Supply chain financial cashing risk prevention and control system and method based on multiple verification mechanisms

Abstract

The invention discloses a supply chain financial cashing risk prevention and control system and method based on a multi-check mechanism in the technical field of supply chain finance. The method comprises the following steps: receiving supply chain financial cashing risk detection acquisition data input externally; data are collected based on supply chain financial cashing risk detection, and data processing is carried out through a multi-stage verification engine; performing supply chain financial cashing risk decision judgment based on a data processing result; and executing a corresponding supply chain financial cashing risk prevention and control action based on the decision judgment result. According to the method, closed-loop prevention and control of supply chain financial cashing risks are realized by constructing a multi-stage dynamic verification engine, risks such as repeated payment, state conflicts or missed payment during manual processing of cashing applications are avoided, the burden of manual recheck is greatly reduced, and the advantage of resource collaboration is embodied.

Description

Technical Field

[0001] This invention relates to a supply chain finance payment risk control system and method based on a multi-verification mechanism, belonging to the field of supply chain finance technology. Background Technology

[0002] Supply chain finance involves buyers and sellers, as well as related entities such as banks, logistics companies, commerce firms, insurance companies, agents, and consulting firms. Its main forms include accounts receivable financing, warehouse receipt financing, and confirmed warehouse receipt financing. In the digital economy era, supply chain finance, as a bridge connecting the real economy and financial services, is becoming an important tool for enterprises to improve capital efficiency and optimize supply chain management. It is characterized by its direct reach and can effectively remove bottlenecks for SMEs in the industrial chain.

[0003] In the traditional supply chain finance platform's redemption process, manual processing of redemption applications is prone to risks such as duplicate payments, status conflicts, or missed payments. Existing technologies lack a risk control mechanism for supply chain finance redemption and cannot meet actual needs. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a supply chain finance payment risk prevention and control system and method based on a multi-verification mechanism. By constructing a multi-level dynamic verification engine, it realizes closed-loop prevention and control of supply chain finance payment risks, avoids risks such as duplicate payments, status conflicts or missed payments when manually processing payment applications, greatly reduces the burden of manual review, and reflects the advantages of resource synergy.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] Firstly, this invention provides a supply chain finance payment risk prevention and control method based on a multi-verification mechanism, including:

[0007] Receive externally input data for supply chain finance payment risk detection and collection;

[0008] Based on the data collected for supply chain finance repayment risk detection, the data is processed through a multi-level verification engine.

[0009] Decision-making and assessment of supply chain finance repayment risks based on data processing results;

[0010] Based on the decision-making results, corresponding supply chain finance payment risk prevention and control actions will be implemented.

[0011] Furthermore, the data collected for supply chain finance repayment risk detection includes the operational instructions of core enterprise users in the repayment process, the bill lifecycle data provided by the bill status machine of the supply chain platform, and the credit characteristics pushed by the external credit reporting system. The credit characteristics include the enterprise's historical default rate and transaction frequency.

[0012] Furthermore, data processing through a multi-level verification engine includes:

[0013] By connecting offline payment vouchers and online application records through a fund flow conflict detection engine, duplicate payment judgment marks are generated based on the duplicate payment verification factor calculation model to intercept duplicate payments from multiple channels.

[0014] By linking ticket status with user operations through the state consistency verification engine, the system calculates and identifies the mismatch risk between lifecycle and instructions based on the conflict factor and state conflict risk index calculation model.

[0015] By integrating the outputs of the duplicate payment verification factor calculation model and the state conflict risk index calculation model through the risk quantification decision engine, and combining them with the enterprise's credit characteristics, a risk score is dynamically generated based on the comprehensive repayment risk scoring calculation model.

[0016] Furthermore, the formula for calculating the duplicate payment verification factor is as follows:

[0017]

[0018] Where: DCF is the duplicate payment verification factor; S pd This is a supplementary registration indicator; 0 indicates no supplementary registration, and 1 indicates supplementary registration. re The status indicates the payment period has begun; True means the payment period has started. rec F is used to identify offline payment records. rec >0 indicates that a payment voucher exists.

[0019] Furthermore, the formula for calculating the state conflict risk index is as follows:

[0020]

[0021] In the formula: SCI is the state conflict risk index; W p W a For weighting coefficients; I pexp For early redemption conflict indicators; I auto Automatic redemption conflict identifier; δ status This represents the weight of the ticket status.

[0022] Furthermore, the formula for calculating the comprehensive risk assessment of repayment is as follows:

[0023]

[0024] In the formula: CRS is the comprehensive score for repayment risk; α, β, and γ are dynamic coefficients; C defhist The number of historical defaults by the core enterprise; T totalThe total number of transactions for the enterprise; DCF is the duplicate payment verification factor; SCI is the state conflict risk index.

[0025] Furthermore, based on the decision-making results, corresponding supply chain finance payment risk prevention and control actions are implemented, including:

[0026] When the duplicate payment verification factor DCF = 1, the current payment application will be automatically intercepted and the supplementary registration process will be triggered; otherwise, no processing will be performed.

[0027] When the State Conflict Risk Index (SCI) > 0.6, the current operation is frozen and an alert is issued to the supervisor for manual review; otherwise, no action is taken.

[0028] When the comprehensive risk score (CRS) for repayment is less than 0.3, automatic release is performed; when 0.3 ≤ CRS ≤ 0.7, the process is transferred to the manual review queue; when CRS > 0.7, the operation is suspended and a compliance audit is triggered.

[0029] Secondly, this invention provides a supply chain finance payment risk control system based on a multi-verification mechanism, comprising:

[0030] Data receiving module: Receives externally input supply chain finance payment risk detection and collection data;

[0031] Data processing module: Based on the data collected for supply chain finance payment risk detection, the data is processed through a multi-level verification engine;

[0032] Decision-making module: Makes decisions on supply chain finance repayment risks based on data processing results;

[0033] Decision Execution Module: Based on the decision judgment results, execute the corresponding supply chain finance payment risk prevention and control actions.

[0034] Thirdly, the present invention provides a supply chain finance payment risk control device based on a multi-verification mechanism, including a processor and a storage medium;

[0035] The storage medium is used to store instructions;

[0036] The processor is configured to operate according to the instructions to perform the steps of the method according to any of the foregoing.

[0037] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0038] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0039] I. This solution achieves closed-loop control of supply chain finance payment risks by constructing a multi-level dynamic verification engine, avoiding risks such as duplicate payments, status conflicts, or missed payments when manually processing payment applications. The multi-level dynamic verification engine is designed to be scalable to access third-party data sources such as blockchain notarization and central bank credit reporting. Quantitative scoring drives automated decision-making levels, including release / review / interception, which greatly reduces the burden of manual review and demonstrates the advantages of resource synergy.

[0040] Second, this solution addresses the risk of funding channel conflicts through a duplicate payment verification factor, eliminates the risk of mismatch between lifecycle and operational behavior through a status conflict risk index, and dynamically quantifies corporate credit risk by integrating external credit information through a comprehensive redemption risk score, thus dynamically defending against multiple risks. At the same time, it upgrades post-event auditing to in-event interception, automatically freezes high-risk operations and recommends remedial actions, eliminates the need for manual cross-system data comparison, and achieves closed-loop process optimization. Attached Figure Description

[0041] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0042] Figure 1 This is a flowchart illustrating the supply chain finance payment risk control method based on a multi-verification mechanism provided in Embodiment 1 of the present invention. Detailed Implementation

[0043] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0044] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0045] Example 1:

[0046] This embodiment proposes a supply chain finance payment risk control method based on a multi-verification mechanism. It achieves closed-loop control of supply chain finance payment risk by constructing a three-level dynamic verification engine. During the data receiving phase, the system collects data from three sources in real time: bill information provided by the bill lifecycle state machine, such as payment period status and maturity date; manual actions recorded in user operation logs, such as payment application / registration instructions; and historical credit behavior of enterprises pushed by external systems, such as credit reporting platforms. The data processing process is executed collaboratively by three core engines: state consistency verification, fund flow conflict detection, and risk quantification decision-making.

[0047] I. The fund flow conflict detection engine connects with offline payment vouchers and online application records. Based on the duplicate payment verification factor calculation model, it generates a duplicate payment judgment flag to intercept duplicate payments from multiple channels. The formula for calculating the duplicate payment verification factor is:

[0048]

[0049] Where: DCF is the duplicate payment verification factor; S pd This is a supplementary registration indicator; 0 indicates no supplementary registration, and 1 indicates supplementary registration. re The status indicates the payment period has begun; True means the payment period has started. rec F is used to identify offline payment records. rec >0 indicates that a payment voucher exists; when DCF=1, the current redemption application is automatically intercepted and the supplementary registration process is triggered; otherwise, no processing is performed.

[0050] II. The State Consistency Verification Engine is used to associate the status of bills with user operations. It calculates and identifies the mismatch risk between lifecycle and instructions through the conflict factor and state conflict risk index calculation model. For example, when a bill that has not yet matured is initiated for redemption, the formula for calculating the state conflict risk index is as follows:

[0051]

[0052] In the formula: SCI is the state conflict risk index; W p W a The weighting coefficient has a default value of W. p =0.7, W a =0.9; I pexp For early redemption conflict indicators; I auto Automatic redemption conflict identifier; δ status This is the weight of the invoice status; when SCI > 0.6, the current operation is frozen and an alert is issued to the supervisor for manual review; otherwise, no action is taken.

[0053] III. The risk quantification decision engine integrates the output results of the first two stages and the enterprise's credit characteristics, and dynamically generates a risk score based on the comprehensive repayment risk scoring model. The formula for calculating the comprehensive repayment risk score is as follows:

[0054]

[0055] In the formula: CRS is the comprehensive score for repayment risk; α, β, and γ are dynamic coefficients, with initial values ​​of α = 0.5, β = 0.3, and γ = 0.2, which are dynamically adjusted through a machine learning model; C defhist The number of historical defaults by the core enterprise; T total This represents the total number of transactions for the enterprise. When CRS < 0.3, automatic release is performed; when 0.3 ≤ CRS ≤ 0.7, the transaction is transferred to the manual review queue; when CRS > 0.7, the operation is suspended and a compliance audit is triggered.

[0056] During the data output phase, the system triggers a three-tiered response based on the scoring results: low-risk instructions are automatically released; medium-risk operations are frozen and pushed to the manual review queue, with correction suggestions generated simultaneously, such as guiding the transfer to a supplementary registration process; high-risk requests are immediately terminated and audit trails are activated. Ultimately, all verification logs, processing results, and changes in invoice status are synchronously updated to the central ledger, achieving closed-loop control of the entire process of "operation, verification, decision-making, and feedback."

[0057] This example illustrates a case where an online payment application was mistakenly initiated after an offline payment was made. The core enterprise has completed the offline payment for bill #YF20250001, which has entered the collection period, but the personnel handling the transaction mistakenly submitted an "online payment application." Verification process:

[0058] Step 1: The fund flow conflict detection engine calculates the DCF factor (S). pd =0, S re =True, F rec =1→DCF=1);

[0059] Step 2: State consistency engine detects operation conflicts (I pexp =0, I auto =0→SCI=0)

[0060] Step 3: The risk quantification engine calculates CRS (CRS = 0.5 × 1 + 0.3 × 0 + 0.2 × (2 / 100) = 0.504).

[0061] Because the CRS is 0.504 (in the range of 0.3 to 0.7), the system automatically freezes the application and pushes it to the supervisor's review queue; at the same time, a risk warning is generated: "Unregistered offline payment records have been detected. It is recommended to proceed with the registration process."

[0062] This solution addresses the risk of funding channel conflicts through DCF factors, eliminates the risk of mismatch between lifecycle and operational behavior through the SCI index, and dynamically quantifies corporate credit risk by integrating external credit information through CRS scoring, thus dynamically defending against multiple risks. Simultaneously, it upgrades post-event auditing to in-process interception, such as automatically freezing high-risk operations and recommending remedial actions, such as re-registration, eliminating the need for manual cross-system data comparison, such as comparing offline payment records with invoice status, achieving closed-loop process optimization. The multi-level dynamic verification engine design is scalable to access third-party data sources such as blockchain evidence storage and central bank credit information. Quantitative scoring drives automated decision-making levels, including release / review / interception, significantly reducing the burden of manual review and demonstrating the advantages of resource synergy.

[0063] Example 2:

[0064] The supply chain finance payment risk control system based on a multi-verification mechanism can implement the supply chain finance payment risk control method based on a multi-verification mechanism described in Example 1, including:

[0065] Data receiving module: Receives externally input supply chain finance payment risk detection and collection data;

[0066] Data processing module: Based on the data collected for supply chain finance payment risk detection, the data is processed through a multi-level verification engine;

[0067] Decision-making module: Makes decisions on supply chain finance repayment risks based on data processing results;

[0068] Decision Execution Module: Based on the decision judgment results, execute the corresponding supply chain finance payment risk prevention and control actions.

[0069] Example 3:

[0070] This invention also provides a supply chain finance payment risk control device based on a multi-verification mechanism, which can realize the supply chain finance payment risk control method based on a multi-verification mechanism described in Embodiment 1, including a processor and a storage medium;

[0071] The storage medium is used to store instructions;

[0072] The processor is configured to operate according to the instructions to perform the steps of the following method:

[0073] Receive externally input data for supply chain finance payment risk detection and collection;

[0074] Based on the data collected for supply chain finance repayment risk detection, the data is processed through a multi-level verification engine.

[0075] Decision-making and assessment of supply chain finance repayment risks based on data processing results;

[0076] Based on the decision-making results, corresponding supply chain finance payment risk prevention and control actions will be implemented.

[0077] Example 4:

[0078] This invention also provides a computer-readable storage medium that can implement the supply chain finance payment risk prevention and control method based on a multi-verification mechanism as described in Embodiment 1. The medium stores a computer program that, when executed by a processor, performs the following steps:

[0079] Receive externally input data for supply chain finance payment risk detection and collection;

[0080] Based on the data collected for supply chain finance repayment risk detection, the data is processed through a multi-level verification engine.

[0081] Decision-making and assessment of supply chain finance repayment risks based on data processing results;

[0082] Based on the decision-making results, corresponding supply chain finance payment risk prevention and control actions will be implemented.

[0083] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative and not exhaustive. All modifications within the scope of this invention or its equivalents are included in this invention.

[0084] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0085] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0086] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A supply chain finance payment risk control method based on multiple verification mechanisms, characterized by: include: Receive externally input data for supply chain finance payment risk detection and collection; Based on the data collected for supply chain finance repayment risk detection, the data is processed through a multi-level verification engine. Decision-making and assessment of supply chain finance repayment risks based on data processing results; Based on the decision-making results, corresponding supply chain finance payment risk prevention and control actions will be implemented.

2. The supply chain finance payment risk prevention and control method based on a multi-verification mechanism according to claim 1, characterized in that, The data collected for supply chain finance repayment risk detection includes the operation instructions of core enterprise users in the repayment process, the bill lifecycle data provided by the bill status machine of the supply chain platform, and the credit characteristics pushed by the external credit reporting system. The credit characteristics include the enterprise's historical default rate and transaction frequency.

3. The supply chain finance payment risk prevention and control method based on a multi-verification mechanism according to claim 1, characterized in that, Data processing through a multi-level verification engine includes: By connecting offline payment vouchers and online application records through a fund flow conflict detection engine, duplicate payment judgment marks are generated based on the duplicate payment verification factor calculation model to intercept duplicate payments from multiple channels. The status consistency verification engine links the ticket status with user operations, and calculates and identifies the mismatch risk between lifecycle and instruction based on the conflict factor and status conflict risk index calculation model. By integrating the outputs of the duplicate payment verification factor calculation model and the state conflict risk index calculation model through the risk quantification decision engine, and combining them with the enterprise's credit characteristics, a risk score is dynamically generated based on the comprehensive repayment risk scoring calculation model.

4. The supply chain finance payment risk prevention and control method based on a multi-verification mechanism according to claim 3, characterized in that, The formula for calculating the duplicate payment verification factor is as follows: In the formula: DCF is the duplicate payment verification factor; S pd This is a supplementary registration indicator; 0 indicates no supplementary registration, and 1 indicates supplementary registration. re The status indicates the payment period has begun; True means the payment period has started. rec F is used to identify offline payment records. rec >0 indicates that a payment voucher exists.

5. The supply chain finance payment risk prevention and control method based on a multi-verification mechanism according to claim 4, characterized in that, The formula for calculating the state conflict risk index is as follows: In the formula: SCI is the state conflict risk index; W p W a For weighting coefficients; I pexp For early redemption conflict indicators; I auto Automatic redemption conflict identifier; δ status This represents the weight of the ticket status.

6. The supply chain finance payment risk prevention and control method based on a multi-verification mechanism according to claim 5, characterized in that, The formula for calculating the comprehensive risk assessment of repayment is as follows: In the formula: CRS is the comprehensive score for repayment risk; α, β, and γ are dynamic coefficients; C defhist The number of historical defaults by the core enterprise; T total The total number of transactions for the enterprise; DCF is the duplicate payment verification factor; SCI stands for State Conflict Risk Index.

7. The supply chain finance payment risk prevention and control method based on a multi-verification mechanism according to claim 6, characterized in that, Based on the decision-making results, corresponding supply chain finance payment risk prevention and control actions will be implemented, including: When the duplicate payment verification factor DCF = 1, the current payment application will be automatically intercepted and the supplementary registration process will be triggered; otherwise, no action will be taken. When the State Conflict Risk Index (SCI) > 0.6, the current operation is frozen and an alert is issued to the supervisor for manual review; otherwise, no action is taken. When the comprehensive risk score (CRS) for repayment is less than 0.3, automatic release is performed; when 0.3 ≤ CRS ≤ 0.7, the process is transferred to the manual review queue; when CRS > 0.7, the operation is suspended and a compliance audit is triggered.

8. A supply chain finance payment risk control system based on a multi-verification mechanism, characterized by: include: Data receiving module: Receives externally input supply chain finance payment risk detection and collection data; Data processing module: Based on the data collected for supply chain finance payment risk detection, the data is processed through a multi-level verification engine; Decision-making module: Makes decisions on supply chain finance repayment risks based on data processing results; Decision Execution Module: Based on the decision judgment results, execute the corresponding supply chain finance payment risk prevention and control actions.

9. A supply chain finance payment risk control device based on a multi-verification mechanism, characterized in that: Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 7.

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