Here we will demonstrate how CABAM can be used to identify outliers in transactional data sets. These outliers may have come as a result of be finger trouble that hasn’t been picked up yet or fraudulent transactions. Additionally, there could be shifts in customer behavior which have gone unnoticed.

Let’s say we have a supplier account that we are making payments to over a specific period of time. If the average amount for this period is $45, CABAM can be set using the Outliers function to flag any amount over (or below) an amount which is specified by the user. For example – anything above the $80 range. This range can be moved and specified by the CABAM Master User.