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Multimodal Machine Learning for Credit Modeling

Nguyen, Cuong V. and Das, Sanjiv R. and He, John and Yue, Shenghua and Hanumaiah, Vinay and Ragot, Xavier and Zhang, Li (2021) Multimodal Machine Learning for Credit Modeling. In: 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE , Piscataway, NJ, pp. 1754-1759. ISBN 978-1-6654-2463-9.

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Credit ratings are traditionally generated using models that use financial statement data and market data, which is tabular (numeric and categorical). Practitioner and academic models do not include text data. Using an automated approach to combine long-form text from SEC filings with the tabular data, we show how multimodal machine learning using stack ensembling and bagging can generate more accurate rating predictions. This paper demonstrates a methodology to use big data to extend tabular data models, which have been used by the ratings industry for decades, to the class of multimodal machine learning models.

Item Type:Book Section
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Nguyen, Cuong V.0000-0003-4019-181X
Additional Information:© 2021 IEEE.
Subject Keywords:credit ratings, multimodal, machine learning, long-form text
Record Number:CaltechAUTHORS:20211124-153938711
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Official Citation:C. V. Nguyen et al., "Multimodal Machine Learning for Credit Modeling," 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 2021, pp. 1754-1759, doi: 10.1109/COMPSAC51774.2021.00262
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112034
Deposited By: Tony Diaz
Deposited On:24 Nov 2021 19:43
Last Modified:24 Nov 2021 19:43

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