The Use Of Knowledge Discovery Techniques
For Behavioural Scoring
Author(s)
N. Meeus, J. Huysmans, B. Baesens, J. Vanthienen
& M. Vandebroek
Abstract
This paper discusses the use of knowledge discovery techniques for a recent
development in the field of scoring: behavioural scoring. The goal of behavioural
scoring is to develop a model that predicts the creditworthiness of existing
customers on the basis of their behaviour in the past. This paper explains briefly
the Knowledge Discovery in Data process and applies the technique of logistic
regression to real life datasets of a Belgian financial institution. It describes the
development of scoring models for a cheque account, a credit account and the
customer level and compares the model results for different pre-processing
values and selection methods by means of the ROC curve, p-values and
misclassification rates.
Keywords: behavioural scoring, ROC-analysis, credit scoring, KDD.
1 Introduction
Credit companies have always aimed at predicting as accurate as possible the
creditworthiness of their (future) customers. In the past this was based on the
personal judgement of the lender. Nowadays however credit-granting decisions
are based on statistical or operational research methods.
The continuous search for better techniques led to the idea of applying
Knowledge Discovery in Data (KDD) techniques for scoring. A financial
institution has access to lots of customer data in which one can possibly find
interesting patterns and correlations that can contribute to a better credit granting.
To indicate the prediction of customer creditworthiness one often uses the
term ‘scoring’. Through the help of a scoring technique the customer is given a
Keywords
behavioural scoring, ROC-analysis, credit scoring, KDD.
Related Book
Other papers in this volume
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