Markov model with machine learning integration for fraud detection in health insurance

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<rdf:Description>
<dc:date>2022</dc:date>
<dc:description xml:lang="es">Sumario: Fraud has led to a huge addition of expenses in health insurance sector in India. The work is aimed to provide methods applied to health insurance fraud detection. The work presents two approaches - a markov model and an improved markov model using gradient boosting method in health insurance claims. The dataset 382,587 claims of which 38,082 claims are fraudulent. The markov based model gave the accuracy of 94.07% with F1-score at 0.6683. However, the improved markov model performed much better in comparison with the accuracy of 97.10% and F1-score of 0.8546. It was observed that the improved markov model gave much lower false positives compared to markov model</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/181182.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Fraude en el seguro</dc:subject>
<dc:subject xml:lang="es">Seguro de salud</dc:subject>
<dc:subject xml:lang="es">Modelos actuariales</dc:subject>
<dc:subject xml:lang="es">Modelo de Markov</dc:subject>
<dc:subject xml:lang="es">Reclamaciones</dc:subject>
<dc:subject xml:lang="es">India</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Markov model with machine learning integration for fraud detection in health insurance</dc:title>
<dc:relation xml:lang="es">En: arXiv.- New York : Cornell University. - February 11, 2021 ; 6 p.</dc:relation>
<dc:coverage xml:lang="es">India</dc:coverage>
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