AI-Powered Fraud Prevention

AI Fraud Prevention That Stops Fraud Without Blocking Good Customers

Every fraud prevention system makes a trade-off between catching fraud and blocking legitimate customers. Poorly tuned systems decline 10-15% of valid orders. That means every $1 saved in fraud costs $2-3 in declined revenue.

GetPayment's ML fraud detection is trained on 10B+ ecommerce transactions and continuously calibrated for your specific customer patterns — delivering 99.4% detection accuracy with false-positive rates below 2.1%. Included with every account. No extra cost.

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99.4% detection accuracy
3DS2 liability shift
200+ signals per transaction
Included free

True Cost of Fraud at $1M/Month

LexisNexis: $1 of fraud = $3.75 total cost

Transaction value lost (0.5%)$5,000
Merchandise + fulfillment costs$2,500
Chargeback fees & management$3,500
False-positive declined revenue (8%)$80,000
Processor penalty riskVariable
With GetPayment Fraud Prevention

Fraud rate drops to 0.08%. False positives under 2.1%. Total fraud-related cost: ~$3,200/month vs ~$91,000.

99.4%
Detection Accuracy
76%
Fraud Loss Reduction
<100ms
Risk Score Time
200+
Signals Per Transaction

Why Most Fraud Prevention Fails High-Volume Merchants

Most payment processors include a basic fraud prevention tool built on static rules. These tools work adequately at low volumes but break down as you scale for two reasons: rule-based systems can't adapt to your evolving customer base, and the false-positive cost of overly aggressive rules grows linearly with your volume.

At $1M/month, a fraud prevention tool with an 8% false-positive rate is declining $80,000 in legitimate orders every month. That's 960,000 annually in declined good customers — many of whom never return. The fraud you prevented might be $5,000. The math is catastrophic.

GetPayment's approach is different: ML models trained on your specific transaction patterns, continuously recalibrated, with false-positive rates below 2.1%. Combined with 3DS2 for liability shift and a custom rules engine for business-specific logic, it's the most accurate fraud prevention available at any price — included in your account. Works seamlessly with our chargeback protection for complete dispute coverage across CBD, gaming, and all high-risk verticals.

Fraud Challenges We Solve

CHALLENGE
Rule-based systems block good customers (high false positives)
SOLUTION
ML models calibrate false positive rate to under 2.1% by learning your customer patterns
CHALLENGE
Fraud vectors evolve faster than manual rule updates
SOLUTION
Continuous model retraining on live transaction data catches novel fraud in real-time
CHALLENGE
Carding attacks generate processing fees before you notice
SOLUTION
Velocity detection stops attacks after <10 attempts, seconds after initiation
CHALLENGE
Chargebacks from unauthorized transactions cost 3.75x face value
SOLUTION
3DS2 liability shift eliminates unauthorized transaction chargebacks entirely
CHALLENGE
Generic fraud rules don't fit your specific business model
SOLUTION
Custom rules engine lets your team encode business-specific fraud logic without engineers
CHALLENGE
Fraud from organized rings operating across multiple stolen cards
SOLUTION
Network graph analysis connects orders across device, email, IP, and card to identify rings

Multi-Layer Fraud Prevention System

200+ signals, machine learning models, and 3DS2 working together to stop fraud at every stage.

ML Fraud Detection

Models trained on 10B+ transactions. Continuously updated on your transaction patterns. Detects novel fraud before you can write rules for it. 99.4% accuracy, <2.1% false positives.

3D Secure 2.0

Frictionless authentication for 90%+ of transactions. Full liability shift to card issuers for authenticated transactions. Eliminates unauthorized transaction chargebacks entirely.

Device Fingerprinting

Unique device identification across sessions and cards. Identifies returning fraudulent devices even when using different stolen card credentials. Effective against account takeover.

Network Graph Analysis

Connects orders across email, device, IP, and card to identify fraud rings. Catches organized fraud that evades single-transaction analysis.

Velocity Controls

Real-time detection of carding attacks. Stops card testing after <10 attempts. Per-device, IP, email, and BIN velocity monitoring with automatic escalation.

Custom Rules Engine

Visual rule builder for non-technical teams. Shadow mode testing before activation. Encode business-specific logic the ML model doesn't capture.

200+ Fraud Detection Signals

Every transaction is analyzed across device, network, behavioral, and transactional dimensions simultaneously.

Device fingerprinting
Browser configuration
IP geolocation
VPN/Proxy detection
Tor exit node detection
Email domain age
Email reputation score
Phone number validation
Billing/shipping mismatch
Shipping address velocity
Card BIN analysis
Card country mismatch
Order value vs. history
Checkout session speed
Typing cadence analysis
Mouse movement patterns
Product category risk
Time-of-day patterns
Historical fraud data
Network graph connections
ISP reputation
Geolocation accuracy
Device-card associations
Carding attack detection

Fraud Detection and Prevention: How E-Commerce Fraud Works and How to Stop It

Types of E-Commerce Fraud and How AI Detects Them

Modern e-commerce fraud has evolved beyond simple "stolen credit card" scenarios. Today's fraud is sophisticated, coordinated, and designed to evade traditional rules-based systems.

  • Account Takeover (ATO) Fraud

    Fraudsters compromise customer credentials and make unauthorized purchases. AI detects this through device fingerprinting, behavioral patterns, and location anomalies.

  • Carding and Velocity Attacks

    Automated attacks test thousands of stolen card numbers at high speed. Detected through velocity checks, IP reputation, and transaction clustering.

  • Friendly Fraud and Chargebacks

    Legitimate customers dispute charges after receiving products. AI identifies high-risk patterns (mismatched shipping/billing address, high-value orders, certain product categories).

  • Refund Fraud

    Customers request refunds but keep products, or file returns for items they never received. Detected through return frequency, product value, and customer history.

False Decline Problem: Why Traditional Fraud Rules Cost Revenue

Rule-based fraud prevention is overly aggressive. A simple rule like "block all transactions from outside the customer's country" will catch some fraud—but it will also block legitimate travel purchases, international customers, and gift purchases. This is called a "false decline."

At scale, false declines are expensive. Studies show:

  • • 1 in 4 false declines drives the customer to a competitor permanently
  • • Average e-commerce store loses 2–3% of revenue to overly aggressive fraud prevention
  • • For a $1M/month store, that's $20–30k/month in lost revenue

GetPayment's AI balances fraud prevention with customer experience. Our 99.4% accuracy rate with false declines under 2.1% means you block fraud without blocking sales.

How to Implement Fraud Prevention Without Killing Conversion

  • ✓

    Start with passive detection

    Use AI to identify high-risk transactions, but don't block them immediately. Let some through and monitor chargeback rates.

  • ✓

    Use step-up authentication (3D Secure)

    Reserve 3D Secure challenges for truly high-risk transactions (>$500, high-risk product, suspicious patterns). This catches fraud without blocking good customers.

  • ✓

    Optimize by vertical and product category

    CBD has different fraud patterns than gaming or supplements. Use industry-specific models for better accuracy.

Measuring Fraud Prevention Effectiveness

Track these metrics to know if your fraud prevention is working:

  • • Chargeback ratio — Goal: under 0.5%. Track monthly and by product category.
  • • Friendly fraud rate — What % of chargebacks are friendly fraud vs. legitimate disputes?
  • • False decline rate — How many legitimate customers are being declined? Should be under 2–3%.
  • • Conversion rate impact — Has your fraud prevention rules increased checkout drop-off?

Fraud Prevention FAQ

Technical and strategic questions from high-volume ecommerce operators.

People Also Ask About Fraud Prevention

What is the industry average fraud rate for ecommerce?

US ecommerce fraud averages 0.5-1.0% of revenue. GetPayment clients average 0.08% after implementing ML fraud detection. Source: LexisNexis True Cost of Fraud Study (2024), GetPayment client data

Does 3D Secure reduce conversion rates?

Modern 3DS2 uses risk-based authentication: 90%+ of transactions pass frictionlessly with no customer action. Only high-risk transactions trigger a step-up challenge. Conversion impact is typically under 0.5%. Source: EMVCo 3DS2 specification, GetPayment implementation data

Is fraud prevention included in payment processing fees?

At GetPayment, ML fraud detection, 3DS2, velocity controls, device fingerprinting, and the custom rules engine are all included free with every merchant account — no separate fraud module cost.

What is the difference between fraud and friendly fraud?

True fraud = unauthorized transaction (stolen card). Friendly fraud = legitimate customer disputes a charge they made. Friendly fraud accounts for 60-80% of ecommerce chargebacks. Different prevention strategies apply to each. Source: Chargebacks911 Industry Report (2024)

Recommended Ecommerce Fraud Prevention Guides

Dive deeper into topics related to this guide.

Stop Fraud, Not Customers

AI fraud prevention included with every GetPayment account. 99.4% accuracy, false positives under 2.1%.

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