Skip to main content

Research Repository

Advanced Search

Indebted households profiling: a knowledge discovery from database approach

Scarpel, Rodrigo; Ladas, Alexandros; Aickelin, Uwe


Rodrigo Scarpel

Alexandros Ladas

Uwe Aickelin


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.


Scarpel, R., Ladas, A., & Aickelin, U. (2015). Indebted households profiling: a knowledge discovery from database approach. Annals of Data Science, 2(1),

Journal Article Type Article
Publication Date Mar 1, 2015
Deposit Date Oct 14, 2015
Publicly Available Date Oct 14, 2015
Journal Annals of Data Science
Print ISSN 2198-5804
Electronic ISSN 2198-5812
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 2
Issue 1
Keywords Clustering, Homogeneity analysis, Silhouette width, credit risk
Public URL
Publisher URL
Additional Information The final publication is available at Springer via


You might also like

Downloadable Citations