Differential Game Model of Information Sharing among Supply Chain Finance Based on Blockchain Technology

Enterprise financial sharing and risk identification model combining recurrent neural networks with transformer model supported by blockchain

The objective of this study is to investigate methodologies concerning enterprise financial sharing and risk identification to mitigate concerns associated with the sharing and safeguarding of financial data. Initially, the analysis examines security vulnerabilities inherent in conventional financial information sharing practices. Subsequently, blockchain technology is introduced to transition various entity nodes within centralized enterprise financial networks into a decentralized blockchain framework, culminating in the formulation of a blockchain-based model for enterprise financial data sharing. Concurrently, the study integrates the Bi-directional Long Short-Term Memory (BiLSTM) algorithm with the transformer model, presenting an enterprise financial risk identification model referred to as the BiLSTM-fused transformer model. This model amalgamates multimodal sequence modeling with comprehensive understanding of both textual and visual data. It stratifies financial values into levels 1 to 5, where level 1 signifies the most favorable financial condition, followed by relatively good (level 2), average (level 3), high risk (level 4), and severe risk (level 5). Subsequent to model construction, experimental analysis is conducted, revealing that, in comparison to the Byzantine Fault Tolerance (BFT) algorithm mechanism, the proposed model achieves a throughput exceeding 80 with a node count of 146. Both data message leakage and average packet loss rates remain below 10 %. Moreover, when juxtaposed with the recurrent neural networks (RNNs) algorithm, this model demonstrates a risk identification accuracy surpassing 94 %, an AUC value exceeding 0.95, and a reduction in the time required for risk identification by approximately 10 s. Consequently, this study facilitates the more precise and efficient identification of potential risks, thereby furnishing crucial support for enterprise risk management and strategic decision-making endeavors.

SEIR-diffusion modeling and stability analysis of supply chain finance based on blockchain technology

Inadequate information sharing and difficult information diffusion are the main factors that cause upstream and downstream enterprises to default on supply chain finance. Blockchain technology, which exploits distributed storage and a consensus mechanism, can provide effective solutions to overcome these problems such as information sharing. When blockchain technology is adopted by the enterprises that comprise the supply chain finance business, this technology shows a diffusion trend. As a result, the decision pertaining to the application of novel technologies is affected. Therefore, to investigate the diffusion mechanism pertaining to the blockchain technology that is applied in supply chain finance, the study exploited the idea of a class of SEIR infectious disease models, and built a blockchain model that considers the supply chain financial system. Besides, the study verifies the stability of the model by constructing a Lyapunov function. The results indicate that the basic reproduction number determines the proliferation of the blockchain technology. When the basic reproduction number is less than 1, the proliferation of the blockchain technology that is applied in supply chain finance system would terminate. By contrast, when the basic reproduction number is greater than 1, during the average infection period, the number of non-adopting enterprises that accept the blockchain technology becomes greater than 1, which can maintain a continuous impact on supply chain finance system. Over time, the number of enterprises that accept blockchain technology tends to be stable. Through numerical simulations that consider the influencing parameters pertaining to the basic regeneration number, which has important effect on blockchain technology diffusion, we enlarge the diffusion efficiency and increase the transfer rate of potential on-chain enterprises or decrease the default exit rate. As a result, we facilitate the diffusion of blockchain technology in the system.

Governmental Anti-Pandemic and Subsidy Strategies for Blockchain-Enabled Food Supply Chains in the Post-Pandemic Era

Aiming to explore whether governments should choose anti-pandemic or subsidy strategies in the post-pandemic era, this study constructed a three-level food supply chain that was composed of a leading third-party logistics provider, a supplier and a retailer, in which the third-party logistics provider used blockchain technology for food traceability to address consumer concerns about food safety. We then used game theory to analyze the pricing decisions, traceability levels, anti-pandemic effort levels and subsidy levels of the supply chain under different governmental anti-pandemic or subsidy strategies. Our results showed that in all scenarios, the higher the consumer preference for traceability information, the larger the traceability levels and anti-pandemic effort levels and the more favorable the outcome for all parties; thus, governments should improve consumer awareness of pandemic prevention. For the benefit of all parties, governments should adopt anti-pandemic and subsidy strategies simultaneously, even in the post-pandemic era. Interestingly, for the scenario in which governments could only adopt one strategy, when the cost coefficient of traceability was small, the governmental subsidies would actually lead to lower traceability levels of the 3PL. This study could provide decision-making references for governments during the post-pandemic era and a new possibility for blockchain application.