# Towards Explainable Deep Learning for Credit Lending: A Case Study

arXiv: https://arxiv.org/abs/1811.06471 · HTML: https://mlsanity.com/paper/1811.06471/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences](https://mlsanity.com/paper/2310.04621/) (2023)
- [The FIX Benchmark: Extracting Features Interpretable to eXperts](https://mlsanity.com/paper/2409.13684/) (2024)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set](https://mlsanity.com/paper/2404.05908/) (2024)
- [Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models](https://mlsanity.com/paper/2405.20794/) (2024)
- [Model-agnostic interpretation by visualization of feature perturbations](https://mlsanity.com/paper/2101.10502/) (2021)

## Abstract (arXiv)

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.