Addressing imbalanced insurance data through zero-inflated Poisson regression with boosting

<?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 a2200000   4500</leader>
    <controlfield tag="001">MAP20210005442</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20220911190203.0</controlfield>
    <controlfield tag="008">210218e20210101bel|||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="100" ind1=" " ind2=" ">
      <subfield code="0">MAPA20210003035</subfield>
      <subfield code="a">Lee, Simon C.K. </subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="0">
      <subfield code="a">Addressing imbalanced insurance data through zero-inflated Poisson regression with boosting</subfield>
      <subfield code="c">Simon C.K. Lee</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">A machine learning approach to zero-inflated Poisson (ZIP) regression is introduced to address common difficulty arising from imbalanced financial data. The suggested ZIP can be interpreted as an adaptive weight adjustment procedure that removes the need for post-modeling re-calibration and results in a substantial enhancement of predictive accuracy. Notwithstanding the increased complexity due to the expanded parameter set, we utilize a cyclic coordinate descent optimization to implement the ZIP regression, with adjustments made to address saddle points. We also study how various approaches alleviate the potential drawbacks of incomplete exposures in insurance applications. The procedure is tested on real-life data.We demonstrate a significant improvement in performance relative to other popular alternatives, which justifies our modeling techniques.</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20090041721</subfield>
      <subfield code="a">Distribución Poisson-Beta</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080592059</subfield>
      <subfield code="a">Modelos predictivos</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080602437</subfield>
      <subfield code="a">Matemática del seguro</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20170005476</subfield>
      <subfield code="a">Machine learning</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20077000420</subfield>
      <subfield code="t">Astin bulletin</subfield>
      <subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
      <subfield code="x">0515-0361</subfield>
      <subfield code="g">01/01/2021 Volumen 51 Número 1 - enero 2021 , p. 27-55</subfield>
    </datafield>
  </record>
</collection>