This page explains the payment fraud integration process.
Our dedicated guides will help you to integrate with Ravelin seamlessly.
Ravelin team will help you through the integration from start to finish.
We will work with you to understand how your platform and Ravelin can best work together
to stop payment fraud, optimise conversion and support growth.
During the initial sales meetings, we will:
Develop a deep understanding of your checkout and payment flow.
Assess how your business can get the most out of Ravelin.
Share information from initial discovery calls with our integrations team.
A dedicated project manager and integration engineer will be assigned to support you and will
organise a kick off meeting with you. We will discuss technical details of the
project, target outcomes and any time constraints you may have.
The fields required by the API requests and how they map to your data.
Implementation requirements for the web and mobile SDKs.
Current payment fraud trends.
Your existing internal processes to counter payment fraud.
What evidence you have about confirmed cases of payment fraud.
The actions you will be taking based on our recommendations.
Go live approach.
After the kick off meeting we will focus on implementation.
This means building the integration on your side and sending test data to your sandbox account.
Establish how best to work with you and your team during the integration.
Answer any questions you or your engineers have.
Raise any integration issues.
Ensure a clear feedback cycle for testing and sign off of the integration.
At this stage, as part of on-boarding, you will be assigned a dedicated client support agent,
investigator and data scientist:
Your client support agent will provide training to members of your team.
Your investigator will monitor performance and recommend any changes.
Your data scientist will train your custom machine learning model.
Once we are happy with the data we are seeing in your sandbox account,
we can begin the go live process.
Assess your traffic and add any appropriate rules for your production environment.
Train your custom payment fraud machine learning models.
Enable your production keys and agree a suitable time for you to start sending production data.
Following the go live, we will closely monitor the data to ensure there are no surprises and will arrange regular
calls to discuss performance and any trends that our teams have identified.
We will continue to make adjustments to maximize performance as needed.