Automated fraud management in transaction-based networks

Abstract

Fraud losses in a communication network are substantially reduced by automatically generating fraud management recommendations in response to suspected fraud and by deriving the recommendations as a function of selected attributes of the fraudulent activity. More specifically, a programmable rules engine automatically generates recommendations based on call-by-call fraud scoring so that the recommendations correspond directly to the type and amount of suspected fraudulent activity. Using telecommunications fraud as an example, an automated fraud management system receives call detail records that have been previously scored to identify potentially fraudulent calls. Fraud scoring estimates the probability of fraud for each call based on the learned behavior of an individual subscriber as well as that of fraud perpetrators. Scoring also provides an indication of the contribution of various elements of the call detail record to the fraud score for that call. A case analysis is initiated and previously scored call detail records are separated into innocuous and suspicious groups based on fraud scores. Each group is then characterized according to selected variables and scoring for its member calls. These characterizations are combined with subscriber information to generate a set of decision variables. A set of rules is then applied to determine if the current set of decision variables meets definable conditions. When a condition is met, prevention measures associated with that condition are recommended for the account. As one example, recommended prevention measures may be automatically implemented via provisioning functions in the telecommunications network.