Lucent Technologies Inc.

Michael H Cahill

Diane Lambert

Gerald Donald Baulier

Virginia Kay Ferrara

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.

Automated fraud management in transaction-based networks

Fraud losses in a communication network are substantially reduced by automatically and selectively invoking one or more authentication measures based on a fraud score that indicates the likelihood of fraud for that particular call or previously scored calls. By selectively invoking authentication on only those calls that are suspected or confirmed to be fraudulent, fraud prevention can be achieved in a way that both reduces fraud losses and minimizes disruptions to legitimate subscribers. Using telecommunication fraud as an example, a subscriber is registered in a system by collecting data on that subscriber based on the particular authentication method being used, such as shared knowledge (e.g., passwords), biometric validation (e.g., voice verification), and the like. Once registered, the authentication function for the subscriber's account is activated and subsequent calls are then scored for the likelihood of fraud during the call setup request phase. Fraud scoring estimates the probability of fraud for each call based on the learned behavior of an individual subscriber and the learned behavior of fraud perpetrators. If fraud is not suspected based on the fraud score, then normal call processing can resume without the need for authentication. If fraud is suspected based on the fraud score, then the system automatically invokes authentication. If authentication indicates suspicion of fraud, e.g., voice prints do not match, then the call may be either be blocked or referred for other appropriate prevention measures, e.g., intercepted by an operator. If fraud is not indicated by authentication, then normal call processing may resume.

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New Providence, NJ, United States of America

Michael H Cahill