Kolmogorov-Smirnov (KS) score: considerations for using KS score in AI model evaluation (February 2026)

The Kolmogorov-Smirnov (KS) score is a vital metric for evaluating AI models, particularly in credit scoring and fraud detection, as it measures the maximum separation between cumulative distribution functions of two classes in binary classification. The KS score ranges from 0 to 100, with values above 40 indicating strong model performance, and is more actionable than ROC AUC for operational risk teams as it identifies the single best decision boundary. Calculating the KS score involves ranking predictions, computing cumulative percentages for each class, and finding the largest gap between distributions, which signals discriminatory power. However, users must be cautious of pitfalls like hypersensitivity in large datasets and the test's limitations with discrete data or small samples. Automated KS monitoring in production, facilitated by tools like Openlayer, helps detect model drift and ensures that models maintain their performance by triggering alerts when scores fall below predefined thresholds, thus translating technical performance into business value.