Dijous 11 de juny, el Dipòsit Digital no estarà operatiu de 15:00 a 17:00 h per tasques de manteniment. Disculpeu les molèsties.
El jueves 11 de Junio, el Dipòsit Digital no estará operativo de 15:00 a 17:00 h debido a tareas de mantenimiento. Disculpen las molestias.
Thursday, Jun 11th, the Digital Repository will be unavailable due to a system update.

Files

TFM-CAF_ValleNofuentes_2021.pdf (3.01 MB)

Document type

Master thesis

Publication date

2021

Publication license

cc-by-nc-nd (c) Valle Nofuentes, 2021
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/178720

Automatic Machine Learning for Insurance: H2O Experiment

Journal Title

Authors

Director/Tutor

Journal ISSN

Volume Title

Related resource

Abstract

This thesis provides an introduction of machine learning (ML), shows the implication that ML has on the insurance sector and takes a special consideration to the H2O ensemble modelling approach for the insurance claim fraud detection binary classification. The aim of this thesis is to study the H2O Automatic ML potential and compare the results generated with traditional algorithms such as lineal perceptron, Logistic Regression, multilayer perceptron, support vector machine and decision tree. Using H2O web interface or R programming, not only the most efficient ML algorithms are obtained with no effort but also provide better modelling metrics than traditional methods.

Description

Treballs Finals del Màster de Ciències Actuarials i Financeres, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2020-2021, Tutor: Dr. Salvador Torra Porras

Subject

Aprenentatge automàtic, Companyies d'assegurances, Sistema binari (Matemàtica), Treballs de fi de màster

Subject (English)

Machine learning, Insurance companies, Binary system (Mathematics), Master's theses

Citation

Collections

Màster Oficial - Ciències Actuarials i Financeres (CAF)

Citation

VALLE NOFUENTES, Samuel. Automatic Machine Learning for Insurance: H2O Experiment. [consulted: 11 of June of 2026]. Available at: https://hdl.handle.net/2445/178720

Export metadata

JSON - METS

Share record