Artificial intelligence-based factoring contract service system

Abstract

The invention discloses a factoring contract service system based on artificial intelligence, and belongs to the technical field of factoring services, and the system comprises a data storage module which builds a database and a local knowledge base, carries out the classified storage of enterprise information, laws and regulations, industry regulations, contract templates, historical judicial cases and historical file contracts, and stores the enterprise information, the laws and regulations, the industry regulations, the contract templates and the historical file contracts; the intelligent file identification module is used for extracting, identifying and verifying information contents of file contracts, judging whether the file contracts are compliant or not, identifying non-compliant texts, judging risk levels, analyzing risk reasons and finally providing text contents after rectification and compliance based on contract review rules, and the enterprise query and auditing module is used for querying and auditing enterprise information. The intelligent contract generation module deploys an intelligent contract large model and reads in contract variables to generate a factoring electronic contract, the online contract signing module uploads, verifies and registers the factoring electronic contract, online signing is carried out by a financing enterprise, a platform operator and a fund party, the contract generation and signing efficiency is improved, and the legal risk is reduced.

Description

Technical Field

[0001] This application belongs to the technical field of factoring services. Specifically, it relates to a factoring contract service system based on artificial intelligence. Background Art

[0002] Factoring business is a comprehensive financial service based on enterprise accounts receivable. Currently, it is the type with the largest potential scale in the supply chain finance market. The core of the factoring business is that the financing enterprise transfers its unexpired accounts receivable to the factor, and the factor provides the financing enterprise with commercial factoring financial solutions, including investment and financing solutions, asset management solutions, risk management solutions, collection solutions, etc. The financing enterprise can thus quickly obtain funds and at the same time transfer the collection risk.

[0003] With the accelerating development of the factoring business, the mode of a large number of manual operations and manual reviews has resulted in a long process, low efficiency, labor-intensive, and high-risk in the asset service stage, directly hindering the growth of the business scale. For example, the traditional factoring asset service platform has the following problems: it takes a long time and is prone to omissions in manually verifying the authenticity of enterprise assets, and the efficiency of manual inspection and registration with the China Trustee Association is low. Summary of the Invention

[0004] To solve the above problems and technical deficiencies, the present application adopts the following technical solutions. A factoring contract service system based on artificial intelligence includes:

[0005] A data storage module, used to establish an enterprise information database, a laws and regulations database, a local knowledge base, and a document contract database, and classify and store enterprise information, laws and regulations, industry regulations, contract templates, historical judicial cases, and historical document contracts;

[0006] A document intelligent recognition module, used to extract, identify, and verify the information content of the document contract provided by the financing enterprise, determine whether the document contract is compliant, mark out non-compliant texts, determine the risk level and analyze the risk reasons, and finally, based on the contract review rules, give the text content after rectification and compliance;

[0007] An enterprise query and review module, used to query and review the enterprise information of the buyer and seller enterprises in the document contract, and determine whether the enterprise meets the conditions for factoring signing. The enterprise information query and review includes:

[0008] Extract the capital chain data of the upstream and downstream enterprises of the target enterprise from the enterprise information and establish an enterprise associated transaction graph;

[0009] Use the enterprise associated transaction graph to detect the enterprise financial statements, identify and extract the accounts receivable period and abnormal capital transactions, detect the abnormal capital transactions through federated learning, and obtain the enterprise's liability situation;

[0010] Use the Monte Carlo method to predict the corporate debt situation and the account period of accounts receivable, and obtain the probability of the capital gap of the target enterprise within the preset period;

[0011] Judge whether the enterprise meets the signing conditions of the factoring service according to the probability of the capital gap. If it meets the signing conditions of the factoring service, generate a capital risk strategy according to the requirements;

[0012] The contract intelligent generation module is used to deploy an intelligent contract large model, combine the content extracted from the document contract and the enterprise information audit result, and input contract variables for enterprises that meet the factoring signing conditions to generate factoring electronic contracts;

[0013] The contract online signing module is used to upload, verify and register the factoring electronic contract, and the financing enterprise, the platform operator and the capital provider conduct online signing.

[0014] Preferably, the document intelligent recognition module includes: a document extraction module, an information classification module and a content verification module;

[0015] The document extraction module is used to identify, extract and convert the key content in paper documents and electronic documents, identify and extract the text, pictures, seals and signatures in the documents, and convert them into corresponding formats;

[0016] The information classification module is used to analyze the content extracted and converted through natural language processing algorithms, and classify the content extracted and converted according to the analysis results;

[0017] The content verification module is used to query the network and combine the data stored in the data storage module according to the information classification result, verify the key contract content in the target classification, judge whether the document contract is compliant, and generate a risk prompt if it is not compliant.

[0018] Furthermore, the process of the content verification module for verification is as follows:

[0019] Conduct network retrieval, update the laws and regulations in the laws and regulations database, use natural language processing algorithms to extract the updated laws and regulations, and construct a dynamic contract knowledge graph;

[0020] Use the graph neural network model to analyze the dependency relationship between contract clauses in the key contract content, and detect potential logical contradictions between contract clauses;

[0021] Use the dynamic contract knowledge graph to associate historical judicial cases in the data storage module, analyze the potential logical contradictions between contract clauses, obtain potential legal dispute points of contract clauses, and generate risk prompts.

[0022] Furthermore, the process of judging whether the enterprise meets the signing conditions of the factoring service is as follows:

[0023] Three capital gap thresholds are preset, namely the first capital gap threshold, the second capital gap threshold, and the third capital gap threshold. The first capital gap threshold is greater than the second capital gap threshold, and the second capital gap threshold is greater than the third capital gap threshold;

[0024] If the probability of capital gap is greater than the first capital gap threshold, it is determined that the target enterprise's capital chain has a high risk of breakage and does not meet the signing conditions for factoring services;

[0025] If the probability of capital gap is less than or equal to the first capital gap threshold and greater than the second capital gap threshold, it is determined that the target enterprise's capital has an intermediate risk of breakage and does not meet the signing conditions for factoring services. However, a capital risk strategy needs to be generated and sent to the administrator for manual judgment on whether the enterprise meets the signing conditions for factoring services;

[0026] If the probability of capital gap is less than or equal to the second capital gap threshold and greater than the third capital gap threshold, it is determined that the target enterprise's capital has a low risk of breakage, meets the signing conditions for factoring services, and a capital risk strategy needs to be generated;

[0027] If the probability of capital gap is less than or equal to the third capital gap threshold, it is determined that the target enterprise's capital does not have a risk of breakage, meets the signing conditions for factoring services, and no capital risk strategy needs to be generated.

[0028] Furthermore, the generation process of the capital risk strategy is as follows:

[0029] Obtain the potential logical contradictions between contract terms, and calculate the allocated financing funds in combination with the account period matching degree;

[0030] Calculate the factoring capital cost based on the factoring financing interest rate for the allocated financing funds;

[0031] Use the capital routing optimization algorithm to calculate and process the factoring capital cost to obtain the factoring capital risk hedging result, and generate a capital risk strategy based on the factoring capital risk hedging result.

[0032] Preferably, the generation process of the factoring electronic contract is as follows:

[0033] Deploy an intelligent contract model, embed the internal factoring service rules of the factor, and perform localization processing on the intelligent contract model;

[0034] Obtain the contract template of the factoring contract, and fill in the regular contract terms according to the enterprise information;

[0035] Conduct a capital risk assessment on the probability of capital gap and the capital risk strategy, and fill in the contract amount value according to the capital risk assessment result;

[0036] Generate risk blocking clauses based on the contract amount value and potential legal dispute points in the contract terms;

[0037] Associate with updated laws and regulations, identify and adjust the clause conflicts among the general contract terms, contract amount values, and risk blocking clauses to make them consistent and compliant with business rationality, and finally generate a factoring e - contract.

[0038] Furthermore, the contract online signing module first adds an anti - tampering hash encryption value to the factoring e - contract and then uploads it. When signing, the signing party uses a mobile digital certificate for signing, and at the same time, a trusted time stamp is automatically added, and it is uploaded to the judicial blockchain in real - time after signing.

[0039] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the content of the above - mentioned factoring contract service system based on artificial intelligence.

[0040] A computer - readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the content of the above - mentioned factoring contract service system based on artificial intelligence.

[0041] Compared with the prior art, the beneficial effects of this application are:

[0042] (1) Through the document intelligent recognition module of this application, the information content of the document contract provided by the financing enterprise is extracted, recognized, and verified to judge whether the document contract is compliant. By identifying and detecting the document contract, it is judged whether the document contract is compliant, a dynamic contract knowledge graph is constructed, and potential logical contradictions between contract terms are detected, so as to avoid the factoring provider's assets being damaged due to potential risks in the contract to be transferred.

[0043] (2) Through the enterprise query and audit module of this application, the enterprise information is queried and audited to judge whether the enterprise meets the conditions for factoring signing, an enterprise related - transaction graph is established, abnormal fund transfers are detected through federated learning, the enterprise's debt situation is obtained, the Monte Carlo method is used to predict the enterprise's debt situation and the accounts receivable period, and the probability of the target enterprise's capital gap within the preset period is obtained to judge whether the enterprise meets the conditions for factoring service signing, so that the factoring provider will not sign a contract with a risky financing enterprise, reducing factoring risks.

[0044] (3) Through the contract intelligent generation module of this application, an intelligent contract model is deployed. According to the content extracted from the document contract and the enterprise information audit results, a factoring e - contract is generated for the enterprise that meets the conditions for factoring signing. By deploying the intelligent contract model, embedding the internal factoring service rules of the factoring provider, and localizing the intelligent contract model, the contract generation and signing efficiency are improved. Brief Description of the Drawings

[0045] In the drawings:

[0046] Figure 1 is a schematic diagram of the system structure of the embodiment of the present application;

[0047] Figure 2 is a schematic diagram of the device structure of the embodiment of the present application. Detailed Embodiments

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0049] Embodiment 1

[0050] As Figure 1 shown, a factoring contract service system based on artificial intelligence includes:

[0051] A data storage module for establishing an enterprise information database, a laws and regulations database, a local knowledge base, and a document contract database, and classifying and storing enterprise information, laws and regulations, industry regulations, contract templates, historical judicial cases, and historical document contracts;

[0052] A document intelligent recognition module for extracting, recognizing, and verifying the information content of the document contracts provided by the financing enterprise, determining whether the document contracts are compliant, identifying non-compliant texts, determining the risk level and analyzing the risk causes, and finally giving the text content after rectification and compliance based on the contract review rules;

[0053] The document intelligent recognition module includes: a document extraction module, an information classification module, and a content verification module;

[0054] A document extraction module for recognizing, extracting, and converting the key content in paper documents and electronic documents, recognizing and extracting the text, pictures, seals, and signatures in the documents, converting them into corresponding formats, using OCR technology to recognize the text of paper contract scans, and combining image processing algorithms to extract the seal and signature features;

[0055] An information classification module for analyzing the extracted and converted content through natural language processing algorithms, classifying the extracted and converted content according to the analysis results, using the BERT natural language processing model to perform semantic analysis on the contract content, and automatically marking keyword fields such as contract type, amount, and account period;

[0056] The content verification module is used to query the network according to the information classification result, combine the data stored in the data storage module, verify the key contract content in the target classification, judge whether the document contract is compliant, and if not, generate a risk prompt. For example, it calls the legal regulations database to detect whether the liquidated damages ratio exceeds four times the LPR stipulated in the Civil Code. If it exceeds the limit, it is marked as a "high-risk clause" and a prompt "The liquidated damages rate is too high. It is recommended to adjust it to ≤0.03%" is generated.

[0057] The process of verification by the content verification module is as follows:

[0058] Conduct an online search, update the laws and regulations in the legal regulations database, extract the updated laws and regulations using natural language processing algorithms, and construct a dynamic contract knowledge graph;

[0059] Use a graph neural network model to analyze the dependency relationships between contract clauses in the key contract content and detect potential logical contradictions between contract clauses;

[0060] Use the dynamic contract knowledge graph to associate with historical judicial cases in the data storage module, analyze the potential logical contradictions between contract clauses, obtain potential legal dispute points of contract clauses, and generate risk prompts.

[0061] The enterprise query and review module is used to query and review the enterprise information of the buyer and seller in the document contract to judge whether the enterprise meets the conditions for factoring signing;

[0062] Enterprise information includes: name, type, financial statements, business license, legal person, registered address, business date, judicial dispute cases, and litigation information.

[0063] Conducting enterprise information query and review includes:

[0064] Extract the upstream and downstream enterprise capital chain data of the target enterprise from the enterprise information and establish an enterprise related-party transaction graph;

[0065] Use the enterprise related-party transaction graph to detect the enterprise's financial statements, identify and extract the accounts receivable period and abnormal fund transactions, detect abnormal fund transactions through federated learning, and obtain the enterprise's liability situation;

[0066] Use the Monte Carlo method to predict the enterprise's liability situation and accounts receivable period, and obtain the probability of the target enterprise's capital gap within the preset period;

[0067] Judge whether the enterprise meets the conditions for factoring service signing according to the probability of the capital gap. If it meets the conditions for factoring service signing, generate a capital risk strategy according to the requirements.

[0068] The process of determining whether an enterprise meets the signing conditions for factoring services is as follows:

[0069] Preset three capital gap thresholds, namely the first capital gap threshold, the second capital gap threshold, and the third capital gap threshold. The first capital gap threshold is greater than the second capital gap threshold, and the second capital gap threshold is greater than the third capital gap threshold;

[0070] If the probability of the capital gap is greater than the first capital gap threshold, it is determined that the target enterprise's capital chain has a high risk of breakage and does not meet the signing conditions for factoring services;

[0071] If the probability of the capital gap is less than or equal to the first capital gap threshold and greater than the second capital gap threshold, it is determined that the target enterprise's capital has a medium risk of breakage and does not meet the signing conditions for factoring services. However, a capital risk strategy needs to be generated and sent to the administrator for manual judgment on whether the enterprise meets the signing conditions for factoring services;

[0072] If the probability of the capital gap is less than or equal to the second capital gap threshold and greater than the third capital gap threshold, it is determined that the target enterprise's capital has a low risk of breakage, meets the signing conditions for factoring services, and a capital risk strategy needs to be generated;

[0073] If the probability of the capital gap is less than or equal to the third capital gap threshold, it is determined that the target enterprise's capital does not have a risk of breakage, meets the signing conditions for factoring services, and no capital risk strategy needs to be generated.

[0074] The process of generating a capital risk strategy is as follows:

[0075] Obtain the potential logical contradictions between contract terms, and calculate the allocated financing funds by combining the account period matching degree;

[0076] Calculate the factoring capital cost based on the factoring financing interest rate for the allocated financing funds;

[0077] Use the capital routing optimization algorithm to calculate and process the factoring capital cost to obtain the factoring capital risk hedging result, and generate a capital risk strategy based on the factoring capital risk hedging result.

[0078] For example, extract the financial statements of buyer Company B in the past 3 years from the enterprise information database, construct an associated transaction graph with its upstream supplier C and downstream distributor D, and find that the accounts receivable period of Company B from Company D has been extended from 90 days to 150 days.

[0079] Through federated learning to jointly analyze the capital flows of Company B and its associated enterprises, it is detected that Company B has transferred 2 million yuan to related party Company E abnormally in the past 6 months without indicating the purpose.

[0080] Use Monte Carlo simulation to predict the probability of the funding gap of Company B in the next 12 months. The input variables are as follows: the mean of the accounts receivable recovery rate is 80%, the standard deviation is 10%, the short-term debt is 3 million yuan, and the operating cost is 500,000 yuan per month;

[0081] The first funding gap threshold is 30%, the second funding gap threshold is 10%, and the third funding gap threshold is 5%;

[0082] The output result shows that the probability of the funding gap is 8%, which is lower than the second threshold of 10%. It is determined to be a low risk, allowing signing, but the generated risk strategy is to recommend that the factoring financing amount does not exceed 70% of the accounts receivable.

[0083] The contract intelligent generation module is used to deploy the intelligent contract large model. Combining the content extracted from the document contract and the enterprise information audit results, it generates factoring electronic contracts for enterprises that meet the conditions for factoring signing by inputting contract variables;

[0084] The process of generating factoring electronic contracts is as follows:

[0085] Deploy the intelligent contract model, embed the internal factoring service rules of the factor, and localize the intelligent contract model;

[0086] Obtain the contract template of the factoring contract and fill in the regular contract terms according to the enterprise information;

[0087] Conduct a funding risk assessment on the probability of the funding gap and the funding risk strategy, and fill in the contract amount value according to the funding risk assessment result;

[0088] Generate a risk blocking clause based on the contract amount value and the potential legal dispute points of the contract terms;

[0089] Associate with the updated laws and regulations, identify and adjust the clause conflicts of the regular contract terms, contract amount value, and risk blocking clause to make them consistent and comply with business rationality, and finally generate factoring electronic contracts.

[0090] For example, retrieve the template of the "Non-recourse Factoring Contract" from the template library and automatically fill in the fields. The fields are: "Financing Party: Company X, Factor: Bank Y, Amount: 3.5 million yuan (5 million × 70%)";

[0091] Then insert the risk blocking clause, and the risk blocking clause is: "If the buyer, Company B, is overdue for more than 60 days, the factor has the right to require the financing party to repurchase the accounts receivable";

[0092] Finally, check the clause consistency. It is detected that the original "non-recourse" clause in the template conflicts with the newly added repurchase clause, and the template is automatically revised to a "recourse factoring contract" and the user is prompted to confirm.

[0093] The online contract signing module is used to upload, verify, and register factoring electronic contracts, and online signing is carried out by the financing enterprise, the platform operator, and the capital provider.

[0094] The online contract signing module first adds a tamper-proof hash encryption value to the factoring electronic contract and then uploads it. When signing the contract, the signatory uses a mobile digital certificate to sign, and at the same time, a trusted time stamp is automatically added. After signing, it is uploaded to the judicial blockchain in real time.

[0095] The legal representative of the financing enterprise uses the mobile phone CA certificate to sign on the electronic contract, and the time stamp "2023-08-20 14:30:00 UTC+8" is automatically attached. The contract hash value ABC-123:d4e5f6... is uploaded to the judicial chain of the people's court in real time, and a deposit certificate number such as (2025) SiLian CunZi No.... is generated. The automatic registration interface of the China Bonded Warehouse Network synchronizes the key information of the factoring contract to the central bank's unified registration system for movable property financing.

[0096] Embodiment 2:

[0097] As Figure 2 shown, from the hardware level, the present application provides an embodiment of an electronic device for all or part of the content in the factoring contract service system based on artificial intelligence. The electronic device includes a service processor and a distributed memory. The service processor is connected to the memory. The distributed memory stores a service self-management program, configured to store machine-readable instructions. The service processor executes the service self-management program. When the instructions are executed by the processor, the factoring contract service system based on artificial intelligence as described above is implemented.

[0098] From the hardware level, in order to effectively improve the flexibility, versatility, and acquisition efficiency of data acquisition, the present application provides an embodiment of an electronic device for all or part of the content in the factoring contract service system based on artificial intelligence. The electronic device specifically includes the following:

[0099] A processor, a memory, a communication interface, and a bus; wherein, the processor, the memory, and the communication interface complete communication with each other through the bus; the communication interface is used to realize information transmission between the data acquisition device based on the distributed model and related devices such as the core business system, the user terminal, and the relevant database. The logic controller can be a desktop computer, a tablet computer, a mobile terminal, etc. This embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to the embodiments of the factoring contract service system based on artificial intelligence and the embodiments of the data acquisition device based on the distributed model, and the content is incorporated herein, and the repeated parts will not be described again.

[0100] It can be understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0101] In practical applications, the part of the academic view annotation and analysis method can be executed on the side of the electronic device as described above, or all operations can be completed in the client device. Specifically, it can be selected according to the processing ability of the client device and the limitations of the user usage scenario, etc. The present application does not make a limitation in this regard. If all operations are completed in the client device, the client device may further include a processor.

[0102] The above-mentioned client device may have a communication module (i.e., a communication unit), and can communicate with a remote server to realize data transmission with the server. The server may include a server on the side of the task scheduling center, and may also include a server of an intermediate platform in other implementation scenarios, such as a server of a third-party server platform having a communication link with the task scheduling center server. The server may include a single computer device, or may include a server cluster composed of multiple servers, or a server structure of a distributed device.

[0103] Embodiment 3

[0104] An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the contents in the above-mentioned artificial intelligence-based factoring contract service system with the execution subject being a server or a client. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements all the contents of the above-mentioned artificial intelligence-based factoring contract service system with the execution subject being a server or a client.

[0105] Embodiments of the present application may be provided as a method, a device, or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (devices), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce a means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0107] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0109] The above-described embodiments merely represent the preferred embodiments of the present application, and the description thereof is relatively specific and detailed. However, it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications, improvements, and substitutions can be made, and these all fall within the protection scope of the present application.

Claims

1. An artificial intelligence-based factoring contract service system, characterized in that, Including: A data storage module for establishing an enterprise information database, a laws and regulations database, a local knowledge base, and a document and contract database, and classifying and storing enterprise information, laws and regulations, industry regulations, contract templates, historical judicial cases, and historical document contracts; A document intelligent recognition module for extracting, recognizing, and verifying the information content of the document contracts provided by the financing enterprise, judging whether the document contracts are compliant, identifying non-compliant texts, determining the risk level and analyzing the risk causes, and finally giving the text content after rectification and compliance based on the contract review rules; An enterprise query and review module for querying and reviewing the enterprise information of the buyer and seller enterprises in the document contract to judge whether the enterprise meets the conditions for factoring signing. The enterprise information query and review includes: Extracting the capital chain data of the upstream and downstream enterprises of the target enterprise from the enterprise information and establishing an enterprise associated transaction graph; Using the enterprise associated transaction graph to detect the enterprise financial statements, identifying and extracting the accounts receivable period and abnormal fund transactions, detecting the abnormal fund transactions through federated learning, and obtaining the enterprise liability situation; Using the Monte Carlo method to predict the enterprise liability situation and the accounts receivable period to obtain the probability of the capital gap of the target enterprise within the preset period; Judging whether the enterprise meets the conditions for factoring service signing according to the probability of the capital gap. If it meets the conditions for factoring service signing, generating a capital risk strategy according to the requirements; A contract intelligent generation module for deploying an intelligent contract large model, combining the extracted content of the document contract and the enterprise information review result, and inputting contract variables for the enterprises that meet the conditions for factoring signing to generate factoring electronic contracts; A contract online signing module for uploading, verifying, and registering the factoring electronic contract, and conducting online signing by the financing enterprise, the platform operator, and the fund provider.

2. The factoring contract service system based on artificial intelligence according to claim 1 is characterized in that, The document intelligent recognition module includes: a document extraction module, an information classification module, and a content verification module; A document extraction module for identifying, extracting, and converting the key content in paper documents and electronic documents, identifying and extracting the text, pictures, seals, and signatures in the documents, and converting them into corresponding formats; An information classification module for analyzing the extracted and converted content through natural language processing algorithms and classifying the extracted and converted content according to the analysis results; A content verification module for verifying the key contract content in the target classification by querying the network and combining the data stored in the data storage module according to the information classification result, judging whether the document contract is compliant, and generating a risk prompt if it is not compliant.

3. The factoring contract service system based on artificial intelligence according to claim 2, wherein The process of verification by the content verification module is as follows: Conducting network retrieval, updating the laws and regulations in the laws and regulations database, using natural language processing algorithms to extract the updated laws and regulations, and constructing a dynamic contract knowledge graph; Using a graph neural network model to analyze the dependency relationship between contract clauses in the key contract content and detecting potential logical contradictions between contract clauses; Using the dynamic contract knowledge graph to associate with the historical judicial cases in the data storage module, analyzing the potential logical contradictions between contract clauses, obtaining the potential legal dispute points of the contract clauses, and generating a risk prompt.

4. The factoring contract service system based on artificial intelligence according to claim 1, characterized in that, The process of determining whether an enterprise meets the signing conditions for factoring services is as follows: Preset three capital gap thresholds, namely the first capital gap threshold, the second capital gap threshold, and the third capital gap threshold. The first capital gap threshold is greater than the second capital gap threshold, and the second capital gap threshold is greater than the third capital gap threshold; If the probability of the capital gap is greater than the first capital gap threshold, it is determined that the target enterprise's capital chain has a high risk of breakage and does not meet the signing conditions for factoring services; If the probability of the capital gap is less than or equal to the first capital gap threshold and greater than the second capital gap threshold, it is determined that the target enterprise's capital has a medium risk of breakage and does not meet the signing conditions for factoring services. However, a capital risk strategy needs to be generated and sent to the administrator for manual judgment on whether the enterprise meets the signing conditions for factoring services; If the probability of the capital gap is less than or equal to the second capital gap threshold and greater than the third capital gap threshold, it is determined that the target enterprise's capital has a low risk of breakage, meets the signing conditions for factoring services, and a capital risk strategy needs to be generated; If the probability of the capital gap is less than or equal to the third capital gap threshold, it is determined that the target enterprise's capital does not have a risk of breakage, meets the signing conditions for factoring services, and no capital risk strategy needs to be generated.

5. The factoring contract service system based on artificial intelligence according to claim 1, characterized in that, The generation process of the capital risk strategy is as follows: Obtain the potential logical contradictions between contract terms and calculate the allocated financing funds in combination with the account period matching degree; Calculate the factoring capital cost based on the factoring financing rate for the allocated financing funds; Use the capital routing optimization algorithm to calculate and process the factoring capital cost to obtain the factoring capital risk hedging result, and generate a capital risk strategy based on the factoring capital risk hedging result.

6. The factoring contract service system based on artificial intelligence according to claim 1 is characterized in that, The generation process of the factoring electronic contract is as follows: Deploy an intelligent contract model, embed the internal factoring service rules of the factor, and perform localization processing on the intelligent contract model; Obtain the contract template of the factoring contract and fill in the regular contract terms according to the enterprise information; Conduct a capital risk assessment on the probability of the capital gap and the capital risk strategy, and fill in the contract amount value according to the capital risk assessment result; Generate a risk blocking clause based on the contract amount value and the potential legal dispute points of the contract terms; Associate with the updated laws and regulations, identify and adjust the clause conflicts of the regular contract terms, contract amount value, and risk blocking clause to make them consistent and meet business rationality, and finally generate a factoring electronic contract.

7. The factoring contract service system based on artificial intelligence according to claim 1, wherein The contract online signing module first adds an anti-tampering hash encryption value to the factoring electronic contract and then uploads it. When signing, the signing party uses a mobile digital certificate for signing, and at the same time, a trusted time stamp is automatically added, and it is uploaded to the judicial blockchain in real time after signing.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the content of the artificial intelligence-based factoring contract service system described in claim 1.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the content of the artificial intelligence-based factoring contract service system described in claim 1.

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