Augmented Neural Networks for modelling consumer indebtness

Ladas, Alexandros; M. Garibaldi, Jonathan; Scarpel, Rodrigo; Aickelin, Uwe

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.

Citation

Ladas, A., M. Garibaldi, J., Scarpel, R., & Aickelin, U. (2014). Augmented Neural Networks for modelling consumer indebtness. Proceedings of International Joint Conference on Neural Networks, 3086-3093. https://doi.org/10.1109/IJCNN.2014.6889760

• Journal Article Type: Conference Paper
• Conference Name: 2014 International Joint Conference on Neural Networks (IJCNN)
• Conference Location: Beijing, China
• Start Date: Jul 6, 2014
• End Date: Jul 11, 2014
• Online Publication Date: Sep 4, 2014
• Publication Date: Sep 4, 2014
• Deposit Date: Sep 30, 2014
• Publicly Available Date: Sep 30, 2014
• Journal: Proceedings of International Joint Conference on Neural Networks
• Print ISSN: 2161-4393
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 3086-3093
• DOI: https://doi.org/10.1109/IJCNN.2014.6889760
• Keywords: Data Mining, Digital Economy, Neural Networks, Regression
• Public URL: https://nottingham-repository.worktribe.com/output/737026
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6889760
• Additional Information: Published in: 2014 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ : IEEE, 2014. (ISBN: 9781467347013), pp. 3086-3093, (doi: 10.1109/IJCNN.2014.6889760 ). © 2014 IEEE