Conference Proceedings

Augmented Neural Networks for Modelling Consumer Indebtness

Alexandros Ladas, Jon Garibaldi, Rodrigo Scarpel, Uwe Aickelin

PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | IEEE | Published : 2014

Abstract

Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models. In this work we show that Computational Intelligence can offer a more holistic approach that is more suitable for the complex relationships an indebtness dataset has and Linear Regression cannot uncover. In particular, as our results show, Neural Networks achieve the best performance in modelling consumer indebtness, especially when they manage to incorporate the significant and experimentally verified results of the Data Mining process in the model, exploiting the ..

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University of Melbourne Researchers