An Interpretable Model with Globally Consistent Explanations for Credit Risk
An Interpretable Model with Globally Consistent Explanations for Credit Risk
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发表时间：
2018-11
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通讯作者：
Chaofan Chen-;Kangcheng Lin;C. Rudin;Yaron Shaposhnik;Sijia Wang;Tong Wang
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作者：
Chaofan Chen-;Kangcheng Lin;C. Rudin;Yaron Shaposhnik;Sijia Wang;Tong Wang
针对公平艾萨克公司(FICO)提出的公众挑战，我们提出了一种可能的解决方案，即为信用风险评估提供一个可解释的模型。我们没有提出一个黑盒模型并在后面解释它，而是提供了一个全局可解释的模型，它与其他神经网络一样准确。我们的“两层累加风险模型”可以分解成多个子尺度，其中第二层中的每个节点代表一个有意义的子尺度，并且所有的非线性都是透明的。我们提供了三种类型的解释，它们比全球模型简单，但与全球模型一致。这些解释方法之一涉及解决最小集合覆盖问题，以找到高支持度的全局一致的解释。我们提出了一个新的在线可视化工具，允许用户探索全球模型及其解释。
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.