Risk Prediction of PPP Project Financing Based on BP Neural Network Model

Public-Private Partnership (PPP) project financing is a key solution for infrastructure development but carries inherent financial risks because of investment return uncertainties, policy revisions, and market fluctuations. Classic models of risk forecasting, including statistical regression and simple machine learning models, tend to fail when dealing with nonlinear relationships and intricate dependencies among risk variables, resulting in less than optimal decision-making accuracy. In order to overcome these drawbacks, this research puts forward a Backpropagation (BP) Neural Network-based risk forecasting model adapted for PPP project finance. The model uses multi-layer nonlinear mappings to capture complex patterns from past PPP financing records, thus enhancing predictive performance. Through the incorporation of feature normalization, network optimization, and early stopping validation techniques, the suggested system exhibits better performance compared to traditional methodologies. Experimental analysis shows that the model is 97.4 % accuracy score, which testifies to its reliability and strength in predicting financing risks. The findings point to the effectiveness of $\mathbf{B P}$ Neural Networks as a useful decision-support tool for government organizations, investors, and financial institutions involved in PPP initiatives.