Towards Explainable Deep Learning for Credit Lending: A Case Study

Abstract

Deep learning adoption in the financial services industry has been limited due to a lack of model interpretability. However, several techniques have been proposed to explain predictions made by a neural network. We provide an initial investigation into these techniques for the assessment of credit risk with neural networks.

Accepted NIPS 2018 FEAP

References in corpus (5)

• Towards A Rigorous Science of Interpretable Machine Learning
• Axiomatic Attribution for Deep Networks
• Learning Important Features Through Propagating Activation Differences
• Distilling a Neural Network Into a Soft Decision Tree
• The (Un)reliability of saliency methods

Cited by in corpus (5)

• Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
• SUBPLEX: Towards a Better Understanding of Black Box Model Explanations at the Subpopulation Level
• Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning
• Model-agnostic interpretation by visualization of feature perturbations
• Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models