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Version: 1.1

Integration guide

Client Onboarding Process

Welcome! We're excited to have you as a new client. This document will walk you through the key steps and requirements for onboarding with Fraudio.


The onboarding process generally follows these high-level stages:

  1. Initial Discovery and Setup
  2. Integration and Data Transfer
  3. Model Training and Evaluation
  4. Go Live and Monitoring

Throughout the process there will be ongoing communication and collaboration to ensure we fully understand your needs and that the integration goes smoothly.

1. Initial Discovery and Setup

Kickoff Meeting

Once the contract is signed, we'll schedule an introductory kickoff meeting/call. This meeting covers:

  • Introductions to key team members on both sides
  • Discussion of goals, requirements, and expectations
  • Overview of the onboarding process and timeline
  • Q&A

Information Gathering

After the kickoff, you'll work with your Customer Success Manager to provide additional details we need to proceed:

  • Data samples
    • Share sample data files or access to APIs so we can begin mapping to our schema and testing integration.
  • Use cases
    • Document the key fraud detection use cases you want to address. This focuses the modeling efforts.
  • Data schema
    • Provide overview of your data schema, formats, and any quirks.
  • Historical data
    • Share access to historical transaction data for model training.
  • Rules
    • If you have existing fraud detection rules, sharing these helps jumpstart configuration.

Environments and Access

We'll set up the following access and credentials:

  • Customer data API Test Environment - This secure environment contains your data for integration testing.
  • Model training API Test Environment - Where models are trained on your historical data.
  • Staging environment - Pre-production environment for final validation before go-live.
  • API keys - Credential for your systems to submit data into our platform.
  • Dashboards - Access to monitoring and analytics dashboards.

2. Integration and Data Transfer

The next step is connecting your systems to our platform via API and ensuring the data integration works properly.

Data Mapping

We'll work iteratively with your team to map your data schema into our standard schema. This mapping enables your data to work seamlessly with our models.

  • You provide sample data exports based on our template.
  • We analyze and map into our schema, work with you to clarify any unclear fields.
  • Iterate on the mapping with additional sample data until complete.

Connection and Testing

With schema mapping complete, we move on to live integration.

  • You configure your systems to submit data to our API endpoints.
  • Start submitting test data batches and transactions.
  • We validate successful ingestion, schema conformance, and data quality.
  • Troubleshoot and resolve any issues together.

Historical Data Transfer

Once the integration is working, we pull historical transaction data (past 6-12 months) into our model training environment. This powers more accurate model training.

  • You grant access to exports, databases, data warehouses, or S3 buckets.
  • We pull data into our secure cloud environment.
  • May require heavy data transfer if large volumes, which we accommodate.

3. Model Training and Evaluation

With data integration in place, our team gets to work on configuring and training the models on your data.

Training and Configuration

  • Models are trained on your historical transactions.
  • Key thresholds and hyperparameters are tuned based on your specific data patterns.
  • Rules and lists can be configured to meet your needs.
  • Iterative improvements based on model evaluation.

Performance Evaluation

We thoroughly evaluate model performance before go-live.

  • Data scientists analyze model metrics like fraud detection rate, false positives, precision, recall, etc.
  • Models run in parallel to production for comparison.
  • Assess models across key segments (geography, payment types, etc).
  • Review predictive performance with your team.


Once you are satisfied with model performance on historical data, we move to production deployment.

4. Go Live and Monitoring

With your approval, we transition the fully configured and tuned models into the live production environment.

Staged Deployment

Initial production release starts with a percentage of your traffic to monitor stability and performance.

  • 5% of transactions routed to new models.
  • Verify models performing as expected before ramping up.
  • Easy to rollback or pull traffic if any issues observed.

Ramp Up and Monitoring

With stability validated, we gradually ramp up traffic levels to 100% running through the new models.

  • Continuously monitor key metrics like uptime, latency, and throughput.
  • Review live traffic patterns against historical data.
  • Make minor optimizations or adjustments as needed.

Ongoing Collaboration

Our team remains engaged to ensure you get the most value from our platform.

  • Regular check-ins and performance reviews.
  • Support for additional integrations.
  • New model retraining and refinement.
  • Feature requests and platform feedback.

We're committed to a smooth onboarding process and long-term partnership. Please don't hesitate to reach out to your CSM with any questions!