Title: Deep learning in life insurance risk prediction
Other Titles: Deep Learning für die Vorhersage von Lebensversicherungsrisiken
Language: English
Authors: Gerharter, Caroline 
Qualification level: Diploma
Advisor: Rheinländer, Thorsten 
Issue Date: 2019
Number of Pages: 40
Qualification level: Diploma
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.
Keywords: Lebensversicherungsrisiko; Künstliches neuronales Netz
Life insurance risk; artificial neural network
URI: https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-132365
http://hdl.handle.net/20.500.12708/11451
Library ID: AC15536163
Organisation: E105 - Institut für Stochastik und Wirtschaftsmathematik 
Publication Type: Thesis
Hochschulschrift
Appears in Collections:Thesis

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