<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0012-7353</journal-id>
<journal-title><![CDATA[DYNA]]></journal-title>
<abbrev-journal-title><![CDATA[Dyna rev.fac.nac.minas]]></abbrev-journal-title>
<issn>0012-7353</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0012-73532022000200161</article-id>
<article-id pub-id-type="doi">10.15446/dyna.v89n221.100070</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Technological incident classification model from a machine learning approach in insurance services]]></article-title>
<article-title xml:lang="es"><![CDATA[Modelo de clasificación de incidentes tecnológicos desde un enfoque de aprendizaje automático en servicios de seguros]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gómez-Jaramillo]]></surname>
<given-names><![CDATA[Paola Andrea]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Echavarría]]></surname>
<given-names><![CDATA[Favián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez-Rave]]></surname>
<given-names><![CDATA[Jorge Iván]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Antioquia  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Antioquia Departamento de Ingeniería Industrial ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,IDINNOVS.A.S  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>89</volume>
<numero>221</numero>
<fpage>161</fpage>
<lpage>167</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0012-73532022000200161&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0012-73532022000200161&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0012-73532022000200161&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Managing technological incidents in insurance companies requires a correct and timely assignment of these to the problem-solving teams. Classifying these incidents by humans demands time and knowledge and is frequently executed erroneously. This paper addresses this classification problem from a machine learning approach. The performance of five supervised learning methods (logistic regression, classification trees, random forest, discriminant linear analysis, support vector machines) is compared in three scenarios of inclusion of predictors: structured, texts, and both. The use of unstructured variables considerably improves the accuracy of the models (e.g., Random Forest, validation sample: 0.709 using structured data; 0.881 using text data). Moreover, considering the practical implications of the human correct classification rate (66%) vs. machine (88%, Random Forest, SVM, or linear regression), the machine favors resource-saving in the organization. This article is a successful case of machine learning in the insurance industry.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La gestión de incidentes tecnológicos en las compaiiías de seguros requiere una asignación conecta y oportuna de estos a los equipos de resolución de problemas. La clasificación de tales incidentes por humanos demanda tiempo y conocimiento y, con frecuencia, se ejecuta de manera errónea. Este documento aborda este problema de clasificación desde un enfoque de aprendizaje automático. Se compara el desempeito de cinco métodos de aprendizaje supervisado (regresión logística, árboles de clasificación, bosque aleatorio, análisis lineal discriminante y máquinas de vectores de apoyo) en tres escenarios de inclusión de predictores: estructurado, textos y ambos. El uso de variables no estructuradas mejora considerablemente la exactitud de los modelos (ej., Random Forest, muestra de validación: 0,709 con datos estructurados; 0,881 con datos de texto). Además, considerando las implicaciones prácticas de la tasa de clasificación humana conecta (66%) frente a la máquina (88%, Random Forest, SVM o regresión logística), la máquina favorece el ahorro de recursos en la organización. Este artículo es un caso exitoso del aprendizaje automático en la industria de seguros.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[classification of incidents]]></kwd>
<kwd lng="en"><![CDATA[technology incidents]]></kwd>
<kwd lng="en"><![CDATA[insurance]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="es"><![CDATA[clasificación de incidentes]]></kwd>
<kwd lng="es"><![CDATA[incidentes tecnológicos]]></kwd>
<kwd lng="es"><![CDATA[seguros]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>[1]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Al-Hawari]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Barhamb]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A machine learning based help desk system for IT service management]]></article-title>
<source><![CDATA[Journal of King Saud University -Computer and Information Sciences]]></source>
<year>2019</year>
<volume>33</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>702-18</page-range></nlm-citation>
</ref>
<ref id="B2">
<label>[2]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Giurgiu]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Wiesmann]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Bogojeska]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Lanyi]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Stark]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Wallace]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Pereira]]></surname>
<given-names><![CDATA[M.M]]></given-names>
</name>
<name>
<surname><![CDATA[Hidalgo]]></surname>
<given-names><![CDATA[A.A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[On the adoption and impact of predictive analytics for server incident reduction]]></article-title>
<source><![CDATA[IBM Journal of Research and Development]]></source>
<year>2017</year>
<volume>61</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>9-98</page-range></nlm-citation>
</ref>
<ref id="B3">
<label>[3]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Paramesh]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Shreedhara]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automated IT service desk systems using machine learning techniques]]></article-title>
<source><![CDATA[Data analytics and learning]]></source>
<year>2019</year>
<page-range>331-46</page-range><publisher-loc><![CDATA[Singapore ]]></publisher-loc>
<publisher-name><![CDATA[Springer]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B4">
<label>[4]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sulaman]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Weyns]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Höst]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Identification of IT incidents for improved risk analysis by using machine learning]]></source>
<year>2015</year>
<volume>41</volume>
<conf-name><![CDATA[ Euromicro Conference on Software Engineering and Advanced Applications]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1-5</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>[5]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jan]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Ayachitula]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Ni]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<source><![CDATA[A statistical machine learning approach for ticket mining in IT service delivery]]></source>
<year>2013</year>
<conf-name><![CDATA[ International Symposium on Integrated Network Management (IM 2013)]]></conf-name>
<conf-date>2013</conf-date>
<conf-loc> </conf-loc>
<page-range>541-6</page-range></nlm-citation>
</ref>
<ref id="B6">
<label>[6]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Son]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Hazlewood]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Peterson]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<source><![CDATA[On automating XSEDE user ticket classification]]></source>
<year>2014</year>
<volume>14</volume>
<page-range>1-7</page-range><publisher-name><![CDATA[National Institute for Computational Sciences University of Tennessee]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B7">
<label>[7]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bogojeska]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Stöckel]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Zazzi]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Kaiser]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[History-alignment models for bias-aware prediction of virological response to HIV combination therapy]]></article-title>
<source><![CDATA[Journal of Machine Learning Research]]></source>
<year>2012</year>
<volume>22</volume>
<page-range>118-26</page-range></nlm-citation>
</ref>
<ref id="B8">
<label>[8]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Maksai]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Bogojeska]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Wiesmann]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Hierarchical incident ticket classification with minimal supervision]]></source>
<year>2014</year>
<conf-name><![CDATA[ International Conference on Data Mining]]></conf-name>
<conf-loc> </conf-loc>
<page-range>923-8</page-range></nlm-citation>
</ref>
<ref id="B9">
<label>[9]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Akbar]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Jianglei]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<source><![CDATA[Vertical domain text classification: towards understanding IT tickets using deep Neural Networks]]></source>
<year>2018</year>
<conf-name><![CDATA[ Thirty-SecondConference on Artificial Intelligence]]></conf-name>
<conf-loc> </conf-loc>
</nlm-citation>
</ref>
<ref id="B10">
<label>[10]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Silva]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Pereira]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Ribeiro]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<source><![CDATA[Machine learning in incident categorization automation]]></source>
<year>2018</year>
<volume>13</volume>
<conf-name><![CDATA[ Iberian Conference on Information Systems and Technologies]]></conf-name>
<conf-loc> </conf-loc>
<publisher-name><![CDATA[CISTI]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B11">
<label>[11]</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Altintas]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Cuneyd]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Machine learning based ticket classification in issue tracking systems]]></source>
<year>2014</year>
</nlm-citation>
</ref>
<ref id="B12">
<label>[12]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kallis]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Di Sorbo]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Canfora]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Panichella]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Ticket tagger machine learning driven]]></source>
<year>2019</year>
<conf-name><![CDATA[ International Conference on Software Maintenance and Evolution]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1-4</page-range><publisher-name><![CDATA[ICSME]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B13">
<label>[13]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gore]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Daillo]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Padilla]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Ezell]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Assessing cyber-incidents using machine learning]]></article-title>
<source><![CDATA[International Journal of Information and Computer Security]]></source>
<year>2018</year>
<volume>10</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>341-60</page-range><publisher-name><![CDATA[IJICS]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B14">
<label>[14]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhan]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<source><![CDATA[Machine learning methodology for enhacing automated process in IT incident management]]></source>
<year>2012</year>
<conf-name><![CDATA[ Symposium on Network Computing and Applications]]></conf-name>
<conf-loc> </conf-loc>
</nlm-citation>
</ref>
<ref id="B15">
<label>[15]</label><nlm-citation citation-type="">
<source><![CDATA[]]></source>
<year></year>
</nlm-citation>
</ref>
<ref id="B16">
<label>[16]</label><nlm-citation citation-type="">
<source><![CDATA[cran.r-project.org, Package 'caret']]></source>
<year>2021</year>
</nlm-citation>
</ref>
<ref id="B17">
<label>[17]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Diao]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Jamjoom]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Loewenstern]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Rule-based problem classification in IT service management]]></source>
<year>2009</year>
<conf-name><![CDATA[ International Conference on Cloud Computing]]></conf-name>
<conf-date>2009</conf-date>
<conf-loc>New York </conf-loc>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
