FINANCIAL STATEMENT ANALYSIS OF PNB

Abstract

This study proposes an innovative and indepth financial statement analysis of Punjab National Bank (PNB), a prominent public sector bank in India, by synergistically integrating traditional financial ratio analysis with advanced Machine Learning (ML) and Deep Learning (DL) techniques. Recognizing the complex and dynamic nature of the Indian banking sector, characterized by evolving credit cycles, stringent regulatory frameworks, and intense competition, conventional financial analysis often provides a retrospective view. This research aims to move beyond static analysis by leveraging the power of AI to unearth deeper insights and generate predictive intelligence.The methodology will involve meticulously collecting and processing PNB's audited financial statements over a significant 5-10 year period (e.g., FY2015-FY2024). A comprehensive set of bank-specific financial ratios will be computed across key dimensions including liquidity (e.g., CreditDeposit Ratio, Liquid Assets to Total Assets), profitability (e.g., Net Interest Margin, Return on Assets, Cost-to-Income Ratio), solvency/capital adequacy (e.g., Capital Adequacy Ratio), and asset quality (e.g., Gross Non-Performing Assets Ratio, Provision Coverage Ratio).Subsequently, ML algorithms such as Random Forest and XGBoost will be employed. These models are adept at identifying intricate, non-linear relationships and will be used to pinpoint the most influential ratios impacting PNB's core profitability and asset quality, thereby revealing hidden drivers and potential risk factors. Furthermore, to provide crucial forward-looking perspectives, Long ShortTerm Memory (LSTM) networks, a specialized form of Deep Learning, will be applied. LSTMs are particularly well-suited for time-series forecasting, enabling the models to capture inherent temporal dependencies, seasonal patterns (e.g., quarterly fluctuations in deposit growth or credit off-take), and market volatilities unique to the banking sector. The performance of these AI-driven forecasting models will be rigorously evaluated against traditional linear time-series models (e.g., ARIMA) using standard metrics like Mean Squared Error (MSE) and prediction accuracy. The expected outcome is to demonstrate how AI significantly enhances predictive accuracy and offers dynamic, data-driven insights, thereby empowering stakeholders with superior tools for strategic decision-making, proactive risk management, and more informed capital allocation within the complex banking landscape.