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practical credit scoring system |
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The most widely used way to evaluate quality of a scorecard
is Gini coefficient
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ROC curve that located higher and more to the left is indicates better scorecard quality. The evaluation of the quality of classification by the Gini coefficient can be checked with the help of the following tables: |
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Application Scoring
Collection Scoring
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Behavioral Scoring
Fraud Scoring
The Gini approach is not relevant for fraud scoring because
the number of fraudsters in a typical dataset is too small, and
scorecard quality should be analyzed with other methods.
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| ROC Curve values usually calculated not only for the dataset that was used to create a scorecard (training set), but also for a separate out-of-sample validation dataset. ROC Curve values for training and validation datasets should be close to each other. When several scorecards are compared, preference is given to the one with the highest Gini value. | ||
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Unacceptable ROC curve performance.
Scorecard need to be improved.  |
Reality.
Acceptable ROC curve performance.  |
Perfect ROC curve performance. 
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Plug&Score is the most easy-to-use and the fastest to integrate scoring system. |
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For more complex and versatile needs of larger credit institutions we recommend Scorto™: |
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