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We propose a possible solution to a public challenge posed by the Fair IsaacCorporation (FICO), which is to provide an explainable model for credit riskassessment. Rather than present a black box model and explain it afterwards, weprovide a globally interpretable model that is as accurate as other neuralnetworks. Our "two-layer additive risk model" is decomposable into subscales,where each node in the second layer represents a meaningful subscale, and allof the nonlinearities are transparent. We provide three types of explanationsthat are simpler than, but consistent with, the global model. One of theseexplanation methods involves solving a minimum set cover problem to findhigh-support globally-consistent explanations. We present a new onlinevisualization tool to allow users to explore the global model and itsexplanations.

An Interpretable Model with Globally Consistent Explanations for Credit Risk