Applications of Integrated Gradients in Credit Risk Modeling


Statistics and Analytical Sciences

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Deep Learning models are often called ‘Black Box’ models because of the difficulty in providing logical, actionable explanations of their decisions. Attribution methods try to solve this issue by determining the contribution of each feature to the decision. Recently, the Integrated Gradients method has shown promising results in variable attribution for differentiable models [1]. Integrated Gradients belong to the class of a more general attribution method called Path Integrated Gradients. Path Integrated Gradients is a parametric path-dependent and baseline-dependent attribution method. If the path is a straight line, the method becomes the canonical Integrated Gradients method. Choosing any other path leads to different attributions in general. Similarly, using different baselines may also lead to a change in attributions. Sundararajan et al. [1] recommend choosing a baseline that contains as little signal (discriminatory features used by the model for solving the task at hand) as possible, with the expectation that a comparison between the baseline and any input will reveal the signals of that input. In this paper, we show that choosing baselines with signals can also have very informative, distinct, and useful interpretations. We also propose two useful applications in the credit industry using two such baselines.