Towards Explainable Deep Learning for Credit Lending: A Case Study

11/15/2018

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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.

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Towards Explainable Deep Learning for Credit Lending: A Case Study

CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning

Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data

Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes

An Interpretable Neural Network for Parameter Inference

Hide-and-Seek: A Template for Explainable AI

DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data

Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition

Towards Explainable Deep Learning for Credit Lending: A Case Study

Related Research

CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning

Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data

Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes

An Interpretable Neural Network for Parameter Inference

Hide-and-Seek: A Template for Explainable AI

DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data

Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition