Leveraging Artificial Intelligence for Improved Financial Decision-Making in the Indian Mutual Fund Sector

The mutual fund industry in India has grown at an exponential rate, making sophisticated analytical methods necessary for better financial decision-making. This research investigates how machine learning (ML) and artificial intelligence (AI) systems can forecast important mutual fund efficiency indicators, such as returns, volatility, Sharpe ratios, and net asset value (NAV). We use metrics like R2 score, Mean Absolute Error (MAE), and Mean Squared Error (MSE) to assess several systems, including XGBoost, Random Forest, Decision Trees, Linear Regression, and Long Short-Term Memory (LSTM) networks. With the lowest prediction error and a test R2 of 0.93, the results show that deep learning models-in particular, LSTM-perform better than conventional methods. Feature importance analyses also identifies historical returns, AUM, and NAV as important predictors. The results show how AI can improve investment methods, choose funds more wisely, and reduce risks. In India's changing investment environment, this study adds to the expanding area of AI-driven financial decision-making.