Machine learning demystified
Adyen's RevenueProtect engine now supports machine learning based predictions for companies that want to participate in this exciting development for our risk engine.
Most machine learning systems work as a black box solution and only return a prediction for a given payment. Adyen's machine learning for RevenueProtect not only makes an autonomous decision, but also explains the reasoning behind it to you.
Our explainability toolbox
You can see the machine learning decisions on the Risk results page.
To open the risk results page:
- Log in to your live Customer Area.
- Go to Transactions > Payments.
- Select the Risk score for a payment from the payments overview to open the risk results page of that transaction.
The decision outcome for a certain payment is shown at the very top of the page, for example:
The decision outcome can be Looks fraudulent or Looks legitimate.
You can see an explanation of the reasoning behind the decision outcome in the Fraud Signal Analysis section.
RevenueProtect uses three basic elements to explain the decision outcome: the signal bar, the signal category, and the signal name.
The signal bar
The signal bar displays the deductive reasoning behind the model's decision for a given payment. Any payment will have positive and negative characteristics: the former moves the decision towards a 'fraud' classification, while the latter pushes the model's reasoning toward a 'legitimate' classification.
By hovering over the signal bar, you can assess which signal category contributed the most to a certain decision.
The marker indicates the cutoff for the decision outcome that was made at the time of the payment.
The signal category
Signal categories are a common set of payment properties that are grouped together to explain why a certain decision was taken for a payment.
Select a signal category to expand it into the signal names that contributed towards a 'fraud' or a 'legitimate' classification.
The signal name
A signal category consists of multiple signal names. Signal names are the basic unit of a payment's information. All the signal names for a payment are processed by our machine learning engine to produce a 'fraud' or 'legitimate' outcome from that payment.
We use the following design language to clarify a signal name’s contribution towards the outcome of the signal category:
Giving us feedback
Select Give feedback to help us improve by giving us feedback on any payment's fraud signal analysis explanation.
We review this data to constantly improve our machine learning models and explanations.