Enhanced Financial Forecasting and Budgeting Accuracy with Advanced AI Algorithms

The aim of this project is to enhance decision-making and resource allocation by applying sophisticated AI algorithms in enhancing financial prediction accuracy and budgeting. The existing method (ARIMA) predicts financial forecasts and budget accuracy using time series analysis, achieving an accuracy of 85.3%, precision of 76.4%, recall of 72.1%, and an F1 score of 83.2%. LSTM outperforms ARIMA with higher accuracy 89.2%, precision 86.8%, and recall 88.5%, making it more effective in prediction and error reduction. As compared with traditional statistical models, the new AI model of financial forecasting is much more precise. While traditional statistical models are between 62.31% and 87.65%, the AI model is between 85.42% and 98.76%. The optimal data-set size of 10,000 records was achieved in order to receive the most precise forecast in financial forecasting and was statistically significant at a level of around 0.0047. Overall, our approach surpasses traditional methods and yields a better and more precise financial planning alternative.