BIN-IP Fraud Detector
Free fraud signal tool for checkout testing. Compares the card BIN issuing country with an IP you enter and shows mismatch risk for QA and fraud analysts.
What is BIN-IP Fraud Detector?
The BIN-IP Fraud Detector is an advanced fraud detection tool that combines Bank Identification Number (BIN) lookup with IP geolocation analysis to identify potentially fraudulent transactions.
By comparing the country where a credit card was issued (determined from the BIN) with the geographic location of the IP address used for the transaction, this tool helps merchants, payment processors, and security professionals detect suspicious activity patterns and prevent fraud.
Use this tool for fraud prevention and security research. BIN data and IP geolocation are both approximate. Treat the risk score as one signal among many, not as a stand-alone verdict on a transaction.
IP addresses reflect network infrastructure, not the device itself, and a BIN tells you where a card was issued, not where the cardholder is right now. Treat the risk score as one input alongside AVS, CVV checks, device fingerprinting and your own velocity rules. We do not store the BINs, IPs or results you submit.
How to Use BIN-IP Fraud Detector?
Using our BIN-IP Fraud Detector is straightforward and requires no technical expertise. Follow these simple steps:
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Step 1: Enter the BIN (6-8 digits) or full credit card number in the first input field. You can enter just the first 6-8 digits (BIN) or the complete card number. The tool automatically extracts the BIN from full card numbers.
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Step 2: Enter the IP address associated with the transaction in the second input field. You can enter an IPv4 address (e.g., 8.8.8.8) or IPv6 address. This is the IP address from which the transaction originated.
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Step 3: Complete the CAPTCHA verification to ensure secure usage and prevent automated abuse.
- Step 4: Click on the "DETECT FRAUD" button. The tool will perform BIN lookup, IP geolocation analysis, and calculate a risk score based on 17 risk factors.
You get back a risk score from 0 to 100, a Low/Medium/High band, and the reasons that drove the score. Alongside that, the response carries the BIN record (issuing bank, country, card type), the IP geolocation, and a suggested action for the score band. Expand the modals for the full record, copy any field, or export the whole result as JSON, CSV or TXT.
Why Does BIN-IP Fraud Detector Matter?
A common red flag in online fraud is a simple geographical mismatch. A card issued by a bank in the US shouldn't logically be used from an IP address in Southeast Asia moments later.
The BIN-IP Fraud Detector cuts through the complexity. It takes two distinct pieces of information, the Bank Identification Number (BIN) from the card and the customer's IP address and analyzes them using a proprietary algorithm built on 17 risk factors. The result is a simple, actionable risk score from 0 to 100.
Who Should Use This Tool? (Use Cases)
This tool is a vital asset for anyone serious about digital security and fraud prevention:
- E-commerce Merchants (Small & Large): Integrate this check into your manual review process or automatically decline high-risk orders to prevent costly chargebacks.
- Payment Gateway Providers & Fintechs: Enhance your existing fraud screening layers with geographic verification.
- Fraud Prevention & Security Teams: Use the detailed results to rapidly investigate suspicious transactions flagged by other systems.
- Online Marketplaces & Digital Goods Sellers: Verify user location during high-value purchases or account creation to ensure authenticity.
- Developers & Security Professionals: Utilize these data insights for testing and improving internal fraud detection systems during development.
How BIN-IP Fraud Detector Works?
Three steps, no magic. Look up the BIN, look up the IP, compare the two and score the result. The card number you pass in (if you pass one) is processed in memory and discarded.
1. The Deep BIN Lookup
When you input a full card number or a 6-to-8 digit BIN, our system automatically extracts the BIN and cross-references it with our extensive internal database, which currently holds over 400,000+ active BIN entries. This lookup reveals the card's identity:
The specific bank that put the card in your customer's hand.
Identifies the network (Visa, Mastercard, American Express, JCB, UnionPay, etc.).
Is it a Credit, Debit, Prepaid, or Virtual card? Is it a Classic, Gold, or Platinum tier?
The precise country where the card was physically issued. This is the foundation of our geographic check.
Classifies the issuer (e.g., traditional bank, modern fintech like Revolut or Chime, or a dedicated prepaid provider).
Identifies the expected card number length (typically 13, 15, 16, or 19 digits) based on the card brand and type, which helps validate card number format.
2. Real-Time IP Geolocation
Next, we analyze the customer's IP address using the reliable external data sources provided by the IPWHO API (ipwho.is). This step reveals the user's current network footprint and location:
We pinpoint the country, region, city, and exact geographic coordinates.
We gather details on the Internet Service Provider (ISP), organization, and Autonomous System Number (ASN).
This is crucial for risk scoring. We distinguish between standard residential connections (low risk), mobile networks, and commercial/hosting/datacenter connections (higher risk).
Our system flags the use of VPNs, proxies, Tor relays and large hosting environments. These are all common ways for an attacker to hide their true location.
3. Risk Algorithm and Scoring
This is where the magic happens. Our algorithm compares the data points from Step 1 and Step 2. It evaluates 17 distinct risk factors, assigning specific penalty points for each "red flag" detected. The total score (0-100) translates into three actionable risk levels:
Low Risk
The transaction appears safe and legitimate. Geolocation matches between the card's issuing country and the customer's IP address location are consistent. The card type aligns with the connection type (e.g., standard residential IP with a credit card from a reputable bank), and minimal or no risk indicators were detected. The transaction shows typical patterns of genuine customer behavior with no suspicious anomalies in the BIN-IP relationship.
Proceed with order fulfillment with confidence, as the transaction demonstrates strong alignment between card and IP geolocation data with minimal risk indicators detected.
Medium Risk
Some risk factors are present that warrant additional scrutiny. Common scenarios include minor geographic inconsistencies such as a time zone mismatch, a prepaid or virtual card being used from a mobile connection, or slight discrepancies between the card's issuing country and the IP geolocation.
While these indicators don't necessarily indicate fraud, they suggest the need for enhanced verification to ensure transaction legitimacy and protect against potential chargebacks.
Consider implementing additional verification steps, like a quick phone call to the customer or asking for AVS/CVV confirmation.
High Risk
Multiple severe risk factors have been detected that strongly suggest fraudulent activity. This typically includes significant geographic mismatches such as a card issued in one country being used from a completely different country, combined with the use of VPNs, proxies, or Tor networks to mask the true location.
Additional red flags may include prepaid or virtual cards from high-risk financial institutions, datacenter or hosting IP addresses, and multiple behavioral anomalies that deviate from normal customer patterns. These combinations significantly increase the likelihood of chargebacks and financial loss.
We strongly recommend flagging this transaction for immediate manual review or outright declining the purchase to prevent chargebacks and financial loss.
The 17 Risk Factors Explained in Detail
We believe in transparency. Understanding why a transaction is flagged allows you to make better business decisions. Here is the breakdown of the factors our algorithm uses:
BIN-Based Risk Factors (4 factors)
Country mismatch
+30The single most impactful flag. A card used physically thousands of miles from its issuing bank is highly indicative of potential card-not-present fraud.
Card type risk
+15In certain e-commerce scenarios, Prepaid (+15 points) and Virtual cards (+15 points) have historically presented higher risk profiles than traditional Credit (+5) or Debit (0) cards.
Card brand/scheme risk
+15Some regional payment schemes carry more risk when used internationally. (e.g., UnionPay outside of China adds risk).
Bank category risk
+15Transactions involving modern fintech issuers (+10 points) or dedicated prepaid card issuers (+15 points) may warrant additional scrutiny compared to established traditional banks (0 points).
IP Geolocation Risk Factors (9 factors)
Proxy/VPN/Tor detection
+25Anonymization services are a major red flag, as fraudsters use them specifically to conceal their true location and identity.
Hosting provider/datacenter
+20Non-residential IPs (from AWS, Azure, DigitalOcean, etc.) are rarely associated with genuine consumer purchases.
High-risk country
+15IPs originating from countries historically known for high rates of online fraud are automatically flagged.
Continent mismatch
+10A broader geographic check complementing the country mismatch.
Timezone mismatch
+10A difference of greater than two hours between the card's timezone and the IP's timezone suggests time manipulation tactics used in fraud rings.
Distance too large
+10A physical distance exceeding 2,500 km between the card's origin and the IP's location is a significant geographical anomaly.
Suspicious ASN
+10Association with known commercial hosting Autonomous System Numbers flags potential non-consumer traffic.
ISP type classification
+5Network type matters. Corporate (+5) or Mobile Networks (+3) connections often differ from standard residential connections (0 points).
Unknown/Missing ASN
+5When the Autonomous System Number (ASN) is missing or cannot be determined, it raises concerns about the network's legitimacy and makes it difficult to verify the connection's origin.
Behavioral Risk Factors (2 factors)
Income level mismatch
+10A behavioral indicator where a card issued in a high-income country is used from an IP located in a low-income country.
Inconsistent geography pattern
+10When multiple geographic inconsistencies occur simultaneously (e.g., country + distance + timezone mismatch), the risk is compounded.
Input Validation Risk Factors (2 factors)
Invalid IP format
+5Basic validation of input data integrity to ensure the IP address follows proper formatting standards and structure.
IP geolocation API failure
+10If a data source lookup fails, we default to a cautious approach until full validation can be confirmed.
Frequently Asked Questions (FAQ)
A BIN is the first 6 to 8 digits of a credit or debit card number. It identifies the issuing bank or financial institution, card brand (Visa, Mastercard, etc.), card type (credit, debit, prepaid), and the country where the card was issued. This information is crucial for fraud detection as it allows us to compare the card's origin with the transaction's IP address location.
Our risk score (0-100) is based on 17 distinct risk factors that analyze geographic mismatches, connection types, card characteristics, and behavioral patterns. While highly accurate, it should be used as one layer of fraud prevention, not the sole decision-making factor. Always combine it with other verification methods like AVS, CVV checks, and customer communication.
Yes, it can sit inside a fraud prevention workflow. Use it as one layer among several: AVS, CVV checks, device fingerprinting and your own velocity rules. For production traffic, you will also want rate limiting, enterprise-grade geolocation database integrations, and a way to combine those signals with your scoring.
We do not store inputted BIN, IP, or card data in our application database. All lookups are processed in memory for the request and discarded when the response is returned. Standard server access logs are described in our Privacy Policy. Only aggregated, anonymized statistics may be collected for service improvement purposes.
A high-risk score (71-100 points) indicates multiple red flags. We recommend flagging the transaction for manual review, requesting additional verification (phone call, AVS/CVV confirmation), or declining the purchase if your business policy allows. However, remember that legitimate customers traveling or using VPNs may sometimes trigger false positives.
Our internal BIN database contains over 400,000+ active BIN entries and is constantly updated to reflect new card issuers, bank mergers, and changes in the payment industry. We regularly sync with industry sources to ensure accuracy and completeness.
Yes, the BIN-IP Fraud Detector is completely free to use. We utilize reCAPTCHA validation to prevent automated abuse and ensure fair usage for all users. There are no hidden fees or subscription requirements.