Core Concepts
This article demonstrates how to develop an unsupervised machine learning analysis on corporate financial data to assess and segment business credit risk levels.
Abstract
The article discusses the importance of credit analysis in the financial domain, particularly for corporate borrowers. Unlike individual borrowers, corporate credit analysis typically focuses on the business's cash flow and ability to meet loan agreements.
The author explains that credit analysts can use various techniques to score business credit risk based on quantitative and qualitative financial information. One approach is to analyze data patterns and group them using unsupervised machine learning.
The article then guides the reader through the process of collecting relevant corporate financial data using the Financial Modeling Prep API. This includes accessing the company's income statements, balance sheets, and cash flow statements.
The author plans to use this financial data to develop an unsupervised machine learning model to segment and assess the credit risk levels of the companies. This will demonstrate a modern, data-driven approach to corporate credit risk analysis.
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