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Credit scorecards are models used for the modelling of the probability ofdefault of clients. The decision to extend credit to an applicant, as well asthe price of the credit, is often based on these models. In order to ensurethat scorecards remain accurate over time, the hypothesis of populationstability is tested periodically; that is, the hypothesis that thedistributions of the attributes of clients at the time when the scorecard wasdeveloped is still representative of these distributions at review is tested. Anumber of measures of population stability are used in practice, with severalbeing proposed in the recent literature. This paper provides a critical reviewof several testing procedures for the mentioned hypothesis. The widely usedpopulation stability index is discussed alongside two recently proposedtechniques. Additionally, the use of classical goodness-of-fit techniques isconsidered and the problems associated with large samples are investigated. Inaddition to the existing testing procedures, we propose two new techniqueswhich can be used to test population stability. The first is based on thecalculation of effect sizes which does not suffer the same problems asclassical goodness-of-fit techniques when faced with large samples. The secondproposed procedure is the so-called overlapping statistic. We argue that thissimple measure can be useful due to its intuitive interpretation. In order todemonstrate the use of the various measures, as well as to highlight theirstrengths and weaknesses, several numerical examples are included.

A critical review of existing and new population stability testing procedures in credit risk scoring