Machine learning-powered liability determination in motor vehicle accidents

Detalhes bibliográficos
Autor(a) principal: Machatschek, Michael
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/118202
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
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spelling Machine learning-powered liability determination in motor vehicle accidentsMachine learningDeep learningNatural language ProcessingInsuranceInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe determination of liability in motor vehicle accidents is a complex problem influenced by various dynamic factors. Frequently an initial assessment of liability must be made early in the claims settlement process. However, at this stage, decision-makers regularly find themselves in a situation with incomplete information, which is why a decision must be made under uncertainty. Machine learning can facilitate and improve these decision situations by learning from historical data and thereby generating decision criteria. This master thesis explores how the decision of liability determination in motor vehicle accidents can be improved by machine learning. To achieve this goal, this thesis analyzes to what extent can a machine learning model predict the liability constellation in motor vehicle accidents. An adapted variant of the CRISP-DM methodology was applied to achieve the thesis goal. In the first step, the underlying business problem was examined, and the business goals of the desired solution were defined. In the second step, the data relevant to the solution was collected, processed, and analyzed. In the third step, concrete models were developed. Finally, the third step results were discussed and evaluated with the goals from the first step. The outcomes of this work are two artifacts. On the one hand, a model that is trained to predict the liability constellation. On the other hand, an analysis of how the problem can be solved using different machine learning techniques.António, Nuno Miguel da ConceiçãoRUNMachatschek, Michael2021-05-24T16:21:21Z2021-05-182021-05-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/118202TID:202727505engmetadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:01:08Zoai:run.unl.pt:10362/118202Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:47.608621Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Machine learning-powered liability determination in motor vehicle accidents
title Machine learning-powered liability determination in motor vehicle accidents
spellingShingle Machine learning-powered liability determination in motor vehicle accidents
Machatschek, Michael
Machine learning
Deep learning
Natural language Processing
Insurance
title_short Machine learning-powered liability determination in motor vehicle accidents
title_full Machine learning-powered liability determination in motor vehicle accidents
title_fullStr Machine learning-powered liability determination in motor vehicle accidents
title_full_unstemmed Machine learning-powered liability determination in motor vehicle accidents
title_sort Machine learning-powered liability determination in motor vehicle accidents
author Machatschek, Michael
author_facet Machatschek, Michael
author_role author
dc.contributor.none.fl_str_mv António, Nuno Miguel da Conceição
RUN
dc.contributor.author.fl_str_mv Machatschek, Michael
dc.subject.por.fl_str_mv Machine learning
Deep learning
Natural language Processing
Insurance
topic Machine learning
Deep learning
Natural language Processing
Insurance
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2021
dc.date.none.fl_str_mv 2021-05-24T16:21:21Z
2021-05-18
2021-05-18T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/118202
TID:202727505
url http://hdl.handle.net/10362/118202
identifier_str_mv TID:202727505
dc.language.iso.fl_str_mv eng
language eng
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