Assessing countries' credit ratings using Machine learning

Abstract

The goal of this research is to present the beneficial improvements that may be brought about in sovereign credit ratings if machine learning is applied to rate them. A rating is assigned after taking into account a wide range of factors affecting an economy's fundamentals. When credit ratings are done by humans, there is a bias in considering these elements, which leads to an underestimation of the country. A country's credit rating is crucial because it represents the risk that a foreign investor considers when purchasing a country's debt. Depending on these credit ratings can also have a negative impact on the flow of foreign portfolio investors. Thus, applying machine learning to analyze these criteria can aid in eliminating prejudice and disclosing a country's genuine credit rating.