Transparency, auditability, and explainability of machine learning models in credit scoring

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 article 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 scorecards. 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.

Automated summary

Credit scoring models require accurate risk prediction and transparency, but the superior predictive power of modern machine learning algorithms is not fully utilized. This article presents a framework for making black box machine learning models transparent, auditable, and explainable, and shows how these techniques can be applied in credit scoring to achieve comparable interpretability while maintaining predictive power.