Exploration of Supply Chain Financing Model and Virtual Economic Risk Control Using the Backpropagation Neural Network

Abstract

This article aims to optimize the supply chain financing model and address virtual economic risk control by effectively reducing associated risks. To achieve this objective, the backpropagation (BP) neural network model is designed and implemented, promoting the application of intelligent technology in supply chain financing and virtual economic risk control. Initially, a fundamental BP neural network model is developed and evaluated. Subsequently, an Adam-BP neural network model is proposed by optimizing the Adam optimizer, providing substantial technical support for enhancing the supply chain financing model and virtual economic risk control. The research results indicate significant performance improvement after applying Adam optimization to BP, with all indicators in the plant classification dataset surpassing 0.92 and those in the credit card fraud dataset increasing to above 0.9. Thus, the model presented here exhibits exceptional adaptability and offers effective technical support for optimizing the supply chain financing model and virtual economic risk control methods.