Indebted households profiling: a knowledge discovery from database approach
A major challenge in consumer credit risk portfolio management is to classify households according to their risk profile. In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencie...
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| Format: | Article |
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Springer
2015
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| Online Access: | https://eprints.nottingham.ac.uk/30448/ |
| _version_ | 1848793986979856384 |
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| author | Scarpel, Rodrigo Ladas, Alexandros Aickelin, Uwe |
| author_facet | Scarpel, Rodrigo Ladas, Alexandros Aickelin, Uwe |
| author_sort | Scarpel, Rodrigo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A major challenge in consumer credit risk portfolio management is to classify households according to their risk profile. In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencies and associations amongst the available continuous and categorical variables altogether and accurately generate profiles of most interesting household segments according to their credit risk. The objective of this work is to employ a knowledge discovery from database process to identify groups of indebted households and describe their profiles using a database collected by the Consumer Credit Counselling Service (CCCS) in the UK. Employing a framework that allows the usage of both categorical and continuous data altogether to find hidden structures in unlabelled data it was established the ideal number of clusters and such clusters were described in order to identify the households who exhibit a high propensity of excessive debt levels. |
| first_indexed | 2025-11-14T19:09:01Z |
| format | Article |
| id | nottingham-30448 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:09:01Z |
| publishDate | 2015 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-304482020-05-04T20:09:48Z https://eprints.nottingham.ac.uk/30448/ Indebted households profiling: a knowledge discovery from database approach Scarpel, Rodrigo Ladas, Alexandros Aickelin, Uwe A major challenge in consumer credit risk portfolio management is to classify households according to their risk profile. In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencies and associations amongst the available continuous and categorical variables altogether and accurately generate profiles of most interesting household segments according to their credit risk. The objective of this work is to employ a knowledge discovery from database process to identify groups of indebted households and describe their profiles using a database collected by the Consumer Credit Counselling Service (CCCS) in the UK. Employing a framework that allows the usage of both categorical and continuous data altogether to find hidden structures in unlabelled data it was established the ideal number of clusters and such clusters were described in order to identify the households who exhibit a high propensity of excessive debt levels. Springer 2015-03 Article PeerReviewed Scarpel, Rodrigo, Ladas, Alexandros and Aickelin, Uwe (2015) Indebted households profiling: a knowledge discovery from database approach. Annals of Data Science, 2 (1). pp. 43-59. ISSN 2198-5812 Clustering Homogeneity analysis Silhouette width credit risk http://link.springer.com/article/10.1007%2Fs40745-015-0031-2 doi:10.1007/s40745-015-0031-2 doi:10.1007/s40745-015-0031-2 |
| spellingShingle | Clustering Homogeneity analysis Silhouette width credit risk Scarpel, Rodrigo Ladas, Alexandros Aickelin, Uwe Indebted households profiling: a knowledge discovery from database approach |
| title | Indebted households profiling: a knowledge discovery from database approach |
| title_full | Indebted households profiling: a knowledge discovery from database approach |
| title_fullStr | Indebted households profiling: a knowledge discovery from database approach |
| title_full_unstemmed | Indebted households profiling: a knowledge discovery from database approach |
| title_short | Indebted households profiling: a knowledge discovery from database approach |
| title_sort | indebted households profiling: a knowledge discovery from database approach |
| topic | Clustering Homogeneity analysis Silhouette width credit risk |
| url | https://eprints.nottingham.ac.uk/30448/ https://eprints.nottingham.ac.uk/30448/ https://eprints.nottingham.ac.uk/30448/ |