Model-Agnostic Interpretable and Data-driven suRRogates suited for highly regulated industries

Highly regulated industries, like banking and insurance, ask for transparent decision-making algorithms. At the same time, competitive markets push for sophisticated black box models. We therefore present a procedure to develop a Model-Agnostic Interpretable Data-driven suRRogate, suited for structured tabular data. Insights are extracted from a black box via partial dependence effects. These are used to group feature values, resulting in a segmentation of the feature space with automatic feature selection. A transparent generalized linear model (GLM) is fit to the features in categorical format and their relevant interactions. We demonstrate our R package maidrr with a case study on general insurance claim frequency modeling for six public datasets. Our maidrr GLM closely approximates a gradient boosting machine (GBM) and outperforms both a linear and tree surrogate as benchmarks.