Gini & ROC Curve
The most widely used way to evaluate quality of a scorecard
is Gini coefficient and ROC curve
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:
Application Scoring
Behavioral Scoring
Gini value
Classification quality
Gini value
Classification quality
from 0.25
Low
from 0.45
Low
0.25 - 0.45
Average
0.45 - 0.65
Average
0.45 - 0.6
Good
0.65 - 0.8
Good
more than 0.6
Very good
more than 0.8
Very good
Collection Scoring
Fraud Scoring
Gini value
Classification quality
from 0.35
Low
0.35 - 0.55
Average
0.55 - 0.7
Good
more than 0.7
Very good
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.
ROC Curve values usually calculated not only for the dataset that was used to create scorecard (training set), but also for a separate out-ofsample 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.
Unacceptable ROC curve performance.
Scorecard need to be improved.
www.plug-n-score.com
Reality.
Acceptable ROC curve performance.
Perfect ROC curve performance.