Each business is unique. Fraud models can be trained using data from historic transactions, black/white lists and other data that is specific to your business. This could also include non-transactional data like SKU, employee lists, POS data etc.
Track user activity
Track User device and browser information
In addition to receiving transactional data for fraud scoring, Feedzai also helps merchants track behavior before and after login to detect and prevent identity fraud. Integrating with Feedzai’s device fingerprinting technology is simple and can be invoked by embedding code snippet in all the webpages where customer interactions are expected.
Score transaction in real time
Feedzai can help you risk score at any point in the transaction journey, either in the pre- or post-payment authorization flow. Once the customer completes the order, the transaction is sent to Feedzai through the main scoring API call. Depending on the score and recommendation provided by Feedzai, you can choose to accept or decline the transaction.
Send your feedback to Feedzai
Feedback cycle is a critical step in a fraud prevention process. The more feedback the models receive, the more accurate they become and the more fraud they detect. When analysts make a decision using the alert manager, feedback about that particular order is automatically sent to Feedzai. However, when a future event on an order changes its state, there will be no alarm to trigger in real-time. This information can be still be transmitted to Feedzai manually in the alert manager interface or can be automated through an API.