Predictive Analytics for Budgeting and Management Using Deep Learning Techniques

Organizational performance depends on financial planning and management, and predictive analytics powered by deep learning has transformed decision-making. This study analyzes various model types and their financial forecast and management performance. We study CNN, RNN, LSTM, and Traditional Machine Learning models, focusing on accuracy, precision, recall, F1 score, and MSE. Deep learning models, particularly LSTM, outperform classical machine learning models in different financial contexts. LSTM has 93.8% accuracy, high precision, recall, and F1 score, showing superior predicting ability. Traditional machine learning models have moderate accuracy and poor performance measures, limiting their ability to capture complicated financial patterns. According to feature importance analysis, historical stock prices, firm financial data, and economic indicators improve prediction accuracy. Combining these attributes, Deep learning algorithms can better predict financial trends and enhance investment strategies. We also evaluate model types for stock price prediction, market trend research, credit risk assessment, and portfolio optimization. LSTM routinely outperforms alternative models in certain settings, helping firms make educated decisions. Deep learning models are feasible for real-time financial applications due to their high computational complexity and quick forecasts. Traditional models, however simpler, are inaccurate and slow.