The checker's methodology from a technical perspective: what requests are sent to banking systems and how fraud detectors respond

[Professor]

Abstract: Analysis of the mechanisms used by bank protection systems (anti-fraud engines) to detect automated verification requests from card checkers. Concepts such as velocity checking, bin velocity, card cycling, and how algorithms distinguish legitimate users from bots are discussed.

Introduction: An Invisible Duel in Milliseconds​

In the digital world, a duel unseen by the average user rages on every second. On one side are automated systems attempting to verify stolen data for "liveness." On the other are intelligent bank guards tasked with recognizing and rejecting these checks without interfering with legitimate transactions. Understanding this duel is key to understanding how sophisticated and intelligent financial security has become.

This article is not a guide, but a technical excursion into the core of modern payment cybersecurity. We will examine in detail the mechanism of so-called "card checkers" and, more importantly, the powerful systems that counter them, making mass automated carding virtually impossible. This is the story of how banks learned to distinguish humans from robots at the very beginning of their journey.

Chapter 1. What is a "checker" on a technical level?​

A card checker isn't a single program, but rather an automated approach. Its purpose is to determine, as quickly and cost-effectively as possible, whether a card is active (if the card number, expiration date, and CVV are valid) and whether its debit limit is available. This is accomplished by simulating a legitimate micropurchase or an authorization request.

Technically, a card checker is a script or bot that:

1. Takes as input a file (dump) with card data (PAN, Expiry Date, CVV, sometimes name).
2. Generates an HTTPS request for each entry to the online store's payment gateway or to the banking system's API.
3. Analyzes the answer and marks the card as "live" or "dead".

Chapter 2. Anatomy of a Request: How a Checker Knocks on a Bank's Door​

To understand protection, you need to know what an attack looks like. A typical request from a checker to a payment gateway simulates a payment attempt. Let's break down its key components.

1. Card Data:

• PAN (number), Expiry (expiration date), CVV/CVC.
• Sometimes BIN is used for initial filtering before sending.

2. Transaction Data:

• Amount: The minimum possible. Often this is $0.01, $0.10, $1.00, or a symbolic amount in the local currency. The goal is to avoid attracting the owner's attention with an SMS notification about a large charge.
• Currency: Corresponds to the country of the card issuer (by BIN).
• Merchant (seller):The checker is often aimed at specific businesses:
  • Charitable foundations: Have a low threshold for refusing transactions.
  • Digital products (activation keys, PIN codes): Instant delivery, no logistics required.
  • Subscription services with a $1 trial period.

3. Technical parameters of the request (Headers & Fingerprint):

• User-Agent: Can be standard or randomized, but often indicates automation.
• IP address:One of the most critical parameters. Checkers use:
  • Public proxies and SOCKS5 proxies.
  • Botnets of infected devices (residential proxies resource).
  • Cloud servers (AWS, DigitalOcean), the IP pools of which are known to banks.
• Timestamps: Requests are sent at minimal, programmatically maintained intervals (e.g., exactly 200 ms), which is not typical for humans.

4. The system response that the checker analyzes:

• "Success" (Authorized): The card is live, there is a limit. Valuable item.
• Declined: The card may be valid but blocked, without a limit, or requiring additional authentication (3-D Secure). Often flagged for manual verification.
• "Gateway Reject": Often indicates that primary fraud filter rules have been triggered (incorrect CVV, BIN and IP country mismatch). For the checker, this is garbage.
• Timeout or connection error: Unclear result.

Chapter 3. Defense Arsenal: How Fraud Detectors Identify Checkers​

Banks and payment gateways are not being idle. Their Fraud Detection Systems (FDS) are built on multi-layered analysis. Here are the key techniques used in real time:

1. Velocity Checks – the main enemy of automation.
This is an analysis of the frequency of events using various keywords.

• IP address velocity: 100 requests from one IP address per minute is a clear signal. No human could enter 100 different card numbers that quickly.
• BIN Velocity: If a single source sends a flood of requests for cards from a single bank (or a single BIN range), this is called a BIN Attack. The system may temporarily block all authorizations for this BIN from suspicious sources.
• Velocity by card number: Multiple attempts with different CVVs to the same card in a short period of time is a sign of CVV brute-force attacks.
• Card Cycling (Carding): Sequential card queries where only the last 4-6 digits of the PAN change (e.g., ...1111, ...1112, ...1113). Algorithms easily detect such arithmetic progressions.

2. Device Fingerprinting and Behavior Analysis.
The system collects hundreds of parameters that form a unique "portrait" of the request source:

• HTTP Headers: Sequence, presence of non-standard fields.
• JavaScript engine properties (if the request is made through a browser in emulation): fonts, screen resolution, time zone, WebGL/Canvas support. In a bot, these parameters are often uniform or absent.
• Real-time behavioral biometrics: For legitimate purchases, mouse movement, typing speed, and keystroke patterns can be analyzed. A bot doesn't have this feature.

3. Reputation analysis of IP addresses and networks.
There are dynamic IP blacklists and reputation systems:

• IPs from data centers (AWS, Google Cloud, Azure) receive an increased risk score by default for operations similar to carding.
• Public proxies and TOR exit nodes are often blocked for sensitive financial transactions.
• The system evaluates whether this is a new IP for a given user (if they have already been authenticated) and how well their geolocation matches history.

4. Semantic analysis of transaction data.

• BIN and IP mismatch: Request from Nigeria for a Russian regional bank card.
• Shopping Cart Abnormality: 100 attempts to purchase the same digital item for 1 cent.
• No browser session: A legitimate user typically browses the website and adds an item to the cart. The checker immediately sends a POST request to the payment endpoint.

5. Machine learning and anomaly detection models.
Modern FDSs not only check rules but also learn.

• The models analyze thousands of features and identify complex, non-obvious patterns characteristic of attacks.
• They may notice that the requests, although coming from different IP addresses (botnet), have the same “handwriting” in the headers or time intervals.
• The system is constantly updated, adapting to new tactics.

Chapter 4. Escalating Defenses: What Happens After Detection?​

When the system assigns a high risk score to a transaction, the scenarios unfold as follows:

1. Hard Decline: The transaction is blocked, and the request doesn't even reach the issuing bank. The client is shown a standard error message.
2. Additional authentication request: 3-D Secure activation (redirect to the bank's page). For an automated checker, this is an insurmountable obstacle.
3. Targeted blocking: It is not the card that is blocked, but the attack source (IP address, range, digital fingerprint) at the gateway level.
4. Silent Monitoring (Honeypot): Sometimes the system is configured to give false positives to scammers, buying time to analyze their infrastructure and methods.
5. Issuing Bank Alert: The payment gateway can alert the bank that its BIN is under attack so that it can increase monitoring on its end.

Conclusion: Why the era of mass checkers is ending​

Technical analysis clearly shows that the confrontation between checkers and fraud detectors is a war in which protection has an overwhelming technological advantage.

• The checker operates with limited resources: IP addresses, request templates, speed.
• The fraud detector relies on context: the global reputation of networks, the behavioral history of millions of users, the power of machine learning, and connections to thousands of banks worldwide.

Massive, "bullet-force" carding, based on the automated checking of thousands of cards, is currently economically ineffective. Security systems have learned to detect and stop such attacks in their tracks, in the first milliseconds.

This doesn't mean fraud has disappeared. It has shifted toward targeted, highly skilled attacks (such as spear-phishing, mobile app attacks, and social engineering), where automation is less prevalent and the human factor is more prominent.

Understanding the methodology of checkers and fraud detectors doesn't provide a technical manual, but a deep respect for the complexity and reliability of modern financial systems. This knowledge allows us, as users, to feel more confident: behind every online purchase we make, there's not just a bank, but an entire army of algorithms waging an invisible yet highly effective battle to keep our funds safe.