Integration Process

Integration Steps

Our fraud prevention solution operates in three simple steps:

  1. Provide data: 
    Send SEON user, transaction, and/or device data
  2. Receive enriched data & risk scoring results:
    SEON enriches the data and its Scoring Engine assigns and returns risk scores
  3. Feedback:
    Provide SEON with feedback to refine the rules and get more precise fraud scores. This can be done via our user-friendly Admin Panel, or through our Label API.


Integration timeline

You can find a detailed timeline here to see how long it takes to get results with SEON.


Step 1 - Providing the Data

All the user, transaction, and device data is sent via the Fraud API. Your first step is to define payloads for the API, populating it with as many relevant data points as possible. All the fields are optional, but the more you fill, the more precise our results will be.

  • For custom business-specific data points, use the custom_fields object.
  • The config object helps you to fine-tune settings such as versions, response, and aggregating data enrichment APIs when required.
  • You must define the authentication points aka. action_type-s (account_register, account_login, purchase, etc.) where risk assessment data can be collected or fraud should be prevented.
  • For device fingerprinting, you can use our JavaScript snippet for web apps, and the SDKs for iOS and Android mobile apps. Use the session to send the encrypted payload returned by the SDK (supported by JS Agent v4, iOS SDK 3.0.0, Android SDK 3.0.0) for device data collection.

This allows for a more comprehensive overview of each transaction, allows us to establish meaningful connections across different users, and it makes for better data quality which is important for the machine learning functions to work properly.


Custom Support

Please get in touch with your dedicated customer success team member to tailor and validate your specific payloads. Our team is ready to support you via email, phone, the shared Skype, or Slack channel for any queries you might have.


Payload Examples

You can find examples for an airline company and an e-wallet here, including custom fields specific to each industry. 


Step 2 - Enrichment and Scoring

SEON is designed to give you full transparency behind every score and decision (a.k.a. state). This is why every data point will be available in the response.

By default, the fraud scores are based on preset rules, which can be reviewed in the Scoring Engine. A score of 10+ is considered risky. Standard thresholds for each state are:

REVIEW10 - 20



Step 3 - Feedback

Providing feedback is the key to refining the rules and getting more precise fraud scores. This is particularly important when discovering false positives and false negatives.

Every transaction state should therefore be set to the appropriate category:

APPROVESafe transaction.
REVIEWSuspicious transaction, not confirmed fraud yet.
DECLINEConfirmed fraudulent transaction.

You can also create categories of fraud reasons in the Machine Learning section of your Settings page, which support the Label API (e.g. chargeback, bonus abuser, or postback data from payment: authorized, lost or stolen, etc.).


Please jump to the Machine Learning part to learn more about how SEON's ML module can help to fine-tune its algorithm.

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