Fair Isaac Corporation

Michael K Tyler

Anu Kumar Pathria

Jean De Traversay

Nallan C Suresh

Krassimir G Ianakiev

Hyma Gollamudi

Shafi Ur Rahman

Andrea L Allmon

Nitin Basant

Moiz Saifee

Nitan Basant

Robin P

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

Data is received that characterizes a plurality of procedures for a single event. Thereafter, one or more dynamically determined groups are associated with the plurality of procedures, the dynamically determined groups being generated from similarity metrics derived from a plurality of historical procedures for a plurality of historical events. A likelihood of the plurality of procedures being associated with the single event can then be determined based on the associated one or more dynamically determined groups. This determined likelihood can be used to determine whether the data is indicative of fraud. Related apparatus, systems, techniques and articles are also described.

Characterizing relationships among procedures using similarity metrics

Various techniques are described that enable a smaller insurer (or an insurer with a less developed dataset) to be able to characterize whether certain healthcare insurance claim elements are potentially fraudulent or erroneous. Datasets from larger insurers (with well developed datasets) and/or datasets from a consortium of insurers can be leverage by the smaller insurer. Related techniques, apparatus, systems, and articles are also described.

Healthcare insurance claim fraud detection using datasets derived from multiple insurers

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.

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San Diego, CA, United States of America

Michael K Tyler