Search

Modeling Malicious Hacking Data Breach Risks

<?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">MAP20220000673</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20220114125731.0</controlfield>
    <controlfield tag="008">220114e20211206esp|||p      |0|||b|spa 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="1" ind2=" ">
      <subfield code="0">MAPA20220000116</subfield>
      <subfield code="a">Sun, Hong</subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="0">
      <subfield code="a">Modeling Malicious Hacking Data Breach Risks</subfield>
      <subfield code="c">Hong Sun, Maochao Xu, Peng Zhao</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">Malicious hacking data breaches cause millions of dollars in financial losses each year, and more companies are seeking cyber insurance coverage. The lack of suitable statistical approaches for scoring breach risks is an obstacle in the insurance industry. We propose a novel frequencyseverity model to analyze hacking breach risks at the individual company level, which would be valuable for underwriting purposes. We find that breach frequency can be modeled by a hurdle Poisson model, which is different from the negative binomial model used in the literature. The breach severity shows a heavy tail that can be captured by a nonparametric- generalized Pareto distribution model. We further discover a positive nonlinear dependence between frequency and severity, which our model also accommodates. Both the in-sample and out-of-sample studies show that the proposed frequencyseverity model that accommodates nonlinear dependence has satisfactory performance and is superior to the other models, including the independence frequencyseverity and Tweedie models.

</subfield>
    </datafield>
    <datafield tag="700" ind1=" " ind2=" ">
      <subfield code="0">MAPA20120013339</subfield>
      <subfield code="a">Xu, Maochao</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20190008785</subfield>
      <subfield code="a">Zhao, Peng</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20077000239</subfield>
      <subfield code="g">06/12/2021 Tomo 25 Número 4 - 2021 , p. 484-502</subfield>
      <subfield code="x">1092-0277</subfield>
      <subfield code="t">North American actuarial journal</subfield>
      <subfield code="d">Schaumburg : Society of Actuaries, 1997-</subfield>
    </datafield>
  </record>
</collection>