A data mining framework to model consumer indebtedness with psychological factors

Ladas, Alexandros; Ferguson, Eamonn; Garibaldi, Jonathan M.; Aickelin, Uwe

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.

Citation

Ladas, A., Ferguson, E., Garibaldi, J. M., & Aickelin, U. A data mining framework to model consumer indebtedness with psychological factors. Presented at IEEE International Conference on Data Mining: The Seventh International Workshop on Domain Driven Data Mining 2014 (DDDM 2014)

• Conference Name: IEEE International Conference on Data Mining: The Seventh International Workshop on Domain Driven Data Mining 2014 (DDDM 2014)
• Publication Date: Jan 1, 2014
• Deposit Date: Feb 10, 2015
• Publicly Available Date: Feb 10, 2015
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/999055
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7022592
• 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