Profit Prediction with Financial Accounting Systems using a Reinforced Hybrid GRU-LSTM Model

Highly precise profit forecasting in financial accounting is crucial for informed decision-making. Conventional models, such ARIMA and XGBoost, frequently encounter difficulties with challenging temporal correlations and nonlinear patterns in financial data. To overcome these limitations, this research proposes a Reinforced Hybrid GRU-LSTM model that integrates the advantages of gated recurrent units (GRU) and long short-term memory (LSTM) networks, enhanced with reinforcement learning to optimise efficacy. Our model showed exceptional accuracy (98.7%), lowered RMSE (0.021), and decreased execution time (12.8s), surpassing baseline models by a considerable margin. The system effectively manages high-dimensional, time-series financial data, guaranteeing accurate profit forecasting and improving operational efficiency. Our methodology enhances decision-making relative to standard methods by decreasing mistakes, minimising makespan, and optimising cost-efficiency. Our results demonstrate that the suggested model can be efficiently utilised for real-time financial forecasting, facilitating the development of more intelligent and adaptable financial decision-making systems.