Antifraud

Antifraud Getnet evaluates the risk of e-commerce transactions by combining customer-provided data with advanced machine learning technologies, neural networks, configurable rules, and managerial decision-making.

The accuracy of fraud detection increases as the volume and quality of data used to train the models grow. Richer datasets enable the use of a broader set of fraud indicators, resulting in more precise risk assessments for each transaction.

Getnet Payment Link API features an integrated antifraud solution that operates in real time throughout the payment flow. The system analyzes multiple signals—such as purchasing behavior, device information, and data consistency—to generate a risk score and automatically approve or block transactions. Providing complete and accurate customer data at the time of payment intent creation significantly enhances the effectiveness of the analysis and improves overall fraud detection rates.

Therefore, we request that all required customer information be correctly provided at the time of creating a Payment Link API payment intent, as outlined below:

Field	Type	Description	Example
`customer_id`	String	Customer’s id number	`customer_21081826`
`first_name`	String	Customer’s first name	`John`
`last_name`	String	Customer’s last name	`Doe Smith`
`name`	String	Customer’s full name	`John Doe Smith`
`document_type`	String	Document type	`CPF`
`document_number`	String	Number from the customer’s document	`12345678912`
`email`	String	Customer’s email	`[email protected]`
`billing_address`	Object	Complete information from customer’s address	—

Example of a customer object in the payment intent:

{
  ...
  "customer": {
    "customer_id": "customer_21081826",
    "first_name": "John",
    "last_name": "Doe Smith",
    "name": "John Doe Smith",
    "email": "[email protected]",
    "document_type": "CPF",
    "document_number": "12345678912",
    "phone_number": "5551999887766",
    "gender": "Male",
    "checked_email": false,
    "billing_address": {
      "street": "Av. Brasil",
      "number": "1000",
      "complement": "Sala 1",
      "district": "São Geraldo",
      "city": "Porto Alegre",
      "state": "RS",
      "country": "BR",
      "postal_code": "90230060",
      "reference": "Near the hospital"
    }
  },
  ...
}

Device Fingerprint

Device fingerprinting is a fraud prevention technique used to uniquely identify the device involved in a transaction based on a combination of technical and behavioral attributes. Instead of relying on a single identifier, the method analyzes multiple signals, such as device type, operating system, browser characteristics, IP address, geolocation, screen resolution, language settings, and other environmental factors to generate a unique device profile.

Within the Getnet antifraud ecosystem, device fingerprinting helps detect suspicious behavior by identifying patterns such as repeated transactions from the same device using different cards, abnormal purchase frequency, or inconsistencies between device data and customer information. This approach strengthens risk assessment by enabling more accurate detection of fraudulent attempts while minimizing false positives.

The effectiveness of device fingerprinting increases when device data is consistently and correctly collected during the payment flow, contributing to a more reliable risk score and improved real-time decision-making for transaction approval or blocking.