A Data Mining framework to model Consumer Indebtedness with Psychological Factors
Machine Learning
2015-02-23 v1 Computational Engineering, Finance, and Science
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
Cite
@article{arxiv.1502.05911,
title = {A Data Mining framework to model Consumer Indebtedness with Psychological Factors},
author = {Alexandros Ladas and Eamonn Ferguson and Uwe Aickelin and Jon Garibaldi},
journal= {arXiv preprint arXiv:1502.05911},
year = {2015}
}
Comments
IEEE International Conference of Data Mining: The Seventh International Workshop on Domain Driven Data Mining 2014 (DDDM 2014)