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FINANCIAL STATEMENT ANALYSIS OF BANK OF BARODA
This study undertakes an advanced and comprehensive financial statement analysis of Bank of Baroda, one of India's leading public sector banks, by strategically integrating traditional financial ratio analysis with cutting-edge Machine Learning (ML) and Deep Learning (DL) methodologies. Acknowledging the inherent limitations of conventional static analysis in capturing the complex dynamics of the banking sector, the research will systematically collect and scrutinize Bank of Baroda's audited financial statements over a significant 5-10 year historical period (e.g., FY2015-FY2024). This extensive dataset will first be used to compute a nuanced array of bank-specific financial ratios spanning critical dimensions such as liquidity (e.g., Advances to Deposits Ratio), profitability (e.g., Net Interest Margin, Return on Assets), solvency/capital adequacy (e.g., Capital Adequacy Ratio), and asset quality (e.g., Gross Non-Performing Assets ratio). Following a traditional trend analysis and benchmark comparison against peer banks in the Indian landscape, the study will delve into advanced analytics. ML models, specifically powerful ensemble techniques like Random Forest and XGBoost, will be meticulously applied to identify the most influential ratios and underlying financial factors that significantly impact the bank's core profitability metrics and asset quality, thereby uncovering previously hidden or less obvious relationships and drivers. Furthermore, to address the critical need for forward-looking insights, Deep Learning models, particularly Long Short-Term Memory (LSTM) networks, will be developed and deployed for forecasting crucial future financial metrics. These LSTMs are uniquely capable of capturing intricate temporal dependencies, seasonal patterns (ee.g., quarter-on-quarter variations in credit growth or deposit mobilization), and market volatilities that profoundly affect banking operations. The performance of these AI-driven forecasting models will be rigorously evaluated against traditional linear time-series models using metrics such as Mean Squared Error (MSE) and prediction accuracy, with the expectation of demonstrating superior predictive power. Ultimately, this research aims to provide Bank of Baroda's stakeholders – including management, regulators, and investors – with more robust, dynamic, and datadriven insights, enhancing strategic decision-making capabilities, optimizing risk management frameworks, and fostering greater financial stability in a complex and evolving banking environment
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