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Towards Explainable Deep Learning for Credit Lending: A Case Study

Machine Learning 2018-12-04 v2 Artificial Intelligence
Authors: Ceena Modarres , Mark Ibrahim , Melissa Louie , John Paisley
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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.

Keywords

deep neural networks machine learning interpretable machine learning

Cite

@article{arxiv.1811.06471,
  title  = {Towards Explainable Deep Learning for Credit Lending: A Case Study},
  author = {Ceena Modarres and Mark Ibrahim and Melissa Louie and John Paisley},
  journal= {arXiv preprint arXiv:1811.06471},
  year   = {2018}
}

Comments

Accepted NIPS 2018 FEAP

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