Project financing risk evaluation based on Bayesian network

This research addresses the persistent challenge of securing financing despite a promising project outlook. Utilizing Bayesian networks, a comprehensive project financing risk assessment is conducted, enabling the integration of diverse information into a unified structure for informed decision-making. This approach provides posterior probabilities for interconnected nodes, enhancing understanding of complex risk factors. Moreover, leveraging constrained variable-scale optimization rooted in nonlinear programming theory, the study explores optimal financing structures for enterprises facing financial hurdles. Significantly, this research contributes a novel methodology that combines Bayesian networks and optimization techniques to navigate intricate financial landscapes. Findings underscore the efficacy of Bayesian networks in assessing project financing risks comprehensively, while the optimization approach aids in identifying pragmatic financing structures crucial for sustained project development. Crucially, this study's significance lies in offering insights pivotal for sound financial decision-making in challenging financing scenarios for project advancements.