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Credit rating agencies provide credit ratings to companies, which provide us with information on the creditworthiness of companies. Traditionally, these rating agencies use information from financial statements. Investors and other stakeholders must have reliable information regarding the creditworthiness and debt-repayment capacity of a corporation. The credit rating score provided is based on credit default swap market trading. Credit ratings, provided by credit rating agencies, have been a traditional way of assessing a borrower's creditworthiness. To overcome potential errors and biases, artificial intelligence-based credit score evaluations have attracted attention in recent years. Machine learning techniques can analyze credit ratings quickly and update them daily, providing more up-to-date and accurate information. This project aims to develop a machine learning model that includes credit default swap (CDS) spreads along with other financial information to provide better insights into an organization’s financial health. The model will use financial ratios, macroeconomic indicators, and CDS spreads as the basis of analysis. The objective of this project is to create a credit rating system that is more accurate and trustworthy so that investors, intermediaries like investment banks, debt issuers, and businesses and organizations may make better investment decisions.

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Integrating Credit Default Swaps Spreads with Clustering Model for Enhanced Understanding of Financial Health–A Machine Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Sakshi Sharma,
  • Bindhia Joji,
  • Ravi Darshini

摘要

Credit rating agencies provide credit ratings to companies, which provide us with information on the creditworthiness of companies. Traditionally, these rating agencies use information from financial statements. Investors and other stakeholders must have reliable information regarding the creditworthiness and debt-repayment capacity of a corporation. The credit rating score provided is based on credit default swap market trading. Credit ratings, provided by credit rating agencies, have been a traditional way of assessing a borrower's creditworthiness. To overcome potential errors and biases, artificial intelligence-based credit score evaluations have attracted attention in recent years. Machine learning techniques can analyze credit ratings quickly and update them daily, providing more up-to-date and accurate information. This project aims to develop a machine learning model that includes credit default swap (CDS) spreads along with other financial information to provide better insights into an organization’s financial health. The model will use financial ratios, macroeconomic indicators, and CDS spreads as the basis of analysis. The objective of this project is to create a credit rating system that is more accurate and trustworthy so that investors, intermediaries like investment banks, debt issuers, and businesses and organizations may make better investment decisions.