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

<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
  <record>
    <leader>00000cab a22000004b 4500</leader>
    <controlfield tag="001">MAP20220029353</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20221025142548.0</controlfield>
    <controlfield tag="008">221025e2022    usa|| p      |0|||b|eng d</controlfield>
    <datafield tag="040" ind1=" " ind2=" ">
      <subfield code="a">MAP</subfield>
      <subfield code="b">spa</subfield>
      <subfield code="d">MAP</subfield>
    </datafield>
    <datafield tag="084" ind1=" " ind2=" ">
      <subfield code="a">6</subfield>
    </datafield>
    <datafield tag="245" ind1="0" ind2="0">
      <subfield code="a">Markov model with machine learning integration for fraud detection in health insurance</subfield>
      <subfield code="c">Rohan Yashraj Gupta... [et al.]</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">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</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080591052</subfield>
      <subfield code="a">Fraude en el seguro</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080573867</subfield>
      <subfield code="a">Seguro de salud</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080592011</subfield>
      <subfield code="a">Modelos actuariales</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080576783</subfield>
      <subfield code="a">Modelo de Markov</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080567118</subfield>
      <subfield code="a">Reclamaciones</subfield>
    </datafield>
    <datafield tag="651" ind1=" " ind2="1">
      <subfield code="0">MAPA20080663636</subfield>
      <subfield code="a">India</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="t">arXiv.- New York : Cornell University</subfield>
      <subfield code="g">February 11, 2021 ; 6 p.</subfield>
    </datafield>
  </record>
</collection>