Interpretation and prediction are two goals of modeling. The goal of interpretation is to extract information from the model about how the response variables are associated to the input variables, while the goal of prediction is to predict what the responses will be given some set of inputs. 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. In this paper, we propose a new credit scoring approach using Deep Learning based on our new Replicated Local Interpretable Model-agnostic Explanations approach. The approach is able to interpret black-box model decisions while provides a better performing model than linear models, so offers a new way for credit scoring for the banking and finance industries.