  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 flexibility Neural Networks offer in designing their topology.
This novel method forms an elaborate framework to model Consumer indebtness
that can be extended to any other real world application.
