Enhancing Financial Management Efficiency through Advanced Prediction Modeling and Data-Driven Decision-Making Strategies

Abstract

Efficient financial management depends on the ability to precisely forecast financial risks, frequently insolvency, which directly impacts strategic planning and resource allocation. However, many existing prediction models struggle to process complex, multivariate financial data, which limits their efficiency in presenting actionable understanding for proactive decision-making. To address this challenge, this research offers an advanced predictive modeling framework based on the Intelligent Grey Wolf Optimized Deep Residual Neural Network (IGWO-DRNN), which incorporates deep learning (DL) with nature-inspired optimization to improve insolvency prediction and financial management efficacy. The research initiates with comprehensive data preprocessing, including normalization. Independent Component Analysis (ICA) is working for feature extraction, modifying complex financial variables into numerically independent components to uncover hidden patterns within the data. The predictive core is the IGWO-DRNN, incorporating the learning ability of deep residual networks with the global optimization strength of the Intelligent Grey Wolf Optimizer (IGWO) to efficiently model nonlinear relationships within financial datasets and avoid local minima during training. The entire implementation is created in Python and its machine-learning (ML) libraries, certifying computational flexibility and scalability. The proposed IGWO-DRNN model achieves a high R² (0.498) with reduced MSE (0.014), MAE (0.078), and RMSE (0.120). The IGWO-DRNN cruciallyimproves both predictive accuracy and computational efficiency. This intelligent framework contributes modern financial management by enabling timely, reliable, and data-driven forecasts, supporting proactive risk mitigation and strategic decision-making.