Augmented Neural Networks for Modelling Consumer Indebtness

TL;DR

This paper demonstrates that neural networks, enhanced with data mining insights, outperform traditional statistical models in capturing the complex factors influencing consumer indebtness, offering a flexible and extendable modeling framework.

Contribution

It introduces a novel neural network-based approach that integrates data mining results to better model consumer indebtness compared to linear regression.

Findings

Neural networks outperform linear regression in modeling consumer indebtness.

Incorporating data mining results improves neural network performance.

The proposed framework is adaptable to other real-world applications.

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 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.