Paper Title
Financial Ratios Impact on Company’s Rating and Bankruptcy’s Risk using Machine Learning

Abstract
This research focuses on predicting corporate credit ratings using machine learning techniques by using financial ratios and other company-specific features, emphasizing their impact on financial stability and decision-making for businesses, investors, and policymakers. Corporate ratings are integral to assessing a company’s creditworthiness and play a vital role in the financial ecosystem. The methodology involves evaluating and processing a dataset that includes financial ratios and extending it with economic indicators. Exploring various machine learning techniques include Random Forest, XG Boost, and regression tree diagrams which will be utilized and compared for accuracy while investigating the effectiveness of different approaches in forecasting ratings as well as bankruptcy’s risk. The analysis also highlights the importance of addressing class imbalance in the target variable, as unbalanced data distributions often lead to weak predictions. The findings underscore the potential of machine learning techniques in improving the accuracy of these predictions and highlight the need for continuous advancements in financial data analysis and decision-making tools. Keywords - Machine Learning, Corporate Credit Rating, Bankruptcy Risk, Financial Ratios, Financial Stability.