Title :
A Data Mining Framework to Model Consumer Indebtedness with Psychological Factors
Author :
Garibaldi, J. ; Ferguson, E. ; Aickelin, U.
Author_Institution :
Sch. of Comput. Sci., Univ. of Nottingham, Nottingham, UK
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.
Keywords :
consumer behaviour; data mining; psychology; consumer debt; consumer indebtedness; data mining framework; data mining technique; psychological characteristics; psychological factors; Analytical models; Biological system modeling; Data mining; Data models; Economics; Predictive models; Psychology; Consumer Debt Analysis; Data Mining; Impulsivity; Psychological Factors;
Conference_Titel :
Data Mining Workshop (ICDMW), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4799-4275-6
DOI :
10.1109/ICDMW.2014.148
