In credit risk scoring models are a tool used to evaluate the level of risk associated with applicants or customers. The aim of these models is not only to estimate the probability that the client will not be able to fulfil his financial commitments but also to identify and estimate the risk drivers i.e., client’s attributes that are responsible for risk occurrence.

Unfortunately, scoring models are built based on historic data stored by bank over the clients. Selection of clients is not random. This leads to systematic errors.  Therefore one seeks methods that allow for a model correction that enables application of statistical inference. Quasi-experimental designs are practical solutions to this dilemma. One of such methods is Propensity Score Matching (PSM).  PSM involves controlled selection of subjects ex post.

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