CentralPay relies on a fraud detection service based on machine learning algorithms.
This predictive engine is made from a large sample of data provided by CentralPay in JSON format and from TRANSACTION / REFUND / DISPUTE data.
This service is based on a behavioral classification related to the merchant's business line.
The engine returns an action and a score.
The action invites the payment service to accept or deny the transaction.
The score, classifies the level of risk by providing a percentage of probability of fraud. This score is then interpreted in the rule engine.
The score allows the merchant and the algorithm to interact together to improve themselves.
The scores are classified as follows:
From 0 to 19 = low risk
No carried out action
From 20 to 59 = average risk
Action: send an event with score details for manual review and learning
+60 = high risk
Action: send an event with score details for manual reviewing and learning
This fraud exposure analysis service analyzes the exposure context of the fraud risk of each transaction. This service returns a score that allows to process automatically the expected response in the rule engine.
The score is based on the cross-analysis of the following data:
- IP Risk Index
- Proxy Detection
- Network Detection TOR
- IP address Checking
- Confidence Factors
- Email Checking
- Address & Phone Checking
- High-risk shipping address
- Geolocation of IP addresses
- Identification of the equipment used
- Email Address
- Browser Type
- Country Inconsistencies
- Distance from the shipping address
- Distance from the billing address
- Email Domain
- Amount of the order
- Telephone Number
- IP Holder
- E-mail Holder
- CB address Checking