Indebted households profiling: a knowledge discovery from database approach

TL;DR

This paper presents a data-driven approach to classify indebted households by discovering hidden structures in consumer credit data, aiding risk management.

Contribution

It introduces a knowledge discovery framework that analyzes both categorical and continuous data to identify and describe high-risk household segments.

Findings

Identified distinct household clusters with high debt propensity.

Established the optimal number of clusters for risk profiling.

Provided detailed profiles of high-risk indebted households.

Abstract

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.