Antifraud Systems
In this lecture we will look at the concept of antifraud. Everyone has heard of it, but not many understand what it is, and how to work around it.
“Why with seemingly the same parameters, some are able to place an order and others cannot?”
“Why with an identical sequence of your actions, with clean IPs, we get a lock of our accounts?”
“Why can’t we pay for some service even with a blank card, with our own money?”
All this happens due to a lack of understanding of how the payment system works, how antifraud works, what happens when you enter a card in a shop. This is due to the fact, you aren’t understanding the picture as a whole. It seems simple, but those methods that worked 2 years ago, they don’t work at all now.
There are also guys who seem to work successfully without delving into anti-fraud systems, but you need to dig a little deeper – what kind of stores do they operate? What kind of goods are in these stores? And it will become clear that these are small shops that didn’t have time to connect a decent anti-fraud system, or these are shops in small countries. Of course, such schemes can work, but they die very, very quickly, connecting to new anti-fraud systems or training the system using machine learning.
Let’s skip all the stages of searching for shops, registering in them, this is all part of the process, let’s look at the payment stage.
When entering the card number on the payment page, our payment does not go directly to the bank, but goes to a third-party service – antifraud service, which analyzes all the information that you provided about yourself, and this is not only those the data that you entered manually, such as address, telephone, email, card and everything else, the system also evaluates you according to those parameters that are not obvious but are unique for each user in the store.
If at this stage, the antifraud system doesn’t have any problem/s with us, or there are, but we haven’t scored high enough fraud points to immediately ban us, our payment goes to the next stage of verification – this is an antifraud of the Visa system itself , Mastercard, and others. At this stage, we may be asked for a 3DS (3D Secure). This is not a mandatory stage, but it exists and that’s why we must know about it.
Only after these checks does our payment reach the bank, where the bank, based on the results of checking the antifraud systems, sees that we are all good, deducts the money from our account, and the payment goes to the store.
Everyone is happy – the shop gets money, someone gets a product or service, but what happens behind the scenes of these checks? What do antifraud systems look at?
There are 2 types of anti-fraud systems – these are those which we can see what is being checked and closed, in which we will never know exactly what is being analyzed, we will look at the open infrastructure anti-fraud system, look at those parameters which can be analyzed by fraud
There are about 170 such main parameters and this is at the moment, before there were fewer, later there will be more
There is an antifraud system called ‘SEON’ (seon.io). I’ll be using it as an example in this lecture. Why? Simply because this is one of the powerful antifraud system that works with large companies such as Forex Club, Air France, After Pay, PokerStar, Home Credit and hundreds of others. These are sites which agreed to place their logo on the home page, most don’t do this for security reasons. This service allows you to look inside this system and do it for free (for now at least).
If you decide to register in their antifraud system, do not forget that you and your registration will also be analyzed, and you will not be able to register with the wrong email or even with a dirty Google account. Do it on a clean email.
After registration, we see this powerful tool from the inside, and what to see and what is important for this system – go to the “Scoring Engine tab”, and in the default rules, this will be enough to help us understand how their system works.
Example of rules which are include, “Timezone based on IP geolocation and user’s device does not match”, “Domain is a free provider. At least 2 online profiles were found. It was involved in at least a data breach”, “Phone country and IP country does not match”, “IP was found on 2 spam blacklists”.
Whether or not you have access, there’s a bunch of parameters that can be sorted by fraud points, or importance in their system. We’re able to see what is important, and also, what category this parameter belongs to.
The first parameter that can kill all pure thoughts and undertakings is if you use Tor to visit the site. You will receive an instant ban, even if you have a highly trusted email provider, a clean system, and other parameters.
The second most important parameter concerns your email – the use of disposable email will also negate all your further efforts.
The third important parameter, and immediately a big jump in points – this is the use of a proxy. If you were to get caught doing this, you’d get 20 points (not critical but you’ll be affected).
FORMATTED: ID RULE NAME | SCORE | CATEGORY (Higher score is worse)
P103 | Customer is using TOR | 95 | IP Rules
E100 | Domain is disposable | 80 | Email Rules
P105 | Customer is using a Web proxy | 20 | IP Rules
E120 | Domain is not registered | 20 | Email Rules
HC117 | Suspicious browser profile – Bots and automation | 12 | Other Rules
PH105 | Phone is disposable | 10 | Phone Rules
P112 | Customer is using public proxy | 10 | IP Rules
HC107 | Customer is from Nordic country and using VPN | 10 | Other Rules
P106 | Customer is using a data center ISP | 10 | IP Rules
E102 | Domain is custom and was registered less than 1 month ago. No online profiles were found. It was not involved in a data breach | 10 | Email Rules
E114 | Domain is a free provider. No online profiles were found. It was not involved in a data breach | 10 | Email Rules
HC125 | Suspicious browser profile – Spoofing | 8 | Other Rules
HC124 | Browser version age is greater or equal to 5 years | 8 | Other Rules
PH103 | Phone is not possible | 8 | Phone Rules
The remaining parameters are less important, and the average parameters for the successful passage of this system are about 50 points. The less, the better.
Note, these are all default parameters, just a possibility of what to look out for.
Now, what can the anti-fraud system find out about us if desired, what parameters are available for analysis? This question will be answered by the “Custom Rules” tab, where you can see all possible parameters.
We create a new rule for evaluation and see that only this system can see about 470 parameters about us, each of which can be configured and each of which can be assigned its own value of fraud points
Once again, there are 470 parameters which the system sees about you. Most, of course, will not be analyzed by the default system, but if necessary, the rules will be configured so that they can see what they need to in each specific case.
Let’s go over the main ones
Email – registered social networks include Facebook, LinkedIn, GitHub, Vimeo, Flickr, Foursquare, LastFM, Myspace, Pinterest, Skype, Yahoo, Twitter, Apple, Yahoo, Ebay, Gravatar, Airbnb, and dozens of others. You can check not only whether your account is registered or not , but also filled in fields – last name, first name, biography and the rest. This parameter is one of the most important, since there are no living people who do not have this, which means that either this is a newly created account of a living person or it was created for some purpose – but in any case, this is out of the ordinary
Phone number – Skype, Viber, Whatsapp, and the same social networks as when analyzing email, plus the validity of the phone, operator, country, etc.
Please note these are the most important parameters that are currently used in most antifraud systems.
When you use Google Voice, you should immediately understand that it’s all visible, and you’ve already collected extra fraud points.
Google Voice – purchased somewhere in a bot, will not have registrations in social networks, will not have Viber, WhatsApp, and other services.
I would also like to draw attention to fake numbers. When you indicate fake numbers or, even worse, fictitious ones, you must understand that the numbers in a phone number are just the tip of the iceberg, which contains tons of information.
The phone verification system works on the “Get Contact” principle, it can check how the owner is recorded in the phone books of people with said account name.
IP – cleanliness, blacklists, proxies, open ports, DNS and everything else related to your IP. This is the third important parameter that you need to work with; using a clean IP is +20% to your success, yet a good handful of you overlook this step.
People with experience remember the time when the cleanliness of the IP was assessed by the ability to register for an email on Google, without the requirement of a phone number. But now you understand that clean, new email, is a bright spot about you in any anti-fraud system, and a clean IP is always good.
Also, this SEO system has machine learning, which will draw conclusions based on the history of work, even if something is not configured for it initially.
I drew your attention to this example of one antifraud system, ‘SEON’, for a reason – it’s renown, works with many services and shops, and if you have an identity (victim’s email, phone, system, IP) that was exposed, then you can guess that with the same introductory information, you will get a turnaround in any other service (or shop) that is served by the current antifraud system.
This is something that concerns exactly one system. But the parameters for assessing personality are very similar from one system to another. If your email does not have registrations, and is shown as a new region, it will be like this in all antifraud systems. Even though the systems have different databases, and the dossier on your identity will be in one system; when you enter it into a shop, it is serviced in another antifraud system, it will pull up almost all the parameters and data that is stored on your personality in SEO.
Lastly, A.F. systems are constantly growing and developing, and in order to be successful in this field, we must constantly monitor these changes and adapt to them. Research, gain knowledge, try and try again, and you will be among the rich/ successful, remember this.
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Fraud and Carding Must Know: Antifraud Systems [Part 2 – FREE SAUCE INCLUDED]
Ever wondered how you could have what is arguably the most flawless setup (high balance card, proper BIN, clean same city socks) imaginable on the cyberspace and still not get a good hit while carding something online? Ever wondered why Stripe keeps refusing your ‘high-balance’ card even for a low amount? Or why even a cheap order on Shopify gets cancelled due to ‘unforeseen circumstances’? 🤔
🛑 The answer is quite simple: AI Anti-Fraud Systems. And today we’re tackling this concept that is foreign to noobs, but seasoned carders are all too familiar with. Understanding it essentially guarantees a shipment notification in your email, and not an order cancellation notice.
🔍 What are modern anti-fraud systems?
Antifraud systems are essentially gates and hoops you have to bypass (besides the bank) in order for your order to get successfully processed. The systems decide whether to force you to go through 3DS, or not. The companies who run these include, but not limited to:
Stripe Radar
Signifyd
Riskified
Accertify
Forter
SEON
💡 Who came up with this shit?
While large websites like Amazon, Walmart, etc roll their own, corporate assholes figured out that there’s money to be made in stopping script kiddies from copy pasting free CCs from Telegram and getting their iPhone 15 Pro Maxes next day. Somehow they had the brilliant idea of offering fraud prevention as a service (SaaS). Their pitch to business owners was simple: You install our javascript on your website and we watch over everyone who’s trying to make an order from your store, we get to decide whether an order is approved or not. All orders we process we take a % cut. If we approve an order and it turns out to be fraudulent and the cardholder charges back, we compensate you 100% for your loss.
This is probably one of the most profitable venture ever created, just a little bit below a casino. Think about it: Not only are there statistically a minuscule percentage of fraudulent orders compared to legitimate ones, an overwhelming majority of carders doing fraud are—lets admit it—noobs and are very easy to detect. If you’re one, then keep reading as this is perfect for you.
🔒 But what makes them different?
Two words: data & AI. Modern antifraud systems became much more effective since they are equipped with more data—since hundreds/thousands of businesses use them, they are effectively collecting order data from thousands of shopping websites—and this in turn results in far more superior AI decision making. These systems asses your risk in a point-system where each hit or risky aspect of your purchase adds to your overall ‘risk score’. Their software are actually much easier to deploy, giving the business owner the peace of mind that there will be minimal chargebacks on their shopping site, and if ever there were, they are covered and compensated by the antifraud’s guarantee system.
😎 At the heart of this is the tradeoff between true positives and false positives. An antifraud system that is too strict will block MOST of the fraudulent orders while at the same time blocking a huge portion of false positives (legitimate purchases). This is bad for the shop-owner, as often times their loss from blocked legitimate purchases are higher than the actual possibility of loss from fraudulent purchases; not to mention it damages their reputation whenever a legitimate customer attempts to purchase and is suddenly blocked without doing anything wrong. The job of the fraud detection companies is to fine-tune their AI and balance true positives to false positives.
And they need to make it as seamless as possible. A shopping owner nowadays wouldn’t have to hassle themselves in deciding if they should ship a shiny new PS5 to Brandon from Portland; the AI had already decided to reject the transaction because it has data that someone from the same delivery address charged-back a dildo purchase from six months ago. And if you’re shipping to a freight forwarder, good luck, because there are probably countless dildos already fraudulently purchased to that warehouse’s address. 😅
💻 Ok I get it, I’m fucked, how can I be not fucked?
“Give me six hours to chop down a tree and I will spend the first four sharpening the knife.”
— Abraham Lincoln
Before you can start mowing down the shopping sites with your 517805s and 518698s you first need to understand what data during shopping is taken, how it is processed, and how huge of a factor each data plays in the AI’s decisions making process.
🌐 Common misconception regarding your IP address.
Back in the days you just needed choose a proxy in the same city/state as the billing of the card and you’re good to go. Go make a quick search on the forums for guides, and that’s pretty much what everyone tells you: same IP city or state of the billing, and voila, your order goes from processing to preparing for shipment. That couldn’t bw further from truth nowadays. While proximity of your IP is a factor to the system’s decision making, it isn’t the ONLY factor, nor is it the most important one.
The opposite is also true: if same city/state to cardholder’s billing is the most important deciding factor, why is it that your relatives, who orders online from anywhere else in the country still get their orders processed? Why is it that your Uncle, who’s taking a vacation thousands of miles away from his billing address is still having no troubles getting his legitimate orders through?
📊 IP quality > IP proximity. When deciding regarding your IP address, IP quality is a far more important factor than proximity. You could be using an IP on the same street as the billing details of your card, but if it was ran over a thousand times already by other cards your order will simply not push through.
Some websites that offer IP health checks include:
Scamalytics: https://scamalytics.com/ip
Seon: (this is good if you’re trying to hit a site that uses SEON to block fraud, as you get a picture of how the service looks at your IP) https://seon.io/resources/ip-fraud-score/
IPscore.IO: https://ipscore.io/
These help with assessing your IP’s health, but it doesn’t paint the entire picture. Consider the recent IP address somebody used that scored extremely low on all these services. It passed through these tests with flying colors yet it failed Stripe’s Radar for mere $45 purchase.
🔍Why? Let’s take a look at Stripe’s AI decision-making
If you look at an example of Stripe Fraud online, you’ll see ‘Previous disputes from IP’, ‘Authorization rate’, and ‘Number of cards previously associated with’?
While the IP health services sees the IP as clean, it’s obvious it has been ran over hundreds of times in the past hence the transaction failed.
💡But if I had no way of reliably knowing if the IP is clean or not, how can I pick which one?
You can increase your chance tremendously by combining the data you have: first the cleanliness of the IP on these tools, and the source you’re getting the IPs from. Making sure your IPs are actually squeaky clean is also a multi-step process:
1. First thing you need to make sure is that you’re getting either residential IPs, or 4G LTE IPs.
Some ISPs offer IP blocks to companies who host proxies on their own servers, while these proxies are FAST, they are considered ‘RISKY’ by fraud AIs as there’s really a low chance an actual consumer will be using an IP from a company server. Steer clear of them and just use residential IP proxies.
2. Make sure the Socks/Proxy provider doesn’t primarily cater to carders/fraud audience
One extra tip is to go through each provider & know who they are primarily catering to. A company that is primarily offering its proxies to fraudsters give you a lower chance of success as its pool is most likely tainted by its own customers.
3. The bigger the provider pool, the better
A proxy platform that offers a huge pool, sometimes upwards of millions, tend to increase your chances of success simply because any IP you get will have a lower chance of having been used in the past by another fraudster. This effectively bypasses the pitfalls that happened to the Stripe transaction above.
🔥MY EXTRA SECRET SAUCE REGARDING IPs FOR FREE🔥
If you want the best of the best, cleanest IP address you can find, then get an Apple device and use their iCloud Private Relay VPN:
Not only does it help you with privacy, Antifraud checker systems are forced to give a low fraud rating to IPs in Apple’s pool, simply because they are shared by all Apple users who uses Safari, and punishing any IP inside the pool will cause legitimate Apple device customers who uses the services to get hit too, causing legitimate purchases to get cancelled. Abuse this while Apple is forcing these privacy-breaking companies’ hands.
https://news.ycombinator.com/item?id=27760391
🕵️♂️ Now, shifting gears from picking the right IPs, let’s talk about another crucial detail : your browser fingerprint. It’s like your browser’s unique ID card on the internet and it’s as vital as choosing the right IP.
Picture this: you’ve nailed the IP game, but forget about your browser fingerprint, and you might as well be wearing a neon ‘fraudster’ sign online.
Surprisingly, a lot of newcomers in the carding scene fumble on this step, and that’s where things can go south real quick.
🔍What is a browser fingerprint?
Your browser fingerprint is like your browser’s secret recipe – a unique mix that makes it stand out online. When you visit a website, your browser spills the beans, sharing info like its version, type, operating system, screen resolution, plugins, fonts, time zone, language preferences – the works. And thanks to JavaScript, websites can even unearth more details about your browser’s capabilities and device features. So, as you move through the internet, your browser unwittingly reveals its details—even your fucking battery percentage!—basically broadcasting your digital identity to the websites’ servers and antifraud mechanisms.
Companies collect millions of these fingerprints, as left by their users. By piecing together these fingerprints, they create a coherent picture of visitors without them even realizing it. It’s like assembling a puzzle of online habits, preferences, and activities to get to know users on a more detailed level. By analyzing patterns and details, these systems can effectively assess whether a person has engaged in fraud in the past, linking their current browser & sessions with previous order sessions. Inversely, they can piece together that your current session does not fall in line with the cardholder’s sessions, ultimately resulting in declined/cancelled orders.
So, here’s the deal with browser fingerprints: some folks think they should be like the James Bond of the internet – all unique and untraceable. But here’s the twist – that’s not the right move with fingerprints. Unlike IP addresses where you’re after the squeakiest clean, with browser fingerprints, you’re aiming for the dirtiest, most common fingerprint possible, as this allows you to blend in the crowd like any normal person would!
🌐Antidetect Browsers
Enter antidetect browsers – these are like your secret weapon. They’re special browsers designed to make you blend in even more and throw off those pesky JavaScript trackers by antifraud systems. They let you tweak things like your user agent, disable browser plugins, and mess with cookie settings. The goal? To make your online fingerprint look so generic that it’s hard to pick you out from the crowd. Plus, they help prevent trackers from linking your different online sessions on the same device. Some of these include:
CheBrowser
Linken Sphere
Multilogin
Kameleo
GoLogin
Incogniton
These browsers are primarily used by internet marketers and botters who snag the next Nike release, and for a monthly price they pretty much do all the heavy lifting in making sure each session is different from the other, while at the same time maintaining a ‘genericness’ to it that makes you mix perfectly with the crowd.
Each browsers have their strengths and weaknesses, so try as many as you can and decide which works perfectly for your workflow. Just make sure you remember what I said: your goal with these browsers is to be as ‘non-unique’ as possible!
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Here’s another free sauce that will surely help your workflow. Did you know most Safari browsers on iOS have similar fingerprints? And here’s the kicker – even iOS apps can’t track your device ‘hardware id’ between resets.
So reset your iPhone, install the Surge App on the App Store, connect to your proxy and change your timezone: bam! you have the most perfect piece of anti detect software there is. There’s a reason why expert carders showing off their orders being shipped all take screenshots with their iPhones: it is simply the best tool to get the job done.
🛒Browsing Patterns
Another huge part of the order flow that raises a red flag and increases your ‘risk score’ to the eyes of AI systems is your browsing pattern. Think about it: what kind of animal of a person would go to a shopping site, pick an expensive item within a span of a couple of seconds, checkout by pasting their credit card info, and keep refreshing the order status page every couple of minutes? That’s right, a CARDER.
Humans are creatures of habit, and these antifraud companies know this: that’s why their systems are geared towards statistically comparing patterns of legitimate buyers to fraudsters, and using the recognized pattern to make decisions whether to approve orders or not. This is all done through the magic of modern Javascript, where all your cursor movements, clicks, scrolls, keystrokes, pastes, etc are recorded to perfection. Seriously check out the console for how many data goes to Stripe upon loading the page.
These data (117 requests) were gathered within a couple of seconds of loading the page. A single click creates a request to Stripe’s Radar servers letting them know that you clicked here and there. Now imagine this sort of thing being embedded in ALL of the pages in the shopping website. Yes, clicking the first expensive thing you see and going through the checkout page like a madman with a bunch of cards will surely get your session fucked.
🔄 So how do I bypass this? Pretend like an 80-year old lady from Arkansas?
Perhaps you could, most antifraud pattern matching systems—except Amazon, because Amazon is retarded—in my experience gives enough leeway for a purchaser even if the activity patterns don’t really match. Spend a couple of minutes here and there, pretend you’re having second-thoughts about your purchase, be finicky, scroll and check other products, just wander around a bit before going for the kill.
Again, always think about the diagram I showed you earlier: these systems want to be strict and catch noob carders, but they DONT WANT TO BE TOO STRICT and block legitimate purchases and hurt their client’s bottomline.
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(Don’t worry, this doesn’t require Apple devices anymore.) 😅
One extra-spicy method that we’ve been using all these years in order to bypass fraud checks, and this is especially effective for digital items is split in three steps:
1. Make sure the website accepts signup/checkout with ANY email without any form of email verification. If you’re purchasing a gift card, make sure that the gift card gets sent to an email of your choosing, or stored in the order history page that is completely accessible to you without OTP being sent to the person who ordered.
2. Checkout using the cardholder’s own email. Weird right? Well when you use the cardholder’s email, which the cardholder has most likely have a positive history of legitimate orders from, you’re pretty much guaranteeing the order will go through!
3. Use email spam services and spam the email right after the purchase was done. This guarantees the email from the shopping website doesn’t get read by the account holder, or the gift cards/digital goodies you purchased gets to him. There are plenty of email spam services out there.
🔥Another Spicy Sauce is using Ad Blockers like uBlock Origin🔥
Remember the concept of blending in the crowd? This also applies to shopping patterns: AdBlockers block scripts that track a users movement in the site, effectively making the AI blind to any of your actions; while you may think this will make the AI suspicious and outright block you it will surely won’t because millions of people use ad-blocks, and by using one you’re effectively blending in with millions of people who’s activity inside the shop the AI cannot track. This works so good on some site I used to actually charge people to help them order stuff while using this. And now I’m giving it to you for free.
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Fraud and Carding Must Know: Antifraud Systems [FINAL PART – FREE SAUCE INCLUDED]
🏠Address
Now, let’s talk about the last leg of our journey – the delivery address. Honestly, it’s a critical part of the whole order thing and can either make it or break it. Some big-shot shopping sites like Amazon and Walmart might cut you some slack when it comes to the delivery address, but others, like Forter, Signifyd, Riskified, play hardball and shut down transactions to addresses with a history of fraudulent orders.
Now, you could try these residential drop services floating around the forums and Telegram, but they’re a bit like playing roulette – unpredictable and often risky. They might even rat you out, and worst-case scenario, your stuff could get swiped. Another option is hopping on services like Reship, Shipito, etc., but let’s be real – those addresses have been raped by molested by carders since time immemorial, not to mention they tend to suddenly require complicated KYC processes once they catch a whiff of carded items. So how do we reliably deal with this? Enter my free sauce for you miscreants:
🔥Free Sauce, Address Jigging🔥
Address jigging, primarily used by sneaker botters, is in my experience, an effective way of bypassing address checks by AI system. Remember we’re bypassing AI systems, they might be smart but they’re not infallible, and one prominent weakness of these AI systems is they have no imagination, and this is the part we exploit to get our orders through. 🎯
Address jigging involves intentionally changing your delivery address just enough for it to be different, but not too much for your items to not get delivered.
1. 4 Letter Jig: Add four random letters in front of your address. The AI might see it differently, but your UPS driver won’t notice. Profit.
2. Abbreviation Game: Swap street or road with abbreviations. It may not fool strict sites, but it works from time to time.
3. Apartment/Floor Twist: If you’re not in an apartment, throw in “APT” to signal a change to the antifraud system. The courier won’t care. Gold.
4. On/At Jig: Stick “on” or “at” to your street number. Messes with the AI systems, and you’re good to go.
📚Understand your enemy
Congratulations, you’ve gotten this far, I wish you’ve taken all I’ve laid out here to heart, but there’s a crucial missing piece of the puzzle you must understand that should premise all your carding sessions: you must understand your enemy. Each website is different, they have different checkout flows, different antifraud systems, and different rigidity in how they employ their antifraud. It’s not just about success; it’s about consistent success—and knowing your enemy fully-well guarantees this.
🌐 One way you can go about this is by checking the HTTP console and looking for clues as to what fraud system the website employs:
For example, Farfetch uses Riskified.
This can be done by Right Clicking —> Inspect Element —> Network —> Look Under ‘Name’ For “client_infos” —> Look Under Header and You’ll See “Request URL: https://c.riskifield.com/v2/client_infos”
🔗You can find the guide on how fraud score is calculated by Riskified here:
https://support.riskified.com/hc/en-us/articles/360012160393-API-Integration-Guide-
🔗You can also sign-up to these services, and test your fingerprint, one good example of this is SEON which allows non-KYC sign ups, though this is only effective if the site you’re trying to hit uses SEON:
https://seon.io/try-for-free/
🔍 Another one is Stripe, which you can sign up and use their Radar service, get a couple orders through and look at how they assess your sessions:
Once you’ve signed up for these sites you can use your API keys to approve ‘pretend orders’ as 3DS validated making sure the system trusts you enough so that when you go for the kill you get away with it flawlessly.
🤝Understood. I’ve increased my fraud IQ, but why are you giving these away for free?
I think we should all work together for the improvement of the industry as a whole and not look at each other as competitors in the space. The more we share knowledge with each other, the better we all get, the better money there is to be made for each of us.
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Cheers
