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Scoring by usage

Scoring by usage
Scoring by usage
This paper aims to discover whether the predictive accuracy of a new applicant scoring model for a credit card can be improved by estimating separate scoring models for applicants who are predicted to have high or low usage of the card. Two models are estimated. First we estimate a model to explain the desired usage of a card, and second we estimate separately two further scoring models, one for those applicants whose usage is predicted to be high, and one for those for whom it is predicted to be low. The desired usage model is a two-stage Heckman model to take into account the fact that the observed usage of accepted applicants is constrained by their credit limit. Thus a model of the determinants of the credit limit, and one of usage, are both estimated using Heckman's ML estimator. We find a large number of variables to be correlated with desired usage. We also find that the two stage scoring methodology gives only very marginal improvements over a single stage scoring model, that we are able to predict a greater percentage of bad payers for low users than for high users and a greater percentage of good payers for high users than for low users.
credit scoring, Heckman, selection model
0160-5682
997-1006
Banasik, J.
a3ce3068-328b-4bce-889f-965b0b9d2362
Crook, J.
3dc59075-7ed4-486c-84c5-bbe73108be2a
Thomas, L.
7bfb1cd3-c990-4617-b97a-4d44314ec11c
Banasik, J.
a3ce3068-328b-4bce-889f-965b0b9d2362
Crook, J.
3dc59075-7ed4-486c-84c5-bbe73108be2a
Thomas, L.
7bfb1cd3-c990-4617-b97a-4d44314ec11c

Banasik, J., Crook, J. and Thomas, L. (2001) Scoring by usage. Journal of the Operational Research Society, 52 (9), 997-1006.

Record type: Article

Abstract

This paper aims to discover whether the predictive accuracy of a new applicant scoring model for a credit card can be improved by estimating separate scoring models for applicants who are predicted to have high or low usage of the card. Two models are estimated. First we estimate a model to explain the desired usage of a card, and second we estimate separately two further scoring models, one for those applicants whose usage is predicted to be high, and one for those for whom it is predicted to be low. The desired usage model is a two-stage Heckman model to take into account the fact that the observed usage of accepted applicants is constrained by their credit limit. Thus a model of the determinants of the credit limit, and one of usage, are both estimated using Heckman's ML estimator. We find a large number of variables to be correlated with desired usage. We also find that the two stage scoring methodology gives only very marginal improvements over a single stage scoring model, that we are able to predict a greater percentage of bad payers for low users than for high users and a greater percentage of good payers for high users than for low users.

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More information

Published date: 2001
Keywords: credit scoring, Heckman, selection model

Identifiers

Local EPrints ID: 35667
URI: https://eprints.soton.ac.uk/id/eprint/35667
ISSN: 0160-5682
PURE UUID: 0c740671-c570-4f30-a6db-2aea21a996cb

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Date deposited: 22 May 2006
Last modified: 17 Jul 2017 15:47

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