STUDY ON FINANCIAL ANALYSIS ON ICICI PREDENTIAL LIFE INSURANCE

Abstract

Financial analysis plays a vital role in evaluating the performance, stability, and risk exposure of insurance companies. In this study, we conduct an in-depth financial analysis of ICICI Prudential Life Insurance, integrating Machine Learning (ML) and Deep Learning (DL) approaches to enhance traditional financial assessment. The objective is to automate the analysis of financial statements and extract predictive insights that support data-driven decision-making.Key financial indicators such as premium income, claim ratios, solvency margins, and investment returns are examined using supervised ML algorithms like Random Forest and XGBoost for classification and forecasting. In addition, Long Short-Term Memory (LSTM) networks are employed to model and predict time-series data, such as quarterly premium income or policyholder benefits over time. These techniques enable early detection of risk patterns, deviations from historical performance, and potential financial anomalies.The study demonstrates that the integration of AI technologies significantly improves the accuracy, scalability, and depth of financial analysis in the insurance sector. By using intelligent models, stakeholders can better understand financial dynamics, predict future outcomes, and strengthen strategic planning in a highly competitive and regulated industry