BIN Scout Training: How to Predict a Card's Passability Before Purchasing

[Good Carder]

From a carder to carders. You buy a card, enter it, and it crashes with fraudulent. You lose not only money but also time. What if you could predict whether a card would go through before you buy it, simply by analyzing its BIN? In 2026, this is possible. Using public data, your own attempt logs, and a little analytics, you can accurately filter out dead BINs and select only those that will bring profit.

In this article, I will teach you how to predict the success of a card before purchasing it. You will learn how to evaluate BINs based on five key parameters (country, bank, type, non-3DS, fraud score), how to build predictive models based on logs, how to use public BIN databases for filtering, and how to test BINs without purchasing a card (using the "blind check" method). This is the pinnacle of BIN intelligence.

Part 1. Five parameters that determine cross-country ability​

Before purchasing a card, you should evaluate its BIN using five parameters. Each parameter contributes to the overall probability of success.

1.1. Issuer's country (weight 30%)​

The BIN country must match the country of the target merchant and proxy. A perfect match increases the base probability by 30%. A mismatch (for example, a US card on an EU website) reduces the probability to almost zero, unless it's a non-3DS BIN and a low-value transaction.

Score:

• Complete match (US BIN → US site) = 10/10
• Close country (CA BIN → US site) = 6/10
• Second country (EU BIN → US site) = 2/10

1.2. Issuing bank (weight 25%)​

Some banks are known for their tolerance for fraud. Chase, Bank of America, and Capital One are good. Wells Fargo, Citi, and PNC are bad.

Rating:

• Good bank (Chase, BoA) = 10/10
• Average Bank (US Bank, TD) = 6/10
• Bad bank (Wells Fargo, Citi) = 2/10

1.3. Card type (weight 20%)​

Credit > Debit >> Prepaid. Credit offers high limits and a low fraud rate. Debit offers average rates. Prepaid is almost always blocked at the BIN level.

Rating:

• Credit = 10/10
• Debit = 6/10
• Prepaid = 1/10

1.4. Non-3DS status (weight 15%)​

If the BIN supports 3DS, bypassing it requires additional effort. Non-3DS BINs can be used on any website without authentication.

Rating:

• Non‑3DS = 10/10
• 3DS (but you can get around the low-value) = 5/10
• 3DS (hard) = 1/10

1.5. BIN Fraud Score (weight 10%)​

The higher the score, the more likely BIN is blacklisted. Use IPQualityScore or Scamalytics.

Rating:

• 0–30 = 10/10
• 30–60 = 5/10
• 60–100 = 1/10

1.6. Calculation example​

Let's say you're evaluating BIN 414720 (Chase, US, Credit, non-3DS, fraud score 25).

Parameter	Rating	Weight	Contribution
Page	10/10	30%	3.0
Bank	10/10	25%	2.5
Type	10/10	20%	2.0
Non‑3DS	10/10	15%	1.5
Fraud score	10/10	10%	1.0
Total			10.0/10

The success rate on a good merchant (WooCommerce, Braintree) is about 40%. On a bad one (Amazon, Stripe with a strict Radar), it's 10–15%.

Part 2: Log-Based Predictive Models​

Theory is good, but your own logs are even better. If you kept a table of attempts (as in Article 136), you have data on which BINs passed which gateways how many times.

2.1. Building a simple model​

Take your spreadsheet and calculate the success rate (success / total attempts) for each BIN. The more attempts, the more accurate the prediction.

Example:

BIN	Total	Success	Success rate	Forecast for a new merchant (similar gateway)
414720	50	20	40%	35–45%
439305	30	3	10%	8–12%
536425	20	2	10%	8–12%

Conclusion: BIN 414720 is the best choice.

2.2. Considering the gateway type​

The same BIN may have different throughput on different gateways. Build separate models for Stripe, Adyen, Braintree, and WooCommerce.

Example table (excerpt):

BIN	Stripe	Adyen	Braintree	WooCommerce
414720	25%	10%	30%	40%
439305	5%	2%	8%	12%

2.3. Extrapolation to new BINs​

If you want to evaluate a BIN that is not yet in your table, find similar ones:

• Same bank (Chase)
• Same type (Credit)
• Close range (414720 and 414721 often have similar characteristics)

Evaluate by analogy.

Part 3. Public BIN Databases: How to Get the Most Out of Them​

Free BIN databases (binx.vip, binlist.io, binbase.com) provide basic information: country, bank, and type. Paid ones (BINBase Pro, BIN Checker Premium) also provide non-3DS status, fraud score, and block history.

3.1 How to use binlist.io​

1. Enter BIN.
2. See scheme (Visa/MC/Amex).
3. Look at the type (CREDIT/DEBIT/PREPAID).
4. Look at the brand (Chase, Bank of America, etc.).
5. Look at the country (must match the proxy).

Limitations: Free databases do not show non-3DS status or fraud score.

3.2. Paid services (recommendations)​

• BinBase Pro ($49/month) — shows non-3DS status, block history, and fraud score.
• BIN Checker Premium ($29/month) — API integration, bulk check.
• IPQualityScore BIN Reputation (pay as you go) — fraud score from 0 to 100.

3.3. Closed forums and Telegram channels​

The latest non-3DS lists are published in the closed Exploit and XSS sections and in Telegram bots (@mr_bannker). Prices range from $10 to $50 per list, but they pay for themselves after the first successful purchase.

Part 4. The "Blind Check" Method: Testing BIN Without Purchasing a Card​

How can I check my BIN without purchasing a card? Use the Stripe API with fake data.

4.1. Algorithm​

1. Generate a card number: the first 6 digits are your BIN, the remaining 10 are random.
2. Generate random term and CVV.
3. Send a request to Stripe to create a PaymentMethod (or SetupIntent).
4. Analyze the answer.

Important: Stripe can't verify the validity of the random number, but it may return an invalid_number error if the BIN doesn't exist. If the BIN does exist, Stripe will return generic_decline or do_not_honor (card not found, but the BIN is valid). If Stripe returns fraudulent, the BIN is blacklisted.

4.2. Script example​

import random
from curl_cffi import requests

def test_bin(bin):
# Generate a random card
card_number = bin + ''.join([str(random.randint(0,9)) for _ in range(10)])
exp_month = random.randint(1,12)
exp_year = 2028
cvc = str(random.randint(100,999))

response = requests.post(
"https://api.stripe.com/v1/payment_methods",
headers={"Authorization": "Bearer sk_test_..."},
data={
"type": "card",
"card[number]": card_number,
"card[exp_month]": exp_month,
"card[exp_year]": exp_year,
"card[cvc]": cvc
},
impersonate="chrome120"
)

if response.status_code == 200:
print(f"BIN {bin} exists, not blocked")
elif response.status_code == 402:
error = response.json().get('error', {}).get('code')
if error == 'invalid_number':
print(f"BIN {bin} does not exist")
elif error == 'fraudulent':
print(f"BIN {bin} is blacklisted by Stripe")
else:
print(f"BIN {bin} exists, but declined: {error}")
else:
print(f"API error: {response.status_code}")

4.3. Limitations of the method​

• Does not provide information about non-3DS status.
• Doesn't check the card balance (because the card doesn't exist).
• Stripe may block an account for mass testing.

Use this method with caution, no more than 50-100 requests per day from one account.

Part 5. BIN Scout Checklist Before Purchasing a Card​

• Check the BIN country - it must match the proxy and merchant country.
• Find out the issuing bank - only good banks (Chase, BoA, Capital One).
• Determine the card type - only Credit (Debit at the very least, do not take Prepaid).
• Check non-3DS status using a paid database or closed lists.
• Evaluate your fraud score (IPQualityScore) – it should be <30.
• Look in your table to see how many times this BIN passed through the target gateways.
• Please perform a blind check via Stripe API to ensure BIN is not blacklisted.
• Make a decision: if at least two parameters are in the red zone, don’t buy.

Summary​

Predicting card throughput before purchase isn't magic, but systems analysis. Evaluate BINs based on five parameters, build models based on logs, use public databases, and the "blind check" method. This will allow you to filter out 80% of dead BINs and focus on those that actually bring in money.

A quick one-line reminder:
"Country, bank, type, non-3DS, fraud score — five parameters for forecasting. Log your attempts — build a model. Paid BIN databases pay off. A blind check through Stripe filters out black BINs. 10/10 across all parameters — buy, 5/10 — be careful, 2/10 — don't buy."