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Kolmogorov-Smirnov Curve

Shows how strong is the difference between the distributions of the “Bad” and “Good” borrowers


Kolmogorov-Smirnov curve shows the difference between the distribution of “Goods” and “Bads”. The maximum difference between “Goods” and “Bads” distribution known as a Kolmogorov-Smirnov value, that is often used together with Gini value to asses scorecard quality.


Kolmogorov-Smirnov 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. Kolmogorov-Smirnov values for training and validation datasets should be close to each other.

When several scorecards compared, preference is given to the one with the highest Kolmogorov-Smirnov value.


Unacceptable Kolmogorov-Smirnov curves






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For more complex and versatile needs of larger credit institutions we recommend Scorto™:
Acquaintance with Scoring
What is scoring and why you need it
Scoring
Scorecard
Cut-off, odds and risk groups
What scoring can bring you
Profitability of Operational Decisions
Decreased Decision Time
Agility and Reduced Labor Costs
Types of Scoring
Judgmental vs. Statistical Scorecard
What you need to start using scoring
Scorecard Development
Scorecard Development Stages
Historical Data Preparation
Training & Validation Datasets
Binning
Characteristics Selection Using
Information Value
Logistic Regression for Scorecard Calculation
Automatic Variables Selection with Stepwise Methods
Basic Scorecard Quality Assessment
Reject Inference
Scorecard Scaling
Scorecard Validation
Gini & ROC Curve
Kolmogorov-Smirnov Curve
Risk Segments
Strategy Curve
Approval vs "Bad" Rate
"Good" and "Bad" distribution
Cumulative Event Rates
Chi-squared Scorecard Stability Test
Score Points Distribution
Scorecard Implementation & Deployment
Verifying Borrowers Characteristics Stability
Scorecard Correction (if required)
Setting Cut-off
Policy Rules Definition
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Champion-Challenger Deployment Approach
Advanced How-to
How to Improve Scorecard Accuracy
How to Apply Reject Inference Methods