Financial ratio analysis is a foundational tool in evaluating the performance, stability, and profitability of banking institutions. Traditionally, these ratios—such as liquidity, profitability, solvency, and efficiency—are interpreted through static, historical comparison methods. However, with the increasing complexity of financial data and dynamic market conditions, traditional methods often fall short in identifying hidden patterns and forecasting future performance. This study proposes the integration of Machine Learning (ML) and Deep Learning (DL) techniques to enhance the depth, accuracy, and predictive power of ratio analysis, using HDFC Bank as a case study. In this research, historical financial statements of HDFC Bank over the last decade are used to compute key financial ratios. ML models such as Random Forest, Support Vector Machine (SVM), and XGBoost are applied to classify financial health and predict future trends based on historical patterns. In parallel, Deep Learning models— particularly Long Short-Term Memory (LSTM) networks—are utilized to analyze time-series behavior of the bank’s financial ratios and forecast future values. This hybrid analytical approach allows for more accurate performance evaluation and early warning indicators of potential financial shifts.The results demonstrate that AI-driven techniques provide deeper insights into the interdependencies of financial metrics and enable better prediction of HDFC Bank’s financial trajectory. The study highlights the value of augmenting traditional financial analysis with intelligent models to support strategic planning, regulatory compliance, and investment decision-making. This approach sets a precedent for more automated, data-driven financial assessments in the banking sector.
sultana et al. (Mon,) studied this question.