DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction

Abstract

Financial management prediction, often known as financial forecasting, is the act of estimating future financial outcomes using past data and present trends. It is an essential component of financial analysis and planning that aids businesses in making well-informed decisions and preparing for potential future events. In the healthcare domain, financial management prediction is a crucial task that helps patients track and predict the expenses required for their medical services. The established methods for financial management prediction have some flaws, such as the requirement of labeled data, data quality, time complexity, under fitting problems, and longer execution times. Therefore, in order to resolve these limitations; a deep learning-based model is developed in this study for efficient financial management prediction. Specifically, this research proposes a dual-recurrent neural network with a tri-channel attention mechanism (DR-Z2AN) for accurate prediction. The proposed DR-Z2AN model combines the tri-channel attention mechanism with dual-RNN and multi-head attention, which enhances the robustness and interpretability of the systems. The multi-head attention learns the complex relationships between the data, which develops the generalization capability of the model in prediction tasks. The combined model efficiently processes the sequence data, and the tri-channel attention improves the model's capacity to extract meaningful characteristics from the input. The integration of the incentive learning approach helps the model improve the learning parameters to get better results with the minimum error. The experimental results demonstrate that the DR-Z2AN model attains minimal error in terms of MAE, MAPE, MSE, and RMSE of 1.46, 3.83, 4.32, and 2.08, respectively; thus, the proposed approach gives better results than the other traditional methods. Overall, the DR-Z2AN model offers accurate predictions with reduced computational time and improved interpretability.