mlsanityData to 7 Oct 2026

Towards Explainable Deep Learning for Credit Lending: A Case Study

arXiv 1811.06471 ↗First posted 15 Nov 2018cs.LG cs.AIcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Nov 2018.

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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 3.

6 papers.

  1. 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
  2. 2024The FIX Benchmark: Extracting Features Interpretable to eXpertscited by 5
  3. 2023Survey on AI Ethics: A Socio-technical Perspectivecited by 4
  4. 2024Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data setcited by 3
  5. 2024Model Interpretation and Explainability: Towards Creating Transparency in Prediction Modelscited by 1
  6. 2021Model-agnostic interpretation by visualization of feature perturbationscited by 0