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Activity Number:
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128
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2016 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Consulting
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Abstract #320302
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Title:
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The Statistics of Credit Scoring: How to Use What You Already Know to Build a Credit Scorecard
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Author(s):
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Billie Anderson*
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Companies:
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Ferris State University
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Keywords:
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credit scoring ;
logistic regression ;
binning interval variables
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Abstract:
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Credit scoring is a method of modeling potential risk of credit applicants. It involves using different statistical techniques and past historical data to create a credit score that financial institutions use to assess credit applicants in terms of risk. Credit scoring is essentially a type of classification problem. The classification problem under study in credit scoring is which credit applicants should be considered as good credit risks and which applicants should be considered as bad risks. One of the main classification models used to assess credit worthiness is logistic regression. A logistic regression is built from a number of characteristic inputs. This paper will show how to use a logistic regression to build a scorecard model using common credit scoring characteristic inputs. The binning of the interval inputs will be demonstrated, and the importance of the binning will be discussed. The software used to illustrate how to build a credit scorecard will be SAS® Enterprise MinerT.
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Authors who are presenting talks have a * after their name.
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