  We propose a possible solution to a public challenge posed by the Fair Isaac
Corporation (FICO), which is to provide an explainable model for credit risk
assessment. Rather than present a black box model and explain it afterwards, we
provide a globally interpretable model that is as accurate as other neural
networks. Our "two-layer additive risk model" is decomposable into subscales,
where each node in the second layer represents a meaningful subscale, and all
of the nonlinearities are transparent. We provide three types of explanations
that are simpler than, but consistent with, the global model. One of these
explanation methods involves solving a minimum set cover problem to find
high-support globally-consistent explanations. We present a new online
visualization tool to allow users to explore the global model and its
explanations.
