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Consumer Credit ModelsPricing, Profit and Portfolios$
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Lyn C. Thomas

Print publication date: 2009

Print ISBN-13: 9780199232130

Published to Oxford Scholarship Online: May 2009

DOI: 10.1093/acprof:oso/9780199232130.001.1

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Measurement of scoring systems

Measurement of scoring systems

Chapter:
(p.100) 2 Measurement of scoring systems
Source:
Consumer Credit Models
Author(s):

Lyn C. Thomas

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199232130.003.0002

This chapter describes the different ways of measuring how good a scoring system is. It clarifies that there are three different ways of measuring the systems: their ability to discriminate Goods from Bads, their prediction of the probability of a borrower defaulting, and the accuracy of their categorical calibration. Discrimination, which only requires knowledge of the scorecard itself, is measured using ROC curves, Cumulative Accuracy Profiles (CAP), Gini Coefficient, AUROC, Divergence, Mahalonobis distance, and Somers D-concordance statistic. Probability predictions, which need the population odds as well as the scorecard, are measured using the binomial and normal tests and the Hosmer-Lemeshow test. Categorical calibration, which needs the cut-off score as well as the scorecard, is measured using confusion matrix, swap sets, specificity and sensitivity, and Type I and Type II errors. The chapter also explains how, if one has built a suite of scorecards each on a different segment of the population, one can combine the measures of the different scorecards into an overall measure.

Keywords:   scorecard measurements, ROC Curve, Gini coefficient, AUROC, Manhalonobis distance, Somers D-concordance, confusion matrix, CAP curve, divergence, segmentation

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