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A Data Mining framework to model Consumer Indebtedness with Psychological Factors
lib:4417b5608280b810 (v1.0.0)
Authors: Alexandros Ladas,Eamonn Ferguson,Uwe Aickelin,Jon Garibaldi
ArXiv: 1502.05911
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1502.05911v1
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.
ArXiv: 1502.05911
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1502.05911v1
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.
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