Findings from A. Bayerstadler and Co-Researchers in the Area of Health Insurance Described (Bayesian multinomial latent variable modeling for fraud…

Findings from A. Bayerstadler and Co-Researchers in the Area of Health Insurance Described (Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance)

By a News Reporter-Staff News Editor at Managed Care Weekly Digest -- Current study results on Health Insurance have been published. According to news reporting originating in Munich, Germany, by NewsRx journalists, research stated, "Healthcare fraud and abuse are a serious challenge to healthcare payers and to the entire society. This article presents a predictive model for fraud and abuse detection in health insurance based on a training dataset of manually reviewed claims."

The news reporters obtained a quote from the research, "The goal of the analysis is to predict different fraud and abuse probabilities for new invoices. The prediction is based on a wide framework of fraud and abuse reports which examine the behavior of medical providers and insured members by measuring systematic deviation from usual patterns in medical claims data. We show that models which directly use the results of the reports as model covariates do not exploit the full potential in terms of predictive quality. Instead, we propose a multinomial Bayesian latent variable model which summarizes behavioral patterns in latent variables, and predicts different fraud and abuse probabilities. The estimation of model parameters is based on a Markov Chain Monte Carlo (MCMC) algorithm using Bayesian shrinkage techniques."

According to the news reporters, the research concluded: "The presented approach improves the identification of fraudulent and abusive claims compared to different benchmark approaches."

For more information on this research see: Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance. Insurance Mathematics & Economics, 2016;71():244-252. Insurance Mathematics & Economics can be contacted at: Elsevier Science Bv, PO Box 211, 1000 Ae Amsterdam, Netherlands (see also Health Insurance).

Our news correspondents report that additional information may be obtained by contacting A. Bayerstadler, Munich Re, Munich Hlth, D-80802 Munich, Germany. Additional authors for this research include L. van Dijk and F. Winter.

Keywords for this news article include: Munich, Germany, Europe, Health Insurance, Legal Issues.

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