Assessment of Evaluation Methods for Prediction and Classifications of Consumer Risk in the Credit Industry
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In this chapter, we examine and compare the most prevalent modeling techniques in the credit industry, Linear Discriminant Analysis, Logistic Analysis and the emerging technique of Neural Network modeling. K-S Tests and Classification Rates are typically used in the industry to measure the success in predictive classification. We examine those two methods and a third, ROC Curves, to determine if the method of evaluation has an influence on the perceived performance of the modeling technique. We found that each modeling technique has its own strengths, and a determination of the “best” depends upon the evaluation method utilized and the costs associated with misclassification.
2021 ◽
Vol 1746
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pp. 012008
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2014 ◽
Vol 39
(11)
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pp. 7661-7671
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2007 ◽
Vol 24-25
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pp. 243-248
2001 ◽
Vol 48
(3)
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pp. 495-501
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2021 ◽
pp. 100306
2007 ◽
Vol 28
(7)
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pp. 541-546
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