A data mining framework to model consumer indebtedness with psychological factors
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 De...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2014
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| Online Access: | https://eprints.nottingham.ac.uk/28261/ |
| _version_ | 1848793537660846080 |
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| author | Ladas, Alexandros Ferguson, Eamonn Garibaldi, Jonathan M. Aickelin, Uwe |
| author_facet | Ladas, Alexandros Ferguson, Eamonn Garibaldi, Jonathan M. Aickelin, Uwe |
| author_sort | Ladas, Alexandros |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:01:53Z |
| format | Conference or Workshop Item |
| id | nottingham-28261 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:01:53Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-282612020-05-04T20:17:04Z https://eprints.nottingham.ac.uk/28261/ A data mining framework to model consumer indebtedness with psychological factors Ladas, Alexandros Ferguson, Eamonn Garibaldi, Jonathan M. Aickelin, Uwe 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. 2014 Conference or Workshop Item PeerReviewed Ladas, Alexandros, Ferguson, Eamonn, Garibaldi, Jonathan M. and Aickelin, Uwe (2014) A data mining framework to model consumer indebtedness with psychological factors. In: IEEE International Conference on Data Mining: The Seventh International Workshop on Domain Driven Data Mining 2014 (DDDM 2014), 14 Dec 2014, Shenzhen, China. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7022592 |
| spellingShingle | Ladas, Alexandros Ferguson, Eamonn Garibaldi, Jonathan M. Aickelin, Uwe A data mining framework to model consumer indebtedness with psychological factors |
| title | A data mining framework to model consumer indebtedness with psychological factors |
| title_full | A data mining framework to model consumer indebtedness with psychological factors |
| title_fullStr | A data mining framework to model consumer indebtedness with psychological factors |
| title_full_unstemmed | A data mining framework to model consumer indebtedness with psychological factors |
| title_short | A data mining framework to model consumer indebtedness with psychological factors |
| title_sort | data mining framework to model consumer indebtedness with psychological factors |
| url | https://eprints.nottingham.ac.uk/28261/ https://eprints.nottingham.ac.uk/28261/ |