  A major requirement for credit scoring models is to provide a maximally
accurate risk prediction. Additionally, regulators demand these models to be
transparent and auditable. Thus, in credit scoring, very simple predictive
models such as logistic regression or decision trees are still widely used and
the superior predictive power of modern machine learning algorithms cannot be
fully leveraged. Significant potential is therefore missed, leading to higher
reserves or more credit defaults. This paper works out different dimensions
that have to be considered for making credit scoring models understandable and
presents a framework for making ``black box'' machine learning models
transparent, auditable and explainable. Following this framework, we present an
overview of techniques, demonstrate how they can be applied in credit scoring
and how results compare to the interpretability of score cards. A real world
case study shows that a comparable degree of interpretability can be achieved
while machine learning techniques keep their ability to improve predictive
power.
