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.
Highlights
As Consumer Debt has risen to become a significant problem of our modern societies, especially in developed countries, it triggered the interest of Academic Community, which tried to provide reasonable explanations for the “nature” of this complex problem
In this work we examine the multifaceted aspect of Consumer Indebtedness by exploring the impact of Psychological Factors upon models that are based traditionally on Demographic and Economic Data, within a complete Data Mining framework
Our results show that Psychological Factors are significant predictors of Consumer Indebtedness confirming the multifaceted nature of the problem [9], [18]
Summary
As Consumer Debt has risen to become a significant problem of our modern societies, especially in developed countries, it triggered the interest of Academic Community, which tried to provide reasonable explanations for the “nature” of this complex problem. As the authors in [14] point out that no clear and conceptual model of Consumer Indebtedness has yet emerged despite the fact that many factors influencing Consumer Debt have been proposed in literature, which so far has focused on a few or a subset of demographic, economic, psychological and situational factors, limiting, its predictive ability and its generalizability [18] This may have been due to the reason that no database exists in literature that includes an extensive list of these factors as well as sufficient information to define dependent variables representing financial indebtedness. The careful data preprocessing, the powerful models and reliable evaluation techniques, comprise a complete and sophisticated toolbox to analyse complex real world data, like socio-economic data, and can guarantee representative and meaningful Knowledge discovery Towards this direction, in this work we examine the multifaceted aspect of Consumer Indebtedness by exploring the impact of Psychological Factors upon models that are based traditionally on Demographic and Economic Data, within a complete Data Mining framework.
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