How to Detect Prepaid Cards: 2025 Guide for E-Commerce Fraud Prevention

What Are Prepaid Cards?

A prepaid card is a payment card loaded with a set amount of money. You load it, spend it, and often throw it away.

Think of it like a gift card with a Visa or Mastercard logo. No bank account needed. No credit check required. Just cash and go.

There are three main types:

Gift cards are one-time use cards purchased at retail stores. You see them hanging at CVS or Walmart. These are the highest fraud risk because they're bought with cash and have zero traceability.

Reloadable prepaid cards work like debit cards but without a bank account. Services like Chime, Cash App Card, and Green Dot fall into this category. You can add money repeatedly and use them long-term.

Virtual prepaid cards exist only online. No physical card. They're generated instantly and used for one-time purchases or subscriptions.

Here's why that matters: Each type carries different fraud risk. A Vanilla gift card bought with cash at a gas station? Massive red flag. A Chime prepaid debit card linked to a verified account? Much lower risk.

The key difference? Accountability. Regular debit and credit cards tie back to a real person with a verified identity. Prepaid cards often don't.

Why Detecting Prepaid Cards Matters

Bottom line: Prepaid cards are a fraud magnet, and they're costing you serious money.

Problem #1: Higher Chargeback Rates

Prepaid cards have a 5.2x higher chargeback rate than regular cards. We're talking 4.2% versus 0.8% for standard credit cards.

The average prepaid chargeback costs you $127 when you factor in the lost product, shipping, and chargeback fees.

Do the math: An e-commerce store processing 1,000 orders per month with 5% being prepaid equals 50 prepaid orders. At a 4.2% chargeback rate, that's 2 chargebacks monthly. You just lost $254, and that's before you count lost inventory.

Scale that over a year? $3,048 down the drain.

Problem #2: Anonymity Makes Fraud Easy

Here's the kicker: Prepaid cards are bought with cash. No ID verification. No paper trail.

A fraudster walks into CVS, drops $25 cash for a Visa gift card, and walks out anonymous. They use that card for an $800 online purchase. When you ship the product and they file a chargeback, the card issuer can't track anyone down.

The bank doesn't care. It's not their customer. There's no account relationship. No one pursues the fraud.

You eat the loss. Every single time.

Problem #3: Gift Cards Are the Worst Offenders

Prepaid gift cards specifically have a 7.1% chargeback rate. That's nearly one in every 14 transactions.

They're purchased specifically for fraud. One-time use. Non-reloadable. Bought at a gas station at 2am with cash.

Want proof? A Shopify store analyzed their last 100 chargebacks. 43 were prepaid gift cards. That's 43% of all chargebacks from one card type that represents maybe 5% of their orders.

The Solution

Early detection stops fraud before you ship anything.

It takes 30 seconds per transaction. Zero cost if you check manually. And the ROI? If you prevent just 3-4 chargebacks per month, you save thousands annually with minimal effort.

You just need to know what to look for.

5 Methods to Detect Prepaid Cards

Method 1: BIN Lookup (Most Reliable)

This is your primary weapon. BIN lookup identifies prepaid cards with 95-98% accuracy.

How it works:

The BIN (Bank Identification Number) is the first 6-8 digits of any card number. Every card issuer has specific BIN ranges registered with Visa and Mastercard. These databases are updated monthly with new BINs.

When you look up a BIN, you get the card type: prepaid, debit, or credit. You also get the issuer name and country.

Step-by-step process:

1. Get the card number from your payment gateway (you only need the first 6 digits)
2. Use a BIN lookup tool like BinSearchLookup, BinList.net, or FraudLabsPro
3. Check the result for Type = PREPAID
4. Make your decision based on your risk policy

Pros:

Fast. You're done in 30 seconds. Accurate at 95-98%. Free tools exist for unlimited lookups. You can identify the specific issuer (Vanilla, Green Dot, NetSpend) which helps you assess risk level.

Cons:

Manual checking means you review each suspicious order by hand. Not practical if you process 500+ orders daily without automation.

Recommended tools:

BinSearchLookup offers free unlimited lookups with detailed card information. BinList.net provides basic info for free. FraudLabsPro has a free tier plus paid options with fraud scoring.

Method 2: Transaction Behavior Analysis

Numbers don't lie. Certain patterns scream fraud.

Red flags to watch:

First-time customer plus high-value order plus prepaid card equals a 72% fraud rate. When someone you've never seen before drops $500 on a prepaid card, stop and check.

Multiple orders using similar BIN ranges suggest a batch of stolen prepaid cards. If you see several orders in a short time with BINs like 537211, 537215, 537219, someone is burning through a stack of gift cards.

Unusual order patterns matter too. Purchases at 3am. Rush shipping requested. Expensive items going to freight forwarders.

How to check:

Review your last 10-20 orders. Look for these patterns. Cross-reference suspicious BINs with a lookup tool.

This method takes practice but catches sophisticated fraud that automated systems miss.

Method 3: AVS (Address Verification System)

AVS checks if the billing address matches what the card issuer has on file.

How it helps with prepaid detection:

Prepaid cards fail AVS more often because fraudsters enter fake addresses. They don't care about the billing address since they're abandoning the card anyway.

When you see AVS mismatch plus prepaid card, your fraud probability jumps to 85%+.

Limitations:

Not all prepaid cards fail AVS. Legitimate customers recently moved might fail AVS on regular cards. Use this as a secondary signal, not your primary detection method.

But here's the best part: Combine AVS failure with BIN lookup results. Two red flags together? That's actionable intelligence.

Method 4: Card Network Indicators

Certain BIN ranges are known prepaid issuers.

What to check:

Cards starting with specific prefixes often indicate gift cards. For example, many Vanilla gift cards start with 5372XX. Green Dot prepaid cards use different ranges.

Keep an internal database of BIN ranges you've identified as problematic. When you spot a chargeback, note the BIN. Build your own fraud intelligence.

This takes time but becomes incredibly valuable. After 6 months, you'll recognize high-risk BINs instantly.

Method 5: Geographic Verification

Location mismatches combined with prepaid cards spell trouble.

Red flags:

Card issued in USA, shipping address in a high-risk country. Billing address doesn't match shipping address plus it's a prepaid card. New customer plus international shipping plus prepaid equals mandatory review.

Example: Prepaid card issued in California. Shipping to a freight forwarder in Delaware. Billing address claims to be in New York. That's three geographic inconsistencies on one prepaid card.

Stop. Check. Verify.

Automated Prepaid Detection with BIN Lookup

You have two paths: manual checking or API automation.

Free Manual Checking

Best for: Stores processing under 100 orders daily

Process:

You manually check suspicious orders. Takes 30 seconds per order. Costs nothing. Works perfectly for small stores where you're reviewing orders anyway.

Pick your highest-risk orders (new customers, high value, international) and run the BIN through a free lookup tool. Flag or cancel as needed.

Time investment: 5-10 minutes per day for most small stores.

Paid API Integration

Best for: Stores processing 500+ orders daily

How it works:

An API automatically checks every transaction in real-time. The system flags prepaid cards at checkout or before fulfillment. It integrates with Shopify, WooCommerce, Magento, or custom platforms.

You set rules: Auto-decline prepaid gift cards over $200. Flag prepaid debit cards for review. Auto-approve known legitimate prepaid issuers like Chime.

Cost analysis:

APIs run $50-150 monthly depending on transaction volume. If you prevent just 2-3 chargebacks per month, the API pays for itself. At scale, the ROI is massive.

Implementation example:

POST https://api.binsearchlookup.com/v1/lookup
{
  "bin": "485097"
}

Response:
{
  "type": "PREPAID",
  "issuer": "Vanilla Gift Card",
  "country": "Canada",
  "risk_score": 8
}

Your system reads the response and applies your fraud rules automatically. No human intervention needed.

Creating Your Prepaid Card Policy

Don't auto-decline all prepaid cards. That's leaving money on the table.

Here's why: 70% of prepaid transactions are legitimate. People use Chime for banking. Cash App Card for daily spending. PayPal prepaid for online purchases.

Auto-declining nukes your conversion rate and pisses off real customers.

Risk Scoring System

Build a point-based system instead:

• Prepaid card: +3 risk points
• High value ($200+): +2 points
• New customer: +1 point
• Geographic mismatch: +2 points
• Different billing/shipping addresses: +1 point
• Failed AVS: +2 points

Decision matrix:

0-3 points: Auto-approve. Low risk, process normally.

4-5 points: Manual review. Takes 2 minutes to verify.

6+ points: Cancel or contact customer before shipping.

Example Policies in Action

Scenario 1: Prepaid card, $35 purchase, returning customer, same billing/shipping = 3 points. Auto-approve.

Scenario 2: Prepaid card, $250 purchase, new customer, different country = 8 points. Cancel and refund or contact customer to verify.

Scenario 3: Prepaid gift card (Vanilla), any amount = Manual review every time. Gift cards are too high risk to auto-approve.

Adjust these thresholds based on your fraud rate. If you're getting hammered with chargebacks, lower the auto-approve threshold. If false positives hurt sales, raise it slightly.

Best Practices for Prepaid Detection

Practice #1: Check High-Value Orders First1

Start with orders over $200. That's where fraud hurts most.

After 30 days of data, expand to orders over $100. Eventually check all new customers regardless of order value.

This staged approach prevents overwhelm while protecting your biggest exposure.

Practice #2: Combine Multiple Signals2

BIN lookup alone is good. BIN lookup plus AVS plus device fingerprinting plus velocity checks is bulletproof.

One signal can be wrong. Device fingerprinting might flag a VPN user who's legitimate. AVS might fail because someone just moved.

But four signals pointing to fraud? That's not coincidence. That's a fraudster.

Layer your defenses. Fraud prevention is never about one perfect signal. It's about multiple good signals that together tell the truth.

Practice #3: Document Legitimate Prepaid Patterns3

Not all prepaid issuers are equal.

Track which prepaid cards result in successful deliveries and happy customers. After 90 days, you'll notice patterns. Chime prepaid cards? Usually fine. Cash App Card? Generally legitimate. Vanilla gift cards? Problem children.

Build a whitelist of lower-risk prepaid issuers. Build a blacklist of gift card BINs that always result in fraud.

Your data is more valuable than any generic fraud scoring system because it reflects YOUR customer base and YOUR products.

Practice #4: Contact Customer When Unsure4

Send an email: "We noticed your order used a prepaid card. For security purposes, can you verify your shipping address and provide a phone number?"

80% of legitimate customers respond within 24 hours. They understand security measures. They want their order.

Fraudsters rarely respond. They've moved on to the next target.

This simple email costs nothing and prevents hundreds in losses.

Practice #5: Monitor and Adjust5

Track two metrics monthly:

False positive rate: Percentage of declined orders that were actually legitimate. If this exceeds 5%, you're too aggressive.

False negative rate: Percentage of fraud that slipped through. If this exceeds 10% of your total fraud, you're too lenient.

Adjust your risk thresholds based on these numbers. Fraud prevention is not set-it-and-forget-it. Your fraud patterns change. Your customer base evolves. Your policies should too.

Common Mistakes to Avoid

Mistake #1: Blocking All Prepaid Cards

This kills 70% of legitimate prepaid transactions. You're declining real customers who use prepaid cards for legitimate reasons.

The solution? Risk-based scoring. Not blanket bans.

Mistake #2: Only Checking Obvious Red Flags

Sophisticated fraudsters avoid obvious patterns. They use medium-value orders ($100-200). They ship to residential addresses. They don't rush shipping.

Check orders that seem "almost fine." That's where experienced fraudsters operate.

Mistake #3: Not Checking BIN at All

This is the worst mistake. You're flying blind.

Preventable fraud slips through. You lose thousands. All because you didn't spend 30 seconds checking a BIN.

Even manual checking saves massive money. There's no excuse for skipping this step.

Mistake #4: Relying Only on Prepaid Detection

BIN lookup identifies card type. It doesn't verify the cardholder is legitimate.

A real customer might use a prepaid card. A fraudster might use a regular credit card.

Prepaid detection is one piece of fraud prevention. Use it alongside address verification, device fingerprinting, order velocity checks, and customer communication.

Mistake #5: Ignoring Prepaid Issuer Type

Vanilla gift cards are not the same risk level as Chime prepaid debit cards.

Gift cards: 7%+ chargeback rate. Reloadable prepaid from known issuers: 2-3% chargeback rate. That's a huge difference.

Differentiate by issuer type in your fraud rules. Treat gift cards as high risk. Treat established prepaid debit issuers as medium risk.

Real-World Case Studies

Case Study #1: Shopify Store (Electronics)

Before: 11 chargebacks monthly. 8 were prepaid cards. That's 73% of their fraud coming from one card type.

What they implemented: BIN lookup for all orders over $150. Manual checking took the owner 5 minutes daily.

Results after 90 days: 2 chargebacks monthly. That's an 82% reduction in chargebacks.

Savings: $1,100 per month ($13,200 annually). Time investment: 5 minutes per day. ROI: Massive.

Case Study #2: WooCommerce Store (Fashion)

Before: Accepting all orders. 6% chargeback rate on prepaid card orders. Losing $800-1,200 monthly to prepaid fraud.

What they implemented: Manual BIN checking plus risk scoring system. Orders scoring 6+ points were cancelled with refunds offered.

Results: Chargeback rate dropped to 1.2%. False positive rate stayed under 2% (they declined 2% of legitimate orders).

Net benefit: $3,400 per month saved. They calculated the declined legitimate orders cost about $400 monthly in lost sales, but preventing $3,800 in chargebacks more than compensated.

Case Study #3: Large E-commerce (API Integration)

Volume: 2,000 orders daily. Manual checking impossible at this scale.

What they implemented: BIN lookup API with automated flagging. Prepaid cards over $300 auto-declined. Prepaid cards $100-300 flagged for review. Under $100 auto-approved.

Results first month:

• 94% of flagged prepaid cards turned out to be fraud attempts
• Prevented $47,000 in fraud losses
• API cost: $150 monthly
• ROI: 313x return in first month alone

They also discovered 6% of their customer base legitimately used Chime and Cash App prepaid cards. By whitelisting these specific issuers, they avoided declining real customers while still catching gift card fraud.

FAQ Section

Tools and Resources

Free BIN Lookup Tools

BinSearchLookup: Unlimited free lookups. Fastest response time. Detailed card information including issuer name, card type, and country. Best option for manual checking.

BinList.net: Basic card information for free. Good for occasional checking. Less detailed than BinSearchLookup but reliable.

BinDB.com: Limited free tier (100 lookups per day). Good accuracy. Useful as a backup tool.

Paid BIN Lookup APIs

BinSearchLookup API: $50-150 monthly depending on volume. Real-time integration. Includes risk scoring. Good for growing stores.

FraudLabsPro: $60+ monthly. Combines BIN lookup with broader fraud detection scoring. Useful if you want an all-in-one solution.

BinBase: $99+ monthly. Enterprise features. Higher volume tiers available. Best for large operations processing 5,000+ orders daily.

Fraud Prevention Platforms (Include BIN Checking)

Shopify Fraud Analysis: Built into Shopify. Limited features. Decent starting point for Shopify stores.

Signifyd: Guarantees chargebacks (they cover losses). Premium pricing. Good for stores with major fraud problems.

NoFraud: Pay per transaction model. Real-time decisions. Balances cost with effectiveness.

Conclusion

Prepaid cards create 5x higher chargeback risk than regular cards. But detection takes just 30 seconds and prevents costly fraud before you ship anything.

BIN lookup is the most reliable method with 95-98% accuracy. Combine it with risk-based scoring instead of blanket bans. Start checking high-value orders first, then expand coverage gradually.

Your action plan:

1. Review your last 10 chargebacks. Count how many were prepaid cards.
2. If 50% or more were prepaid, implement BIN lookup today.
3. Start with free manual checking for orders over $200.
4. Track results for 30 days (chargebacks prevented, false positives).
5. Upgrade to API integration if your volume justifies automation.

The numbers don't lie. Stores implementing prepaid detection cut chargebacks by 60-82%. The 30 seconds you invest per suspicious order saves thousands in chargebacks, lost inventory, and payment processor fees.

Prepaid card fraud is preventable. You just need to check before you ship.

Got questions about implementing prepaid detection? Drop a comment below.