The payment fraud score is the computed result of Ravelin’s machine learning analysis that that takes into account over 1000 different fraud signals.

The payment fraud score is a probability of how likely a customer is to be a fraudster. This means the higher the score, the more likely a person is to be a fraudster.

Viewing Payment Fraud Scores

You can view a customer’s score on the customer lists page or a customer’s profile.

There are 3 places where you can see the fraud score:

• On the customers list page in the SCORE column on the left side of the table
• Within the pop out customer list on the left side
• In the Overview on the client profile page

Using Payment Fraud Scores

With payment fraud scores you can prioritise the recommendation given to a customer based on the machine learning output. You can determine your score thresholds based on your tolerance for risk and bandwidth for manual review.

• Genuine scores: These users are likely good and don’t require review. Ravelin will tell your application to ALLOW the customer to place an order.
• Review scores: These customers need review. It is unclear if they are good or bad, you may need to challenge the order or dive into the customer profile and do a manual review. Ravelin will tell your application to REVIEW the customer’s order.
• Fraudster scores: These customers are likely bad and should be automatically blocked. Ravelin will tell your application to PREVENT the customer from placing an order.

Note: If you would like to update your scores thresholds, please contact [email protected].

Payment Fraud Score Contributions

We have grouped contributions so it's easy to understand why a customer has a given score.

• Identity - Looks at information about the customer. For example, the name, email or phone number.
• Orders - Looks at information relating to an order. This can include things like type of item ordered and transaction velocity.
• Payment Methods - Looks at the different payment methods the customer has used.
• Locations - Looks at the different locations associated with the customer. For example, billing address, delivery address and the customers location when completing the order.