  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.
