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A data mining framework to model consumer indebtedness with psychological factors

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

Authors

Alexandros Ladas

Eamonn Ferguson

Jonathan M. Garibaldi jmg@cs.nott.ac.uk

Uwe Aickelin



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.

Publication Date Jan 1, 2014
Peer Reviewed Peer Reviewed
APA6 Citation Ladas, A., Ferguson, E., Garibaldi, J. M., & Aickelin, U. (2014). A data mining framework to model consumer indebtedness with psychological factors
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7022592
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
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

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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