Supply chain finance real-time risk regulation and control system based on dynamic game model

Abstract

The invention relates to the technical field of supply chain finance, and discloses a supply chain finance real-time risk regulation and control system based on a dynamic game model, which comprises a system main body, the system main body comprises a data acquisition module, a data processing and analysis module, a dynamic game model construction module, a risk assessment module, a risk regulation and control strategy generation module and an execution feedback module. According to the supply chain finance real-time risk regulation and control system based on the dynamic game model, decision-making behaviors of multiple subjects in a dynamic environment and mutual influences of the subjects can be simulated in real time, and theoretical support which is more suitable for an actual business scene is provided for risk assessment and regulation and control strategy formulation; and meanwhile, the dynamic game model building module is combined to simulate multi-party subject decision behaviors, so that the risk assessment module can assess the risk levels of each enterprise and the whole supply chain more accurately.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain finance, in particular to a supply chain finance real-time risk regulation system based on a dynamic game model. BACKGROUND

[0002] In the field of supply chain finance, risk regulation is a key link to ensure stable operation of the business. In the prior art, many supply chain finance risk regulation systems use static risk assessment models. By setting fixed risk indicators and thresholds, they analyze the credit of enterprises in the supply chain, transaction data, and other information to assess risks and take appropriate control measures. For example, some systems use traditional credit scoring models to calculate risk levels based on static information such as financial statements and historical transaction records. This approach can to some extent preliminarily screen and control risks, has the advantages of relatively simple operation and clear calculation logic, and provides basic support for risk control in the early stage of supply chain finance development.

[0003] However, such static risk assessment models have significant drawbacks. Since supply chain finance involves multiple parties and the transaction environment is complex and variable, static models are difficult to adapt to the dynamic changes in risk factors in the supply chain. Dynamic information such as market price fluctuations, sudden changes in the operating status of upstream and downstream enterprises, and adjustments to policies and regulations cannot be incorporated into the risk assessment system in a timely manner, resulting in lagging risk assessment and failure to achieve real-time risk regulation. In addition, existing systems lack the ability to handle the game relationship between multiple parties. In supply chain finance, there is a game of interests between banks, core enterprises, and small and medium-sized enterprises in the upstream and downstream, and the decisions of each party influence each other. However, traditional systems often ignore this game relationship and do not consider it in the scope of risk regulation, making it difficult for the control strategy to balance the interests of all parties and effectively reduce the overall risk.

[0004] Therefore, in order to solve the above problems, we propose a supply chain finance real-time risk regulation system based on a dynamic game model. SUMMARY

[0005] (I) Technical problems solved In view of the shortcomings of the prior art, the supply chain finance real-time risk regulation system based on a dynamic game model has the advantages of being able to capture the decision-making behavior of each party in the supply chain and its mutual influence in real time, combining real-time updated market data, enterprise operation data, and other dynamic information to achieve real-time assessment and accurate regulation of supply chain finance risks. It solves the problems of existing supply chain finance risk regulation systems, such as the inability to cope with dynamic risk factors and the inability to effectively handle the game relationship between multiple parties, resulting in lagging risk assessment and unbalanced control strategies.

[0006] (II) Technical solutions To achieve the above objectives, the present invention provides the following technical solution: a real-time risk control system for supply chain finance based on a dynamic game model, comprising a system body, wherein the system body includes a data acquisition module, a data processing and analysis module, a dynamic game model construction module, a risk assessment module, a risk control strategy generation module, and an execution feedback module.

[0007] Preferably, the data acquisition module is used to collect data from multiple channels involved in supply chain finance business.

[0008] Preferably, the data processing and analysis module is used to receive data from the data acquisition module, and the data processing and analysis module is also used to extract characteristic indicators that reflect the credit status and risk level of the enterprise.

[0009] Preferably, the dynamic game model construction module is used to integrate the interests and decision-making behaviors of multiple parties in supply chain finance to construct a dynamic game model.

[0010] Preferably, the risk assessment module is used to combine the data provided in the data processing and analysis module with the game results output in the dynamic game model component module.

[0011] Preferably, the risk control strategy generation module is used to formulate targeted risk control strategies based on the risk level and risk trend output by the risk assessment module, combined with the balance point of interests of all parties obtained by analyzing the dynamic game model constructed by the dynamic game model construction module.

[0012] Preferably, the execution feedback module is used to convey the strategy formulated by the risk control strategy generation module to the relevant execution entities and track the execution status of the strategy.

[0013] (III) Beneficial Effects Compared with existing technologies, this invention provides a real-time risk control system for supply chain finance based on a dynamic game theory model, which has the following advantages: 1. This real-time risk control system for supply chain finance based on a dynamic game model breaks through the limitations of traditional static risk assessment by introducing a dynamic game model into the field of supply chain finance risk control. It can simulate the decision-making behavior of multiple parties in a dynamic environment and their mutual influence in real time, providing theoretical support for risk assessment and control strategy formulation that is more in line with actual business scenarios. At the same time, combined with the simulation of decision-making behavior of multiple parties by the dynamic game model construction module, the risk assessment module can more accurately assess the risk level of each enterprise and the entire supply chain, effectively avoiding risk misjudgment caused by information lag and single analysis dimension, and significantly improving the timeliness and accuracy of risk assessment.

[0014] 2. This supply chain finance real-time risk control system based on a dynamic game model, through the construction of a comprehensive data collection and processing system, enables real-time collection, in-depth analysis and effective utilization of multi-dimensional dynamic data in supply chain finance business, so that risk assessment can reflect the true risk status of the supply chain in a timely manner, and improve the timeliness and accuracy of risk control. 3. This supply chain finance real-time risk control system based on a dynamic game model constructs a closed-loop risk control process from data collection, analysis, model building, risk assessment, strategy generation to execution feedback. This ensures that the risk control strategy can be continuously optimized and adjusted according to the actual situation, forming a continuous and effective risk prevention and control mechanism. This is also an important improvement of this system compared with existing technologies. Attached Figure Description

[0015] Fig. 1 This is a schematic diagram of the system architecture of the real-time risk control system for supply chain finance based on a dynamic game model proposed in this invention. Fig. 2 This is a schematic diagram of the dynamic game model process in this invention; Fig. 3 This is a schematic diagram of the closed-loop risk control process in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figs. 1-3 A real-time risk control system for supply chain finance based on a dynamic game model includes a system body, which comprises a data acquisition module, a data processing and analysis module, a dynamic game model construction module, a risk assessment module, a risk control strategy generation module, and an execution feedback module.

[0018] The data acquisition module is used to collect data from multiple channels involved in supply chain finance business. These channels include, but are not limited to, bank transaction systems, core enterprise ERP systems, financial systems of upstream and downstream SMEs, market data platforms, and policy and regulation release platforms. The types of data collected cover basic enterprise information (such as enterprise size, establishment time, equity structure, etc.), financial data (balance sheet, profit and loss statement, cash flow statement, etc.), transaction data (transaction amount, transaction time, transaction object, etc.), market data (commodity price fluctuations, industry prosperity index, etc.), and policy and regulation data. To ensure the timeliness and accuracy of the data, this module adopts real-time data acquisition technology, using API interfaces, data crawlers, etc., to obtain data from various data sources at a preset frequency, and performs preliminary cleaning on the collected data to remove duplicate and erroneous data, providing a reliable data foundation for subsequent data processing and analysis.

[0019] The data processing and analysis module is used to receive data from the data acquisition module. The data processing and analysis module is also used to extract characteristic indicators that reflect the credit status and risk level of enterprises. First, the data is deeply cleaned and standardized. At the same time, through time series analysis and other methods, the changing trends of market data and policy and regulatory data are predicted to uncover the risk signals hidden behind the data.

[0020] The dynamic game model construction module is used to integrate the interests and decision-making behaviors of multiple parties in supply chain finance to construct a dynamic game model. During the model construction process, the decision variables (such as credit limit, transaction price, delivery time, etc.), payoff functions and constraints of each party are clearly defined. Simultaneously, game theory algorithms, such as dynamic programming and Nash equilibrium solving algorithms, are employed to simulate the behavior of various stakeholders and their mutual influences under different decision-making scenarios, thereby deriving the optimal decision-making path for each party in a dynamically changing environment and providing theoretical support for risk assessment and regulation strategy generation.

[0021] The risk assessment module combines the data provided by the data processing and analysis module with the game results output by the dynamic game model component module to conduct a comprehensive and dynamic assessment of supply chain finance risks. This module establishes a multi-level risk assessment indicator system covering multiple dimensions such as credit risk, market risk, and operational risk. It uses machine learning algorithms, such as random forest and support vector machine algorithms, to perform comprehensive analysis and weight calculation on risk indicators, thereby obtaining the risk level of each enterprise and the entire supply chain. At the same time, based on the results of the dynamic game model, it assesses the impact of the decision-making behavior of each entity on the risk, predicts the development trend of the risk, and provides accurate risk assessment results for the generation of risk control strategies.

[0022] The risk control strategy generation module is used to formulate targeted risk control strategies based on the risk level and risk trend output by the risk assessment module, combined with the balance point of interests of all parties obtained by the dynamic game model analysis constructed by the dynamic game model construction module. When formulating strategies, the interests and decision-making responses of all parties are fully considered to ensure that the control strategies can effectively reduce risks and be accepted by all parties, thereby achieving the stable operation of the supply chain finance system.

[0023] The execution feedback module is used to convey the strategies formulated by the risk control strategy generation module to the relevant execution entities, track the execution status of the strategies, collect feedback information from all parties by monitoring data changes in real time during the execution process, evaluate the implementation effect of the strategies, and if it is found that the expected risk control objectives have not been achieved after the strategy is implemented, or if new risk factors have emerged during the execution process, the execution feedback module will promptly feed the information back to the data processing and analysis module, the dynamic game model construction module, etc., prompting the system to reprocess the data, optimize the model, and adjust the strategy to form a closed-loop risk control process.

[0024] It should be noted that the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0025] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time risk control system for supply chain finance based on a dynamic game model, comprising the main body of the system, characterized in that: The main body of the system includes a data acquisition module, a data processing and analysis module, a dynamic game model construction module, a risk assessment module, a risk control strategy generation module, and an execution feedback module.

2. The real-time risk control system for supply chain finance based on a dynamic game model according to claim 1, characterized in that: The data acquisition module is used to collect data from multiple channels involved in supply chain finance business.

3. The real-time risk control system for supply chain finance based on a dynamic game model according to claim 1, characterized in that: The data processing and analysis module is used to receive data from the data acquisition module, and the data processing and analysis module is also used to extract characteristic indicators that reflect the credit status and risk level of enterprises.

4. The real-time risk control system for supply chain finance based on a dynamic game model according to claim 1, characterized in that: The dynamic game model construction module is used to integrate the interests and decision-making behaviors of multiple parties in supply chain finance to construct a dynamic game model.

5. The real-time risk control system for supply chain finance based on a dynamic game model according to claim 1, characterized in that: The risk assessment module is used to combine the data provided in the data processing and analysis module with the game results output in the dynamic game model component module.

6. The real-time risk control system for supply chain finance based on a dynamic game model according to claim 1, characterized in that: The risk control strategy generation module is used to formulate targeted risk control strategies based on the risk level and risk trend output by the risk assessment module, combined with the balance point of interests of all parties obtained by analyzing the dynamic game model constructed by the dynamic game model construction module.

7. The real-time risk control system for supply chain finance based on a dynamic game model according to claim 1, characterized in that: The execution feedback module is used to convey the strategies formulated by the risk control strategy generation module to the relevant execution entities and to track the execution status of the strategies.

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