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ConvesioPay Advanced Fraud Rules

Purpose of These Rules

These rules allow you to block or allow transactions based on:

  • Who the shopper is
  • How they behave
  • What payment instruments they use
  • How often they transact
  • Whether their behavior resembles fraud or abuse

1. Shopper Identity & Account History

What this category looks at

Information that identifies who the shopper is and how long they’ve been active.

Common uses

  • Blocking repeat abusers
  • Allowing known good customers
  • Reviewing brand-new shoppers

Examples (plain English)

  • Whether the shopper has used the same account before
  • How old the shopper account is
  • Whether the shopper has transacted successfully in the past
  • Whether the shopper has a history of disputes or refunds

2. Card & Bank Account Behavior

What this category looks at

Patterns related to cards and bank accounts, independent of the shopper.

Common uses

  • Detecting shared cards
  • Detecting compromised payment instruments
  • Blocking reused payment details across multiple shoppers

Examples

  • A card or bank account being used by multiple shoppers
  • A payment method previously associated with disputes
  • Repeated use of the same payment method in a short period

3. Device & Technical Fingerprinting

What this category looks at

Whether the same device or browser environment is being reused.

Common uses

  • Blocking fraud rings
  • Identifying automation or bot usage
  • Detecting multi-account abuse

Examples

  • Same device used by many shoppers
  • Same device attempting many payments quickly
  • Device associated with prior fraud

4. Network, IP & Location Signals

What this category looks at

Where the transaction is coming from and how trustworthy the network is.

Common uses

  • Blocking anonymized traffic
  • Restricting geographies
  • Identifying proxy or VPN usage

Examples

  • IP address used across many failed transactions
  • Traffic coming from anonymizing services
  • Country mismatch between shopper, billing, and delivery

5. Velocity & Frequency Controls

What this category looks at

How often things happen — one of the strongest fraud indicators.

Common uses

  • Stopping card testing
  • Stopping brute-force attempts
  • Preventing abuse spikes

Examples

  • Too many attempts in a short time
  • Too many authorizations in 24 hours
  • Too many payment attempts using the same details

6. Authorization & Decline History

What this category looks at

How often transactions have been refused, either by Adyen or issuing banks.

Common uses

  • Blocking persistent retry behavior
  • Identifying automated retries

Examples

  • Many declined transactions over time
  • Repeated refusals across different cards or accounts

7. Chargebacks & Fraud History

What this category looks at

Whether the shopper, card, or account has a known dispute or fraud history.

Common uses

  • Blocking repeat offenders
  • Escalating risky shoppers to review or 3DS

Examples

  • Previous fraud chargebacks
  • Multiple non-fraud disputes
  • Recent notifications of fraud

8. Name & Identity Quality Signals

What this category looks at

Whether names appear synthetic, malformed, or auto-generated.

Common uses

  • Detecting bots
  • Detecting fake identities

Examples

  • Names with excessive symbols
  • Names reused many times
  • Names that do not resemble real person names

9. Transaction Amount & Payment Context

What this category looks at

The shape of the transaction itself.

Common uses

  • Blocking abnormal amounts
  • Flagging outliers

Examples

  • Very small amounts repeated many times
  • Amounts outside normal business ranges
  • Mismatch between transaction context and expected behavior

10. Basket, Product & Promotion Signals

What this category looks at

What is being purchased and how discounts are used.

Common uses

  • Preventing promo abuse
  • Detecting bulk exploitation

Examples

  • Excessive promo usage
  • Repeated use of the same discount
  • Large quantities of the same item

11. Policy & Abuse Management

What this category looks at

Patterns that indicate refund abuse or policy exploitation.

Common uses

  • Blocking refund gaming
  • Limiting abuse of lenient policies

Examples

  • High refund ratios
  • Frequent partial refunds
  • Repeated disputes without fraud indicators

12. Industry-Specific Signals (e.g., Travel)

What this category looks at

Fields only relevant to specific verticals.

Examples

  • Travel timing
  • Passenger counts
  • Routing details
Updated on January 20, 2026

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