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Basic Scorecard Quality Assessment – allows assessing the quality with which the scorecard identifies (classifies) "good" and "bad" borrowers as well as the correctness of risk distribution. |
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Classification quality assessment
The most common method of risk distribution validation is the ROC curve and Gini coefficient.
The higher the curve is, the larger the indicator is, the better the scorecard is.
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Confusion matrix
The confusion matrix reflects the percentage (or the number of):
- correctly identified "good" borrowers (true positive);
- "good" borrowers, mistakenly identified as "bad" ones (False Negative);
- "bad" borrowers mistakenly identified as "good" ones (False Positive);
- correctly identified "bad" borrowers (True Negative).
The confusion matrix shown corresponds to a scorecard that correctly identifies 96% of "good" borrowers and 78% of "bad" borrowers.
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Risk distribution validation
The correct risk distribution involves a monotonous increase in the odds of the "good" outcome. Only such distributions allow formulating rules for working with borrowers based on their score, use of the risk-based price formation, etc.
IMPORTANT: The scorecard that does not demonstrate an increase in the odds of the "good" outcome must be re-developed, even if all the other quality indicators are acceptable. |
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