STUDY ON NALYSIS OF FINANCIAL STATEMENT OF AIRTEL

Abstract

The financial performance of a company serves as a critical indicator of its operational efficiency, stability, and growth prospects. This study aims to conduct a comprehensive analysis of the financial statements of Bharti Airtel, one of India’s leading telecom providers, by integrating Machine Learning (ML) and Deep Learning (DL) techniques to derive predictive and analytical insights. Traditional financial statement analysis relies heavily on static ratios and historical comparisons, which often fail to uncover complex patterns or provide forward-looking insights. To overcome these limitations, this research employs intelligent models to evaluate key financial parameters such as revenue, net profit, EBITDA, debt-equity ratio, and cash flow trends.Machine Learning models such as Linear Regression, Random Forest, and Support Vector Machines (SVM) are applied to assess financial health and identify influential variables impacting performance. Furthermore, Deep Learning models, particularly Long Short-Term Memory (LSTM) networks, are used to forecast future financial outcomes using time-series data, capturing seasonality and market fluctuations that traditional models might overlook. The results demonstrate that ML/DL-based approaches not only enhance forecasting accuracy but also help in risk detection and decision-making. By combining financial domain knowledge with AI-powered techniques, this study offers a dynamic and data-driven framework for financial statement analysis. It empowers stakeholders—including investors, analysts, and decision-makers—with intelligent tools to assess Airtel’s financial trajectory, detect anomalies, and plan strategic actions with greater confidence.