STUDY OF FINANCIAL ANALYSIS OF AXIS BANK.

Abstract

Financial analysis is fundamental to understanding the performance and stability of banks. Traditionally, it involves examining financial statements, ratios, and market trends to assess profitability, liquidity, and risk. However, the banking sector today operates in a highly datadriven environment where conventional methods may not fully capture complex, hidden patterns.This study begins with a comprehensive financial analysis of Axis Bank using traditional tools and techniques, followed by an extension into Machine Learning (ML) and Deep Learning (DL) methodologies. By leveraging historical financial data, stock prices, and macroeconomic indicators, the study builds predictive models using algorithms such as Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks. These models aim to forecast future trends, detect anomalies, and identify significant factors influencing Axis Bank's performance.The integration of ML and DL enhances the depth and accuracy of the analysis, demonstrating the transformative potential of artificial intelligence in modern financial research and decision-making.