Interpretable Deep Learning based Risk Evaluation Approach

摘要

There are two goals of modeling, including interpretation that is to extract information about how the response variables are associated to the input variables, and prediction that is to predict what the responses are going to be. The dilemma is that interpretable algorithms such as linear regression or logistic regression are often not accurate for prediction, while complex algorithms for better prediction are much more accurate but not easy to interpret1. Risk could be in the forms of cyber security risk, credit risk, investment risk, operational risk, etc. In this paper, we propose an interpretable method in evaluating risk using Deep Learning.