Credit Risk Modeling with Graph Machine Learning

Sanjiv Das (), Xin Huang (), Soji Adeshina (), Patrick Yang () and Leonardo Bachega ()
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Sanjiv Das: Amazon Web Services, Santa Clara, California 95053; Santa Clara University, Santa Clara, California 95053
Xin Huang: Amazon Web Services, New York, New York 10001
Soji Adeshina: Amazon Web Services, Santa Clara, California 95053
Patrick Yang: Amazon Web Services, Seattle, Washington 98109
Leonardo Bachega: Amazon Web Services, Seattle, Washington 98109

INFORMS Joural on Data Science, 2023, vol. 2, issue 2, 197-217

Abstract: Accurate credit ratings are an essential ingredient in the decision-making process for investors, rating agencies, bond portfolio managers, bankers, and policy makers, as well as an important input for risk management and regulation. Credit ratings are traditionally generated from models that use financial statement data and market data, which are tabular (numeric and categorical). Using machine learning methods, we construct a network of firms using U.S. Securities and Exchange Commission (SEC) filings (denoted CorpNet) to enhance the traditional tabular data set with a corporate graph. We show that this generates accurate rating predictions with comparable and better performance to tabular models. We ensemble graph convolutional networks with highly-performant ensembled machine learning models using AutoGluon. This paper demonstrates both transductive and inductive methodologies to extend credit scoring models based on tabular data, which have been used by the ratings industry for decades, to the class of machine learning models on networks. The methodology is extensible to other financial machine learning models that may be enhanced using a corporate graph.

Keywords: credit ratings; machine learning; corporate graph; graph neural networks (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:2:y:2023:i:2:p:197-217

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