A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations

no code implementations • 4 Jun 2021 • Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang

We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision.

BIG-bench Machine Learning Interpretable Machine Learning

An Interpretable Model with Globally Consistent Explanations for Credit Risk

no code implementations • 30 Nov 2018 • Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang

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.