A data mining framework to model consumer indebtedness with psychological factors

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Ladas, Alexandros, Ferguson, Eamonn, Garibaldi, Jonathan M. and Aickelin, Uwe (2014) A data mining framework to model consumer indebtedness with psychological factors. In: IEEE International Conference on Data Mining: The Seventh International Workshop on Domain Driven Data Mining 2014 (DDDM 2014), 14 Dec 2014, Shenzhen, China.

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7022592

Abstract

Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consumer Indebtedness by examining the contribution of Psychological Factors, like Impulsivity to the analysis of Consumer Debt. Our results confirm the beneficial impact of Psychological Factors in modelling Consumer Indebtedness and suggest a new approach in analysing Consumer Debt, that would take into consideration more Psychological characteristics of consumers and adopt techniques and practices from Data Mining.

Item Type:	Conference or Workshop Item (Paper)
RIS ID:	https://nottingham-repository.worktribe.com/output/999055
Additional Information:	Published in: 2014 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2014, ISBN: 978-1-4799-4275-6. pp. 150-157, doi: 10.1109/ICDMW.2014.148
Schools/Departments:	University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User:	Aickelin, Professor Uwe
Date Deposited:	10 Feb 2015 15:19
Last Modified:	04 May 2020 20:17
URI:	https://eprints.nottingham.ac.uk/id/eprint/28261

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