Using Machine Learning to Explain Credit Ratings
We explore how machine learning can be leveraged to better understand the drivers behind credit ratings of public companies.
Credit ratings play a crucial role in the financial world. They help lenders, investors, and other stakeholders make informed decisions about the credit risk associated with a specific borrower or investment.
Credit rating agencies such as Moody’s, S&P, and Fitch, regularly gauge the creditworthiness of individual firms, and their issued securities, to help guide investors in making decisions.
These rating agencies employ many factors in determining their ratings. For example, they examine factors such as financial statements, industry position, and the broader economic climate. Key aspects also include the issuer’s balance sheet, profit outlook, competition, and management quality.
While the rating agencies, for example, Moody’s, do publish the methodology surrounding their credit rating analysis, these methodologies, which consider both quantitative and qualitative factors, can be quite complicated to draw inferences from.
For example, what variables were the most important in determining the credit rating? How much does each variable inform the credit rating of any given firm? How does the probability of a rating depend on how these variables change?
This article aims to set up a simple model that illustrates how an ML model, coupled with standards approaches to model “explainability,” can be employed to illustrate the broad influences different variables can have on influencing a given firm’s credit rating.
