in the same account to say it's first party fraud. These are just examples for what to look for when tagging the loss events. Now, let's compare it to third party fraud. Here, you want to look for strong connections between fraud cases like shared devices or IP networks, abnormal shared behavioral patterns, for example, identical email conventions across supposedly different people. So John Smith777@gmail.com, jane smith777@gmail.com and so on. Suspiciously new yet clean identity assets like emails and phone numbers. Geographic mismatches like a new foreign country IP addressing your US service and unusually high issuer decline rates. So it's pretty clear that 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. And by the way, just to get it out of the way, and as I mentioned a minute ago, in some fraud technologies, the lines get blurry. This is often the case with money mules, money laundering, and collusion fraud. So, unfortunately, it's not so easy sometimes to tell the difference.
Chen Zamir
03:20
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. And it happens so frequently that I'm starting to think it's the rule, not the exception. And it cost these companies millions. Why? Because fraud tools, like all tools, are built to solve 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 first party fraud, early chargebacks in established accounts decided to invest heavily in advanced KYC verification, multifactor authentication, and device fingerprinting upgrades. Unsurprisingly, their fraud rates kept climbing because these tools verify identity, something first party fraudsters already have legitimately. Then there's the opposite scenario, a SAS platform hit by classic third party fraud. Connected devices, new email accounts focused on transaction history analysis, credit risk scoring, and 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. Third 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. First party fraud, however, is trickier because the user's behavior often appears perfectly normal until the moment they decide not to pay. And sometimes that decision is made after the payment was made, which makes it nearly impossible to detect at the time of payment. So, what do you do?
Chen Zamir
05:18
As you can learn from the examples I just shared, 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. And you want to make sure that you can tick most boxes. Let's start with uh third party fraud. You want to look at KYC, identity and document verification, device fingerprinting and IP intelligence, velocity counters and network analysis, behavioral biometrics, identity intelligence like email, phone, etc. Two factor authentication or multifactor authentication. For firstparty fraud, you want to look at consortium data, dynamic returns and refunds policy, chargeback dispute management, and device fingerprinting, which is mainly relevant for account sharing and promo use. 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.
Chen Zamir
06:21
When you look at its roots, you realize that the fraud prevention industry was built primarily around third-party fraud detection. That's why so many teams struggle when facing first party fraud. They're using tools designed for a completely different problem. So before investing in another solution, make sure you've correctly identified what you're up against. And 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 because it's simply doesn't work. Anyway, that's all for today and I'll see you next Saturday.
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