The value of text for small business default prediction: a deep learning approach

DOI10.1016/j.ejor.2021.03.008zbMath1487.91171arXiv2003.08964OpenAlexW3012625840MaRDI QIDQ2239924

Cristián Bravo, Matthew Stevenson, Christophe Mues

Publication date: 5 November 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.08964

zbMATH Keywords

risk analysistext miningOR in bankingdeep learningsmall business lending

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Actuarial science and mathematical finance (91G99)

Related Items (4)

Credit default prediction from user-generated text in peer-to-peer lending using deep learning ⋮ Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making ⋮ Machine learning in bank merger prediction: a text-based approach ⋮ A transformer-based model for default prediction in mid-cap corporate markets

Uses Software

• glmnet
• GloVe
• Tensor2Tensor
• FEND
• BERT
• XLNet

Cites Work

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• Deep learning with long short-term memory networks for financial market predictions
• Learning risk culture of banks using news analytics
• On the risk prediction and analysis of soft information in finance reports
• 10.1162/jmlr.2003.3.4-5.993
• Benchmarking state-of-the-art classification algorithms for credit scoring
• Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model

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