Abstract: Transaction-based behavioral profiling, whereby the entity to be profiled is represented by a stream of transactions, is required in a variety of data mining and predictive modeling applications. An approach is described for assessing inconsistency in the activity of an entity, as a way of detecting fraud and abuse, using service-code information available on each transaction. Inconsistency is based on the concept that certain service-codes naturally co-occur more than do others. An assessment is made of activity consistency looking at the overall activity of an individual entity, as well as looking at the interaction of entities. Several approaches for measuring consistency are provided, including one inspired by latent semantic analysis as used in text analysis. While the description is in the context of fraud detection in healthcare, the techniques are relevant to application in other industries and for purposes other than fraud detection.

Abstract: Transition probability sequencing models and metrics are derived from healthcare claims data to identify potentially fraudulent or abusive practices, providers, doctors, clients, or other entities. Healthcare reimbursement claims from hospitals, skilled nursing facilities, doctors, etc., are processed to identify sequences of states, and transition probability metrics are determined from frequency information pertaining to the states. The metrics can these be further analyzed in predictive or rule based models, or other tools.

Abstract: Transition probability sequencing models and metrics are derived from healthcare claims data to identify potentially fraudulent or abusive practices, providers, doctors, clients, or other entities. Healthcare reimbursement claims from hospitals, skilled nursing facilities, doctors, etc., are processed to identify sequences of states, and transition probability metrics are determined from frequency information pertaining to the states. The metrics can these be further analyzed in predictive or rule based models, or other tools.

Abstract: A computer-implemented approach for processing benefits payment claims for prescription medicine, with these operations. Receiving pending pharmacy benefits payment claims submitted for payment by a pharmacy benefits claims payor, each claim specifying a patient. For each claim and its specified patient, performing operations including the following. Performing computer-driven statistical analysis of predefined aspects of one of the following in relation to a compiled history of past claims paid by one or more pharmacy benefits claims payors: claims history for the patient, the claim, medical history of the patient. Generating an indicator of predicted legitimacy by scoring results of the statistical analysis. Providing an output of at least one of the following: the indicator, payment advice prepared by applying predefined criteria to data including the indicator.