Fair Isaac Corporation

Jean De Traversay

Anu Kumar Pathria

Michael K Tyler

Nallan C Suresh

Krassimir G Ianakiev

Hyma Gollamudi

Louis S Biafore

Arati S Deo

Andrea L Allmon

Ho Ming Luk

Craig Nies

Fraud and abuse detection in an entity's payment coding practices includes the ability to search for fraud at all levels of the hierarchical coded payment system within the context of an unsupervised model. The model uses variables derived and profiles created at any level or at all levels of the hierarchical coded payment system to create a comprehensive description of the payment coding activities submitted by the entity. That description is compared with other peer entities to determine unusual and potentially inappropriate activity. The profiles created may themselves be utilized for purposes other than the detection of fraud and abuse.

Detection of upcoding and code gaming fraud and abuse in prospective payment healthcare systems

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.

Consistency modeling of healthcare claims to detect fraud and abuse

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.

Sequencing models of healthcare related states

A system, computer program product, and method provide for reason driven data analysis in a web-based system that is configurable to various types of data and environments. The computer program product provides for navigation of a report tree of reports that are hierarchically organized with respect to reasons derived from an analysis of transactional data in a database. A frame based interface provides for easy and flexible navigation and configuration of the reports and other features. A configuration file is used to rapidly develop and customize the web-based system. Case management functionality is included to allow for management of cases being reviewed using the system.

Webstation: configurable web-based workstation for reason driven data analysis

Computer implemented processes and software products generate profiles of entities, such as providers, clients, merchants and customers, and entities comprising interacting pairs of entities. The processes including deriving direct profiles from transaction data pertaining to an entity and enhancing the profile of one entity using the profile of another entity. Parallel and serial applications of the derive and enhance processes on various individual and multiple entites yields enhanced profiles that powerfully describe the interactions and relationship of the entities to each other, and between their members.

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Solana Beach, CA, United States of America

Jean De Traversay