Payment Fraud Prevention
Catch fraudulent transactions before they clear by recognizing returning fraudsters across sessions.
The Problem
Payment fraud costs e-commerce 1.4% of total revenue — $4.2M/year for a mid-market retailer processing $300M. Chargeback fees ($15-100 per dispute), manual review overhead, and merchandise loss compound the impact. Legacy fraud detection leans on easily-spoofed data points that modern attackers bypass in minutes using stolen cards, VMs, and VPN tunnels.
Our Solution
device fingerprinting persists across sessions, VPN changes, and incognito mode. Combined with bot detection and IP analysis, we catch fraud that other solutions miss.
Key Metrics
How It Works
How tracio.ai prevents payment fraud at every stage of the transaction.
Device connects
Customer enters checkout and initiates payment
Signals analyzed
tracio.ai identifies the device and retrieves its history across all accounts
Threat blocked
High-risk transactions are blocked or flagged for manual review before payment is processed
Customer enters checkout and initiates payment
tracio.ai identifies the device and retrieves its history across all accounts
Device risk score is derived from device signals, velocity, IP intelligence, and historical fraud patterns
High-risk transactions are blocked or flagged for manual review before payment is processed
Before vs After
Without tracio.ai
- Fraudsters use stolen cards across multiple accounts undetected
- IP-based blocking catches only basic fraud, misses VPN users
- Manual review queues overwhelm fraud teams with false positives
- Chargebacks cost $2.40 for every $1 of fraud
With tracio.ai
- Same device is recognized across all accounts and sessions
- VPN, proxy, and Tor detection provides layered defense
- AI-driven suspect scoring cuts false positives by 85%
- Chargeback rates drop by 92% within the first month
Expected Results
Key Features
- 01Device Identification persistent device identification across sessions
- 02IP Intelligence VPN and proxy detection for location verification
- 03bot detection to block automated carding attacks
- 04IP Intelligence velocity tracking to detect rapid-fire transactions
- 05Smart Signals suspect scoring for risk-based decisioning
- 06Real-time risk scoring with configurable thresholds
- 07Historical device activity timeline for investigations
- 08Integration with existing payment fraud tools and rules engines
Frequently Asked Questions
Real-World Scenario
A fraud ring operates across Eastern Europe, purchasing stolen credit card data in bulk. Each operator runs 3-4 virtual machines with rotating residential proxies, creating new accounts per card. Within an hour, they push through dozens of high-value purchases before chargebacks trigger. Traditional fraud tools see each transaction as an isolated event from a unique IP. tracio.ai traces the device graph: the same GPU profile, canvas hash, and audio fingerprint appear across 47 accounts — exposing the entire ring from a single flagged transaction.
Implementation Guide
Step-by-step integration with tracio.ai
Deploy the tracio.ai SDK on your checkout page to begin collecting device signals at the point of payment
Configure the Server API webhook to receive real-time device trace results including visitor ID, confidence score, and smart signal flags
Build correlation rules that link device traces to transaction history — flag devices seen across multiple accounts or with prior chargebacks
Set risk thresholds using signal correlation: high-risk transactions (VPN + incognito + new device) route to manual review or 3DS challenge
Monitor the fraud analytics dashboard to refine scoring weights and reduce false positives based on your chargeback feedback loop
Expected Timeline
Device traces begin building a historical graph. Obvious repeat fraudsters (same device, multiple stolen cards) are caught immediately. Expect 40-50% fraud reduction on flagged transactions.
Signal correlation matures as the device graph grows. Fraud reduction reaches 80-85% with false positive rates under 0.5%. Manual review queue shrinks by 60%.
Full device graph coverage across your user base. 92% fraud reduction sustained. Chargeback rates drop below 0.1%. ROI typically exceeds 20x the subscription cost.
Common Mistakes to Avoid
Blocking all VPN users instead of using VPN detection as one signal in a composite risk score — this creates excessive false positives and alienates privacy-conscious customers
Setting risk thresholds too aggressively at launch before the device graph has enough data — start with monitoring mode and tighten thresholds as you gather baseline metrics
Ignoring the device confidence score and treating all traces as equal — a 99.9% confidence match carries far more weight than an 85% partial match
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