Indebted households profiling: a knowledge discovery from database approach

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

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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.

Item Type: Article
Additional Information: The final publication is available at Springer via
Keywords: Clustering, Homogeneity analysis, Silhouette width, credit risk
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Aickelin, Professor Uwe
Date Deposited: 14 Oct 2015 07:16
Last Modified: 04 May 2020 20:09

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