The use of machine learning algorithms in credit scoring can be enhanced by an improved understanding of the reasoning behind model decisions. Although machine learning algorithms are widely regarded as highly accurate, their use in settings that require an explanation of model decisions has been limited due to a lack of transparency. This is particularly the case in the banking sector, where the model risk frameworks of banks frequently require a significant level of model interpretability. In this paper, the Shapley value is evaluated as a machine learning interpretability technique in credit scoring. The Shapley value is a model-agnostic machine learning interpretability technique that quantifies the contribution of each feature in the prediction of a specific observation. The effectiveness of this technique is tested on various simulated data sets with covariates from different underlying distributions that are linearly and nonlinearly related to the outcome. Traditional models (eg, logistic and linear regression) and machine learning algorithms are trained on the data and the Shapley values are generated. Our results show that Shapley values are related to weights of evidence (a well-known measure in the scorecard literature) and can be used to explain the direction of relationships between explanatory variables and the outcome.