Financial Ratio Analysis and Artificial Intelligence/Machine Learning

This study explores the potential of artificial intelligence (AI) and machine learning (ML) techniques in the context of financial ratio analysis. While traditional financial analysis methods are largely based on static assessments of historical data, machine learning models offer dynamic and predictive insights by utilizing multidimensional financial ratios. This paper defines key financial ratio categories and explains how AI systems employ these indicators within predictive modeling frameworks. A comparative evaluation of various algorithms—such as logistic regression, random forest, XGBoost, and LSTM—is provided. Empirical findings from the literature demonstrate that AI-based approaches often outperform conventional methods in critical applications such as bankruptcy prediction, credit scoring, and portfolio management. Furthermore, the paper addresses ethical and regulatory concerns related to data security, algorithmic transparency, and fairness. In conclusion, machine learning models utilizing financial ratio data play an increasingly vital role in financial decision support systems by offering rapid, flexible, and accurate analytical capabilities.