Publicação

Application of neural networks to the detection of fraud in workers’ compensation insurance : application to a Portuguese insurer

Ver documento

Detalhes bibliográficos
Resumo:Insurance relies on a complex trust-based relationship in which a policyholder pays in advance to be protected in the future. In Portugal, workers’ compensation insurance is mandatory which may restrict the course of action of both players. Insurers face significant losses, not only due to its core business, but also due to the swindles of claimants and policyholders. Insureds may not have in the market what they really want to acquire which may encourage fraudulent actions. Traditional fraud detection methods are no longer adequately protecting institutions in a world with increasingly sophisticated fraud techniques. This work focuses on creating an artificial neural network which will learn with insurance data and evolve continuously over time, anticipating fraudulent behaviours or actors, and contribute to institutions risk protection strategies.
Autores principais:Oliveira, Inês Bruno de
Assunto:Risk Fraud Insurance Neural Networks
Ano:2018
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL
Descrição
Resumo:Insurance relies on a complex trust-based relationship in which a policyholder pays in advance to be protected in the future. In Portugal, workers’ compensation insurance is mandatory which may restrict the course of action of both players. Insurers face significant losses, not only due to its core business, but also due to the swindles of claimants and policyholders. Insureds may not have in the market what they really want to acquire which may encourage fraudulent actions. Traditional fraud detection methods are no longer adequately protecting institutions in a world with increasingly sophisticated fraud techniques. This work focuses on creating an artificial neural network which will learn with insurance data and evolve continuously over time, anticipating fraudulent behaviours or actors, and contribute to institutions risk protection strategies.