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TopResearch Background And Motivations
In the context of the dynamic global economy, the supply chain financing model has gained substantial attention and practical application within the business sphere (Tsai, 2023). This model intricately interconnects financial institutions with diverse supply chain segments, offering vital financial backing to suppliers and mitigating their funding challenges (Wang et al., 2020). Consequently, the supply chain financing model has evolved into an indispensable mechanism for alleviating the financial constraints faced by suppliers (Sahoo & Thakur, 2023). Nonetheless, the benefits of this model are accompanied by inherent virtual economic risks that warrant profound consideration (Wu et al., 2020). In this study, the authors endeavored to delve into optimizing the supply chain financing model while simultaneously addressing virtual economic and financial risks, with the aim of fostering sustainability and enhancing the resilience of the continuously evolving business landscape.
The proliferation of virtual economic risks, encompassing challenges such as information asymmetry, credit risks, and transaction failures, has assumed a pivotal role in the intricate landscape of the supply chain financing model (Song et al., 2020). Given the intricate multistage and multiparticipant nature of this model, adept risk control becomes imperative (Qi et al., 2020). Any perturbation within specific segments of the supply chain has the potential to reverberate throughout, giving rise to financial vulnerabilities and economic losses (Zhang et al., 2020). In this research, the authors introduced an innovative approach employing backpropagation (BP) neural networks to amplify risk management and decision-making capabilities within the context of the supply chain financing model. BP neural networks, as artificial intelligence rooted in statistical learning theory, embody adaptability and nonlinear mapping prowess, rendering them well-suited for risk assessment and credit scoring applications (Queiroz et al., 2022). Through the development of BP neural network models, researchers can harness historical data and credit scores to prognosticate and govern risks inherent in the supply chain financing model (Su et al., 2020; Ye & Zhao, 2023). In this study, the authors aimed to furnish an efficacious solution for mitigating virtual economic risks and fortifying the stability and sustainability of the supply chain financing model. The researchers sought to attain precise risk prognostication and control through meticulous data aggregation, analysis, and the application of BP neural networks. Additionally, they aimed to address information dissemination and collaboration mechanisms across diverse stages and participants, thereby amplifying the overall efficiency and stability of the supply chain. The authors employed BP neural networks to effectively manage virtual economic risks inherent in supply chain financing models. By developing BP neural network models and utilizing historical data and credit scores, the authors were able to predict and control risks within the supply chain financing framework.
Thus, this research has two maincontributions: Firstly, it introduces a novel approach to adeptly control virtual economic risks within the supply chain financing model, addressing an existing research gap; secondly, it offers valuable guidance to business decision-makers and practitioners, facilitating better risk management and mitigation. This endeavor contributes to the sustainable evolution of the supply chain financing model, reduces economic losses, and enhances efficiency and stability. Furthermore, the proposed approach enhances overall stability and resilience in the supply chain financing ecosystem, fostering sustained business development and economic growth. The application of this method empowers participants to accurately predict and control risks, thereby increasing operational efficiency and stability. Ultimately, businesses gain more reliable financing support, fueling broader economic progress.