Working with checker logs: how to read between the lines and not believe them

[Good Carder]

From a carder to carders. You launch the checker, see the green "Card is valid" checkmark, and think, "Great, the card is alive." Five minutes later, you hit it on the website and get a do_not_honor. You've lost $50 and time. Why? Because you don't know how to read the logs. The checker isn't magic. It's a set of raw data that needs to be interpreted. In this article, I'll show you how to read gateway responses, what lies behind the standard codes, how to distinguish a dead card from a problematic BIN, and how to build your own database of "live" and "dead" BINs based on the logs.

Part 1: Why a checker never provides a 100% guarantee​

A checker is a program that sends a request to a payment gateway (Stripe, Adyen, Braintree) with card data and analyzes the response. However, the checker doesn't see the balance, doesn't know whether the card is currently active, and can't predict whether a particular merchant will block it.

1.1. Main types of checkers and their limitations​

Checker type	What does it check?	Restrictions
Luhn-check	Checks whether the card number passes the Luna algorithm	Doesn't check the card's validity at all
Zero-auth check	Sends $0 authorization (Stripe SetupIntent)	Checks only the existence of the card, not the balance.
Micro-check	Conducts a real transaction of $0.50–1	Shows that the card is active and has a balance ≥ $0.50
Balance sheet	Trying to determine the exact balance on the card	Rarely works, often triggers fraudulent systems

Key rule: no checker can guarantee 100% that your card will be processed by the target merchant. The checker verifies your card on one gateway, and you hit it on another. Different gateways have different BIN blacklists and different scoring thresholds.

1.2. Checker vs. real hit – why results may differ​

Factor	Checker	Real hit
Gateway	Stripe (via SetupIntent)	It could be Stripe, Adyen, Braintree, or custom.
Sum	$0 or $0.50	$50–500
BIN filtering	Soft (often disabled for $0)	Hard (included for large amounts)
AVS	Not verified	Being checked
3DS	Not requested	May be requested
Fingerprint	Not analyzed	Under analysis

Conclusion: Even if the checker shows "alive," it doesn't guarantee success with the target merchant. Use the checker as a filter, not a final diagnosis.

Part 2. Gateway Response Analysis: What's Behind the Codes​

The checker returns a response code. Your task is to figure out what this code actually means.

2.1. Stripe Decline Codes — Explanation​

insufficient_funds

• What the checker says: “The card is alive, but the balance is low.”
• What's Real: The balance is less than the verification amount ($0.50 or $1).
• Action: Reduce the check amount to $1 and try again. If it drops by $1, the balance will be $0.

do_not_honor

• What the checker says: "The card is dead."
• What it really is: The card is on the bank’s stop list (stolen, blocked, expired).
• Action: Discard the card. It will not come back to life.

fraudulent

• What the checker says: "The card is blocked by antifraud."
• What's the reality: BIN is blacklisted by Stripe, or your IP/fingerprint is suspicious.
• Action: Check if the BIN is burned out. Change the proxy and fingerprint, try another card with the same BIN. If it also crashes with fraudulent, the BIN is dead.

generic_decline

• What the checker says: "General refusal."
• What's the reality: It could be anything: BIN is blacklisted, there's a problem with AVS, or the IP is suspicious.
• Action: Check the response timing. If <1 second, the BIN is dead. If >1 second, the card may be alive, but there are other issues (CVV, AVS).

authentication_required

• What the checker says: "3DS required."
• What's Real: The card requires confirmation via a bank app or SMS.
• Action: For non-3DS purposes, the card is useless. For low-value exemptions (<€30), try lowering the amount.

expired_card

• What the checker says: "The deadline has expired."
• What's the reality: The card has been physically reissued or the expiration date has passed.
• Action: Discard the card.

invalid_cvc

• What the checker says: "Incorrect CVV".
• What's the reality: Either the CVV doesn't match, or the seller gave the wrong CVV.
• Action: Check the CVV with the seller. If the CVV doesn't match, the card may be valid, but without the CVV, it's useless.

2.2. Adyen decline codes​

Code	What does the checker say?	Reality	Action
NOT_ENOUGH_BALANCE	"Insufficient funds"	Balance < check amount	Reduce the amount or put the card aside
DO_NOT_HONOR	"The card is dead"	The card is on the stop list	Throw it away
FRAUD	Antifraud blocking	BIN is blacklisted or in a bad environment	Change BIN or proxy
BLOCKED	"The card is blocked"	The issuer blocked the card	Throw it away
3D_SECURE_REQUIRED	"3DS required"	The card requires confirmation	Useless for non-3DS purposes

2.3. Braintree decline codes​

Code	Text	Reality	Action
2004	Insufficient Funds	Balance < check amount	Reduce the amount
2005	Do Not Honor	The card is on the stop list	Throw it away
2016	Fraud	BIN is blacklisted	Change BIN
2003	Expired Card	The deadline has expired	Throw it away
2000	Generic Decline	It could be anything.	Check the timing and BIN

Part 3. How to distinguish a dead card from a live one with a problematic BIN​

This is the main skill that separates the pro from the beginner.

3.1. Comparative test​

Algorithm:

1. Check your card using the checker. If it falls under "do_not_honor," the card is dead.
2. If you crashed with fraudulent or generic_decline, get another card with the same BIN.
3. If the second card also falls with fraudulent - BIN is dead.
4. If the second card passes, the problem was in the first card.

Diagnostic table:

Situation	Diagnosis	Action
1 card → do_not_honor	The card is dead	Throw away the card
3 cards with the same BIN → fraudulent	BIN is dead	Stop buying this BIN
1 card → insufficient_funds	The card is alive, but empty.	Check with a micro-check for $1
1 card → generic_decline (timing <1 sec)	BIN is dead	Change BIN
1 card → generic_decline (timing >2 sec)	The card is active, but the CVV/AVS do not match.	Check the data

3.2. The "Three Cards" Technique​

Never draw a BIN conclusion based on a single card. Check at least three cards with the same BIN.

• If all 3 fell from fraudulent → BIN is dead.
• If 2 fell, 1 passed → BIN is alive, but the cards were dead.
• If all 3 passed the checker → BIN is alive, you can buy.

3.3. A practical example from the logs​

BIN 414720:
- Card #1: `insufficient_funds` (timing: 2.3 sec)
- Card #2: `do_not_honor` (timing: 0.8 sec)
- Card #3: `generic_decline` (timing: 0.5 sec)

Diagnosis: BIN 414720 is alive (one card showed insufficient_funds). Cards #2 and #3 were dead. This BIN is worth buying, but look for more recent cards.

BIN 536425:
- Card #1: `fraudulent` (0.4 sec)
- Card #2: `fraudulent` (0.5 sec)
- Card #3: `fraudulent` (0.3 sec)

Diagnosis: BIN 536425 is dead. Don't buy cards from this BIN, even if they're cheap.

Part 4. Building Your Own Database of "Live" and "Dead" BINs​

Without your own database, you're blind. You'll buy dead BINs over and over again, losing money.

4.1. Database structure​

Create a table in Google Sheets or Excel with the following fields:

BIN	Bank	Type	Page	Non‑3DS	Status	Date of inspections	Success (Stripe)	Good luck (Adyen)	Success (Braintree)
414720	Chase	Credit	US	Yes	Alive	2026-07-15	25%	10%	30%
439305	Microsoft	Prepaid	US	No	Dead	2026-07-14	0%	0%	0%
536425	Mastercard	Credit	THAT	Yes	Alive	2026-07-13	15%	5%	20%

4.2. How to fill the database​

1. After each check, enter the data.
2. For each BIN, collect at least 10–20 checks.
3. Calculate the success percentage for each gateway separately.
4. If the success rate is >20%, BIN is alive.
5. If the success rate is <5%, BIN is dead.

4.3. Automation of data collection​

Write a simple Python script that:

1. Reads the BIN list.
2. Checks them via Stripe API (SetupIntent).
3. Saves results to CSV.

import stripe
import csv
import time

stripe.api_key = "sk_live_..."

bins = ["414720", "536425", "439305"]
results = []

for bin in bins:
# Генерируем фейковую карту с данным BIN
card_number = bin + "".join([str(random.randint(0,9)) for _ in range(10)])
try:
pm = stripe.PaymentMethod.create(
type="card",
card={
"number": card_number,
"exp_month": 12,
"exp_year": 2028,
"cvc": "123"
}
)
result = "live"
except stripe.error.CardError as e:
result = e.error.code
results.append({
"bin": bin,
"result": result,
"timestamp": time.time()
})
time.sleep(1)

with open("bin_check.csv", "w") as f:
writer = csv.DictWriter(f, fieldnames=["bin", "result", "timestamp"])
writer.writeheader()
writer.writerows(results)

4.4 How to use the database​

• Before purchasing a card, check the BIN in your database.
• If a BIN is dead, don't buy cards from that BIN.
• If BIN is alive but has low success rates (<10%), buy only cheap cards and test them.
• If BIN is alive and has a high success rate (>20%), buy in bulk.

Part 5. Errors when working with logs and how to fix them​

Mistake 1: Ignoring timing​

Symptom: You see generic_decline and think the BIN is dead. In fact, the timing is 2.5 seconds — the card is alive, but the CVV doesn't match.
Fix: Always look at the response time. <1 second — the BIN is dead. >1 second — the card is alive, the problem is in the data.

Mistake 2: Trusting only one gateway​

Symptom: You only check your card through Stripe and get a fraudulent message. You think the BIN is dead. In fact, Stripe simply blocks the BIN, and Adyen lets it through.
Solution: Check your cards through two or three different gateways. If Stripe cuts your payment, try Adyen or Braintree.

Mistake 3: Not maintaining a database​

Symptom: You checked a BIN a month ago and it was still active. Today you buy cards from that BIN and they're all dead. You don't know when it died.
Solution: Maintain a database and regularly update the BIN status. If a BIN hasn't been checked for more than two weeks, check it again.

Mistake 4: Confusing a dead card with a dead BIN​

Symptom: One card dropped with do_not_honor. You assume the BIN is dead and stop buying it.
Solution: Check at least three cards of the same BIN. If only one dropped, the problem is with the card, not the BIN.

Part 6. Checklist for working with checker logs​

• Check the timing: <1 sec → BIN is dead; >1 sec → card is alive.
• Check the decline code: insufficient_funds → alive but empty; do_not_honor → dead; fraudulent → BIN or Wednesday.
• Check at least 3 cards of the same BIN: if they all fall with fraudulent → BIN is dead.
• Check the card through 2-3 gateways: Stripe, Adyen, Braintree.
• Maintain a BIN database: record status, success rate, and verification date.
• Update your database every 2 weeks: BINs can die and come back to life.
• Don't trust the checker 100%: always make a micro-check of $0.50–1 before the main hit.

Summary​

A checker isn't a magic wand. It's a tool that provides raw data. Your job is to interpret it. Timing, decline code, number of checks, gateway — all are important. Learn to read between the lines, and you'll stop losing money on dead cards.

Maintain a BIN database. Check at least three cards of the same BIN. Use two or three different gateways. And never trust the checker 100%. A micro-check of $0.50–$1 is your best friend. It will save you from 80% of losses.

A quick one-line reminder:
"Timing + decline code + 3 cards + 2 gateways + BIN database = diagnosis. Don't trust the checker — read between the lines. do_not_honor = death, insufficient_funds = life, fraudulent = BIN or environment. A micro-check is your friend."