
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/">
  <dc:source>Innovations in insurance : from traditional to modern market</dc:source>
  <dc:source>startpage: 367</dc:source>
  <dc:source>endpage: 384</dc:source>
  <dc:date>2025</dc:date>
  <dc:publisher>University of Belgrade, Faculty of economics and business, Publishing centre</dc:publisher>
  <dc:language>eng</dc:language>
  <dc:format>application/pdf</dc:format>
  <dc:format>391983 bytes</dc:format>
  <dc:type>info:eu-repo/semantics/bookPart</dc:type>
  <dc:subject xml:lang="eng">Key words: insurance market, Republika Srpska, AI, machine learning</dc:subject>
  <dc:description xml:lang="eng">The rapid evolution of digital financial transactions and insurance operations has
significantly increased the reliance on machine learning for predictive modelling.
The application of sophisticated machine learning techniques, including feature
transformation, data balancing, and model optimisation, enabled the detection of
anomalies in financial systems and claim predictions in the insurance sector.
Assuming that artificial intelligence (AI) can contribute to the improvement of
the actuarial profession in the Republic of Srpska, this chapter of the monograph
will, along with discussing its application in predicting claims and assessing
insurance risk, also present the prerequisites that artificial intelligence needs to
fulfil to be utilised in the insurance market of the Republic of Srpska while
following ethical standards and actuarial practice guidelines. Our aim is to
explore the potential impacts of artificial intelligence on the actuarial profession,
analysing how actuaries can use AI tools and techniques to enhance their work
and competencies and, thus, provide benefits to policyholders, insurers, and the
development of this profession. </dc:description>
  <dc:description xml:lang="eng">This research is supported by the Ministry of Scientific and Technological
Development and Higher Education of the Republika Srpska under the
Agreement on Co-financing of the Scientific and Research Project, No.
19.032/961-46/24 of 30. December 2024.</dc:description>
  <dc:creator id="https://plus.cobiss.net/cobiss/sr/sr/conor/6610279">Mitrašević, Mirela</dc:creator>
  <dc:creator id="https://orcid.org/0009-0008-9198-2770 https://plus.cobiss.net/cobiss/sr/sr/conor/118874121">Bradić, Kristina</dc:creator>
  <dc:creator id="https://plus.cobiss.net/cobiss/sr/sr/conor/15746151">Tešić, Nataša</dc:creator>
  <dc:identifier>https://phaidrabg.bg.ac.rs/o:36302</dc:identifier>
  <dc:identifier>cobiss:170430729</dc:identifier>
  <dc:identifier>ISBN: 978-86-403-1879-2</dc:identifier>
  <dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode</dc:rights>
  <dc:title xml:lang="eng">Challenges in applying machine learning for predictive modelling</dc:title>
</oai_dc:dc>
