Indebted households profiling: a knowledge discovery from database approach
This addresses consumer credit risk management for financial institutions, but it is incremental as it applies existing methods to new data.
The paper tackled the problem of classifying households by credit risk profile using a knowledge discovery from database approach on UK consumer credit data, resulting in the identification of clusters that describe households with high propensity for excessive debt.
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.