<div class="csl-bib-body">
<div class="csl-entry">Gerharter, C. (2019). <i>Deep learning in life insurance risk prediction</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.60045</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2019.60045
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/11451
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dc.description
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
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dc.description.abstract
This diploma thesis deals with the implementation of an artificial neural network to predict life insurance risks. At first, general terms of deep learning are declared and defined. A brief insight into life insurance risk, specifically into the crucial parameter, the probability of dying, is given. Consequently, the structure of a deep neural network, the different activation functions and optimisers are explained in detail. This also includes a precise explanation of the training algorithm of a deep neural network. Finally, the calculation of the probability of dying is performed. Several experiments are carried out to test different scenarios for the neural network and the simulations are thoroughly analysed. In conclusion, the calculation of the probability of dying via an artificial neural network worked exceptionally well. A model with four hidden layers, overall 640 neurons, the Adam optimiser and either the ELU, TanH or Softplus activation function yielded by far the best results for this problem.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Lebensversicherungsrisiko
de
dc.subject
Künstliches neuronales Netz
de
dc.subject
Life insurance risk
en
dc.subject
artificial neural network
en
dc.title
Deep learning in life insurance risk prediction
en
dc.title.alternative
Deep Learning für die Vorhersage von Lebensversicherungsrisiken
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2019.60045
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Caroline Gerharter
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E105 - Institut für Stochastik und Wirtschaftsmathematik