Dynamic interest rate calculation method and device based on credit risk assessment and medium

Abstract

The invention discloses a dynamic interest rate calculation method and device based on credit risk assessment and a medium. The method relates to the technical field of financial science and technology data processing, and comprises the following steps: obtaining a target enterprise of which the interest rate is to be calculated and joint dimension enterprise description data, and processing the joint dimension enterprise description data by using a multi-source data fusion engine processing module to obtain standard joint dimension enterprise description data; constructing a graph by using a dynamic ecological knowledge graph constructor, and generating a current enterprise description knowledge graph; an ecological health portrait engine processing module is used to analyze an input current node feature vector and a current edge weight matrix to obtain a current node risk probability and a current ecological health score; the current ecological health score is calculated through an interest rate calculation method, and the current enterprise calculation interest rate is obtained and fed back. The problems of low efficiency and inaccuracy of interest rate determination caused by information islands and static analysis are solved, and the timeliness and accuracy of dynamic interest rate calculation are improved.

Description

Technical Field

[0001] This invention relates to the field of financial technology data processing, and in particular to a method, apparatus and medium for calculating dynamic interest rates based on credit risk assessment. Background Technology

[0002] In the field of fintech data processing, banks need to verify the qualifications of various enterprises that require credit before issuing loans. In this credit process, the technical means to accurately determine the target loan interest rate are intuitively important.

[0003] In the process of developing this invention, the inventors discovered the following shortcomings in the existing technology: Currently, static risk scores are generally calculated using statistical models such as logistic regression based on historical financial statements, collateral values, and credit reports; or by analyzing the target company's transaction flow and tax data through machine learning to generate operational stability predictions, but this is still limited to individual analysis. This leads to information silos, static analysis results in an inability to respond promptly to market changes, and the relatively singular assessment dimensions lead to inaccurate interest rate settings, failing to achieve timely adjustments to interest rates. Summary of the Invention

[0004] This invention provides a method, apparatus, and medium for calculating dynamic interest rates based on credit risk assessment, so as to improve the timeliness and accuracy of dynamic interest rate calculation.

[0005] According to one aspect of the present invention, a dynamic interest rate calculation method based on credit risk assessment is provided, comprising:

[0006] Obtain the target company for which the interest rate is to be calculated, and the joint dimension company description data corresponding to the target company, and use the pre-built multi-source data fusion engine processing module to process the joint dimension company description data to obtain standard joint dimension company description data;

[0007] The dynamic interest rate calculation system based on credit risk assessment includes a pre-loan assessment stage; the pre-loan assessment stage includes a multi-source data fusion engine processing module, a dynamic ecological knowledge graph builder, and an ecological health profile engine processing module.

[0008] Based on the standard joint dimension enterprise description data, a pre-built dynamic ecological knowledge graph builder is used to construct the graph and generate the current enterprise description knowledge graph.

[0009] The ecological health profile engine processing module is used to analyze the current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score.

[0010] The current ecological health score is calculated using a preset interest rate calculation method to obtain the current enterprise calculation interest rate, and this current enterprise calculation interest rate is fed back to the user in real time.

[0011] According to another aspect of the present invention, a dynamic interest rate calculation device based on credit risk assessment is provided, comprising:

[0012] The standard joint dimension enterprise description data determination module is used to obtain the target enterprise for which the interest rate is to be calculated, and the joint dimension enterprise description data corresponding to the target enterprise, and to process the joint dimension enterprise description data using a pre-built multi-source data fusion engine processing module to obtain the standard joint dimension enterprise description data.

[0013] The dynamic interest rate calculation system based on credit risk assessment includes a pre-loan assessment stage; the pre-loan assessment stage includes a multi-source data fusion engine processing module, a dynamic ecological knowledge graph builder, and an ecological health profile engine processing module.

[0014] The current enterprise description knowledge graph generation module is used to construct the graph based on the standard joint dimension enterprise description data and a pre-built dynamic ecological knowledge graph builder to generate the current enterprise description knowledge graph.

[0015] The current node risk probability and current ecological health score determination module is used to use the ecological health profile engine processing module to parse the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph, and obtain the current node risk probability and current ecological health score.

[0016] The current enterprise calculation interest rate determination and feedback module is used to calculate the current ecological health score through a preset interest rate calculation method, obtain the current enterprise calculation interest rate, and provide real-time feedback of the current enterprise calculation interest rate to the user.

[0017] According to another aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the dynamic interest rate calculation method based on credit risk assessment as described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the dynamic interest rate calculation method based on credit risk assessment as described in any embodiment of the present invention.

[0019] The technical solution of this invention involves acquiring the target enterprise for which the interest rate is to be calculated, and the corresponding joint-dimensional enterprise description data. A pre-built multi-source data fusion engine processing module processes the joint-dimensional enterprise description data to obtain standard joint-dimensional enterprise description data. Based on this standard joint-dimensional enterprise description data, a pre-built dynamic ecological knowledge graph builder is used to construct the graph, generating a current enterprise description knowledge graph. The ecological health profiling engine processing module analyzes the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score. A preset interest rate calculation method is used to calculate the current ecological health score, resulting in the current enterprise's calculated interest rate, which is then fed back to the user in real time. This solves the problems of low efficiency and inaccuracy in interest rate determination caused by information silos and static analysis, improving the timeliness and accuracy of dynamic interest rate calculation. It enables dynamic interest rate calculation by combining multi-dimensional data, enhancing the flexibility, timeliness, and comprehensiveness of credit interest rate calculation.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a dynamic interest rate calculation method based on credit risk assessment provided in Embodiment 1 of the present invention;

[0023] Figure 2 This is a detailed flowchart of a dynamic interest rate calculation method based on credit risk assessment provided in Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of a dynamic interest rate calculation device based on credit risk assessment according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "target," "current," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] It is worth noting that the information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse; if the user chooses to refuse, the process will proceed to the expert decision-making process.

[0029] Example 1

[0030] Figure 1 The flowchart of a dynamic interest rate calculation method based on credit risk assessment is provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where financial institutions dynamically determine credit interest rates when conducting credit risk assessments on different enterprises. This method can be executed by a dynamic interest rate calculation device based on credit risk assessment, which can be implemented in hardware and / or software.

[0031] Correspondingly, such as Figure 1 As shown, the method includes:

[0032] S110. Obtain the target company for which the interest rate is to be calculated, and the joint dimension company description data corresponding to the target company, and use the pre-built multi-source data fusion engine processing module to process the joint dimension company description data to obtain standard joint dimension company description data.

[0033] The dynamic interest rate calculation system based on credit risk assessment includes a pre-loan assessment stage; the pre-loan assessment stage includes a multi-source data fusion engine processing module, a dynamic ecological knowledge graph builder, and an ecological health profile engine processing module.

[0034] Among them, the joint dimension enterprise description data can be obtained from multiple dimensions and is used to describe the enterprise and other enterprise data related to transactions with that enterprise.

[0035] Optionally, the step of obtaining the target company for which the interest rate to be calculated, and the joint dimension company description data corresponding to the target company, and processing the joint dimension company description data using a pre-built multi-source data fusion engine processing module to obtain standard joint dimension company description data includes: obtaining the target company for which the interest rate to be calculated; obtaining the joint dimension company description data corresponding to the target company through a preset federated learning framework and ecosystem partner server interaction system; wherein, the joint dimension company description data includes company description data of multiple dimensions, and the company description data of each dimension is collected from different platforms connected to the federated learning framework and ecosystem partner server interaction system; calculating the data missing rate of the joint dimension company description data using the multi-source data fusion engine processing module, and removing data fields that do not meet the missing rate requirements to obtain joint dimension company description missing rate removed data; and performing data standardization processing on the joint dimension company description missing rate removed data to obtain standard joint dimension company description data.

[0036] Specifically, the federated learning framework for enterprise description data connects to internal and external data sources via an interactive system with ecosystem partner servers. It can connect to platforms such as core banking transaction systems, enterprise resource planning systems, tax platforms, and IoT devices, enabling the collection of enterprise description data. Different platforms grant the federated learning framework and ecosystem partner servers different data collection permissions, allowing for data collection based on preset permissions. Data is transmitted encrypted throughout the process to ensure data security.

[0037] In this embodiment, after collecting the joint dimension enterprise description data, the first step is to clean the data. Specifically, a multi-source data fusion engine processing module can be used to calculate the missing rate of the joint dimension enterprise description data, determining the missing rate for each data point. The calculated missing rate can then be compared with a missing rate requirement. Data fields that do not meet the requirement can be removed, resulting in joint dimension enterprise description data with missing rate removal. Correspondingly, this data can be standardized, for example, by scaling numerical features to a range of 0 to 1, thus obtaining standard joint dimension enterprise description data.

[0038] The advantage of this setup is that it obtains joint-dimensional enterprise description data through the interaction system of the federated learning framework and ecosystem partner servers. Combined with data missing rate calculation and removal operations, as well as data standardization processing, corresponding standardized joint-dimensional enterprise description data can be obtained. This results in effective and standardized enterprise description data, enabling better data analysis and accurate dynamic calculation of interest rates.

[0039] S120. Based on the standard joint dimension enterprise description data, use a pre-built dynamic ecological knowledge graph builder to construct the graph and generate the current enterprise description knowledge graph.

[0040] Specifically, the step of constructing a graph using a pre-built dynamic ecological knowledge graph builder based on the standard joint dimension enterprise description data to generate the current enterprise description knowledge graph includes: using the dynamic ecological knowledge graph builder to extract target enterprises and at least one transaction-related enterprise associated with the target enterprises from the standard joint dimension enterprise description data, and identifying the target enterprises and each transaction-related enterprise as target nodes; wherein, in the standard joint dimension enterprise description data, node annotation attributes corresponding to each target node are obtained, and attribute annotation processing is performed on each target node; target transaction relationships between target enterprises and each transaction-related enterprise in the standard joint dimension enterprise description data are extracted, and directed edges are constructed based on each target transaction relationship; and a graph is constructed using each target node and each target directed edge to generate the current enterprise description knowledge graph.

[0041] The node labeling attributes may include industry type and registered capital, among other labeling attributes.

[0042] In this embodiment, a dynamic ecological knowledge graph builder can first be used to extract the target enterprise and one or more transaction-related enterprises from the standard joint dimension enterprise description data. Specifically, transaction-related enterprises can be enterprises that have transactions with the target enterprise. The target enterprise and each transaction-related enterprise can be identified as target nodes, and node annotation processing operations can be performed on each target node. For example, each target node can be labeled with attributes such as the corresponding industry type and registered capital.

[0043] Furthermore, after identifying the target nodes, it is necessary to extract the target transaction relationships between each target node to generate target directed edges between each node. Then, the nodes can be connected using these target directed edges to obtain the current enterprise description knowledge graph. Additionally, after determining the target directed edges, the corresponding edge weights can be obtained, which can be the percentage of annual transaction volume. These weights can be used to label the target directed edges. This current enterprise description knowledge graph can be scanned for data changes at a preset period for regular updates. This allows for real-time adjustment of node risk status; for example, if a node has a litigation marker, its risk status can be adjusted accordingly.

[0044] The advantage of this setup is that by using a dynamic ecological knowledge graph builder to parse the standard joint-dimensional enterprise description data, multiple target nodes, attribute annotations for each node, and directed edges for each target can be generated to further construct the knowledge graph. This allows for the construction of a more comprehensive and accurate knowledge graph of current enterprise descriptions, effectively transforming enterprise description data, solving the problem of data silos, and improving the flexibility, comprehensiveness, and accuracy of credit interest rate calculations.

[0045] S130. Using the ecological health profile engine processing module, the current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph are analyzed to obtain the current node risk probability and current ecological health score.

[0046] In this embodiment, the current node feature vector and current edge weight matrix can be obtained based on the current enterprise description knowledge graph. The model framework corresponding to the ecological health profile engine processing module can be a graph neural network main model. Specifically, the current node feature vector (with a dimension of 32) and the current edge weight matrix can be obtained through the input layer of this model. Furthermore, the hidden layers of this model can be configured with a corresponding number of layers and activation functions.

[0047] Correspondingly, the output layer of this model can output the current node risk probability and the current ecosystem health score. Specifically, the current node risk probability can be a value between 0 and 1; the current ecosystem health score can be a score between 0 and 100.

[0048] S140. Calculate the current ecological health score using a preset interest rate calculation method to obtain the current enterprise calculation interest rate, and provide real-time feedback of the current enterprise calculation interest rate to the user.

[0049] Specifically, the step of calculating the current ecological health score using a preset interest rate calculation method to obtain the current enterprise calculated interest rate includes: obtaining a preset risk premium weight parameter, and processing the current ecological health score using a preset risk premium coefficient calculation method to obtain the current risk premium coefficient; obtaining the current enterprise credit benchmark interest rate; and calculating the current enterprise credit benchmark interest rate and the current risk premium coefficient using a preset enterprise interest rate calculation method to obtain the current enterprise calculated interest rate.

[0050] For example, the risk premium weighting parameter can be obtained based on the overall situation of the target company, and the risk premium weighting parameter can be set as follows: The current ecological health score is: The risk premium coefficient is calculated as follows: The current risk premium coefficient can be calculated as follows: .

[0051] Furthermore, assuming the current benchmark interest rate for corporate lending is... It can be calculated according to the enterprise interest rate method. This is used to calculate the current corporate interest rate.

[0052] The advantage of this setup is that it allows for the calculation of the current corporate interest rate using both risk premium and corporate interest rate calculation methods. This results in a more accurate interest rate, enabling more personalized interest rate calculations based on the specific circumstances of different companies and improving the user experience.

[0053] Optionally, the pre-loan assessment stage further includes an intelligent monitoring and early warning center module; after the ecological health profile engine processing module parses the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score, the stage further includes: when the intelligent monitoring and early warning center module receives the current node risk probability, it obtains a pre-set early warning node risk probability threshold; it determines whether the current node risk probability is greater than the early warning node risk probability threshold, and if so, it sends a node risk anomaly alarm to the user; or through the intelligent monitoring and early warning center module, it obtains the target cash flow score value within a preset continuous time period corresponding to the target enterprise, as well as the preset minimum threshold for the required cash flow score value, and if the target cash flow score value is less than the minimum threshold for the required cash flow score value, it sends a cash flow score anomaly alarm to the user.

[0054] In this embodiment, the pre-loan assessment stage also includes an intelligent monitoring and early warning center module. This module can be used to issue alarms based on the probability of abnormal node risks and the scoring of abnormal cash flow. One or more early warning rules can be set in the intelligent monitoring and early warning center module, and alarms will be promptly issued to the user when the requirements of the rules are not met.

[0055] Specifically, when the risk probability of the current node is received, let's assume it is... Assume the risk probability threshold for the early warning node is... It is necessary to determine the size relationship between the two, that is, to determine... Is it greater than If so, it means the current node's risk probability exceeds the threshold requirement, indicating a significant abnormal risk. Therefore, a node risk anomaly alert is issued to the user. Conversely, if... Not greater than If the node does not pose any abnormal risk, then the node can be skipped.

[0056] Additionally, it's necessary to obtain the target cash flow score for the target company within a preset continuous time period. For example, obtain the target cash flow score for the target company over three consecutive days, assuming it's 0.5 (the standardized value). Next, it's necessary to obtain the minimum threshold for the required cash flow score, assuming it's 0.2. Since the target cash flow score of 0.5 is greater than the minimum threshold of 0.2, it indicates that the target company's cash flow score for that time period is not abnormal, therefore no abnormality alarm is needed. If the target cash flow score is 0.1, since 0.1 is less than 0.2, the target cash flow score is below the minimum threshold, therefore an abnormal cash flow score alarm should be issued to the user.

[0057] The advantage of this setup is that by using the intelligent monitoring and early warning center module to determine whether the current node's risk probability and target cash flow score are abnormal, and then taking alarm actions, it is possible to monitor the abnormal status of each node or enterprise in real time, respond promptly to market changes, safeguard the bank's financial security, and improve the user experience.

[0058] Optionally, the dynamic interest rate calculation system based on credit risk assessment further includes a post-loan monitoring stage; after generating the current enterprise description knowledge graph by constructing the graph using a pre-built dynamic ecological knowledge graph builder based on the standard joint dimension enterprise description data, it further includes: periodically acquiring and parsing new joint dimension enterprise description data, and using the parsing results to periodically update the current enterprise description knowledge graph; converting the updated current enterprise description knowledge graph into a currently updated enterprise description heatmap knowledge graph; acquiring and traversing each real-time scanning risk transmission path corresponding to the currently updated enterprise description heatmap knowledge graph, and counting the number of high-risk nodes corresponding to the determined high-risk transmission paths; when the number of high-risk nodes is greater than or equal to a preset high-risk node number threshold, it is determined as a target high-risk cluster based on the high-risk transmission path; and jointly feeding back the target high-risk cluster and the currently updated enterprise description heatmap knowledge graph to the user.

[0059] In this embodiment, new joint dimension enterprise description data can be acquired periodically, and the acquired data can be parsed to update the current enterprise description knowledge graph based on the parsing results.

[0060] Furthermore, the updated knowledge graph can be transformed into a heatmap to generate a currently updated enterprise description heatmap knowledge graph. This currently updated enterprise description heatmap knowledge graph includes at least one real-time scanning risk transmission path. This real-time scanning risk transmission path can be a directed transmission process path involving at least two target nodes.

[0061] After identifying each real-time scanning risk propagation path, it is necessary to count the paths containing at least one high-risk node and define them as high-risk propagation paths. That is, for a high-risk propagation path, there must be at least one high-risk node. Next, it is necessary to count the number of high-risk nodes in each high-risk propagation path. Assuming the high-risk node threshold is 3, high-risk propagation paths containing 3 or more nodes will be marked, and corresponding target high-risk clusters will be generated.

[0062] Correspondingly, the target high-risk cluster and the currently updated enterprise description heatmap knowledge graph can be jointly fed back to the user.

[0063] The advantage of this setup is that it allows for post-loan monitoring of the knowledge graph by updating the current enterprise description knowledge graph and counting the number of high-risk nodes. This avoids inaccurate interest rate calculations due to the presence of high-risk nodes, and allows for timely reminders to users to adjust interest rates, preventing financial losses. This makes dynamic interest rate calculations more real-time, flexible, and comprehensive.

[0064] The technical solution of this invention involves acquiring the target enterprise for which the interest rate is to be calculated, and the corresponding joint-dimensional enterprise description data. A pre-built multi-source data fusion engine processing module processes the joint-dimensional enterprise description data to obtain standard joint-dimensional enterprise description data. Based on this standard joint-dimensional enterprise description data, a pre-built dynamic ecological knowledge graph builder is used to construct the graph, generating a current enterprise description knowledge graph. The ecological health profiling engine processing module analyzes the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score. A preset interest rate calculation method is used to calculate the current ecological health score, resulting in the current enterprise's calculated interest rate, which is then fed back to the user in real time. This solves the problems of low efficiency and inaccuracy in interest rate determination caused by information silos and static analysis, improving the timeliness and accuracy of dynamic interest rate calculation. It enables dynamic interest rate calculation by combining multi-dimensional data, enhancing the flexibility, timeliness, and comprehensiveness of credit interest rate calculation.

[0065] Example 2

[0066] Figure 2 This is a detailed flowchart of a dynamic interest rate calculation method based on credit risk assessment provided in Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiments. In this embodiment, after calculating the current ecological health score using a preset interest rate calculation method to obtain the current enterprise calculated interest rate, and providing real-time feedback of the current enterprise calculated interest rate to the user, it further includes related calculation processing operations in the dynamic interest rate calculation stage during the loan process.

[0067] S210. Obtain the target company for which the interest rate is to be calculated, and the joint dimension company description data corresponding to the target company, and use the pre-built multi-source data fusion engine processing module to process the joint dimension company description data to obtain standard joint dimension company description data.

[0068] The dynamic interest rate calculation system based on credit risk assessment includes a pre-loan assessment stage; the pre-loan assessment stage includes a multi-source data fusion engine processing module, a dynamic ecological knowledge graph builder, and an ecological health profile engine processing module.

[0069] S220. Based on the standard joint dimension enterprise description data, use a pre-built dynamic ecological knowledge graph builder to construct the graph and generate the current enterprise description knowledge graph.

[0070] S230. Using the ecological health profile engine processing module, the current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph are analyzed to obtain the current node risk probability and current ecological health score.

[0071] S240. Calculate the current ecological health score using a preset interest rate calculation method to obtain the current enterprise calculation interest rate, and provide real-time feedback of the current enterprise calculation interest rate to the user.

[0072] The dynamic interest rate calculation system based on credit risk assessment also includes a dynamic interest rate calculation stage during the loan period.

[0073] S250. During the dynamic interest rate calculation stage of the loan, the automatic re-scoring operation is periodically triggered, and the operation of obtaining the joint dimension enterprise description data corresponding to the target enterprise is returned to perform, so as to obtain the target ecological health re-scoring.

[0074] This embodiment describes an operation for dynamically adjusting interest rates in a loan after a company has completed its credit process. Specifically, it can trigger automatic re-scoring periodically, for example, by setting the 1st of each month to trigger automatic re-scoring.

[0075] Therefore, upon receiving the automatic re-scoring operation information, it is necessary to obtain the joint dimension enterprise description data corresponding to the target enterprise in order to perform the corresponding target ecosystem health re-scoring calculation.

[0076] S260, Obtain the pre-set threshold for the difference between the old and new ratings.

[0077] S270. Calculate the absolute value of the difference between the current ecological health score and the target ecological health re-score, and determine whether the calculated absolute value is less than the allowable threshold for the difference between the old and new scores. If it is less than the threshold, maintain the current enterprise calculation rate.

[0078] S280. If it is not less than the target ecological health score, the enterprise re-scoring calculation rate is calculated and the enterprise re-scoring calculation rate is fed back to the user in real time.

[0079] For example, suppose we can obtain a threshold for the allowable difference between old and new scores, say a threshold of 5. Suppose we re-evaluate the target ecosystem's health. Furthermore, due to the current ecological health score being... .

[0080] Furthermore, the absolute value of the difference between the current ecological health score and the target ecological health re-score can be calculated to be 6. Since 6 is greater than 5, the enterprise re-score calculation interest rate needs to be calculated based on the target ecological health re-score of 89. Specifically, the risk premium weight parameter can be set as follows. The current ecological health score is: The risk premium coefficient is calculated as follows: The current risk premium coefficient can be calculated as follows: Furthermore, let's assume the current benchmark interest rate for corporate lending is... It can be calculated according to the enterprise interest rate method. To calculate the interest rate for enterprise re-scoring. This allows companies to recalculate interest rates and provide real-time feedback to users.

[0081] Conversely, if the target ecological health is re-evaluated The absolute value of the difference between the current ecological health score and the target ecological health re-score can be calculated to be 2. Since 2 is less than 5, the original current enterprise calculation rate can be maintained without recalculation.

[0082] The technical solution of this invention involves acquiring the target enterprise for which the interest rate is to be calculated, and the joint-dimensional enterprise description data corresponding to the target enterprise. A pre-built multi-source data fusion engine processing module processes the joint-dimensional enterprise description data to obtain standard joint-dimensional enterprise description data. Based on the standard joint-dimensional enterprise description data, a pre-built dynamic ecological knowledge graph builder is used to construct the graph, generating a current enterprise description knowledge graph. The ecological health profiling engine processing module analyzes the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score. Finally, a preset interest rate calculation method is used to calculate the current ecological health score. The system calculates the current enterprise interest rate and provides real-time feedback to the user. During the dynamic interest rate calculation phase of the loan, an automatic re-scoring operation is periodically triggered, and the operation of the joint dimension enterprise description data corresponding to the target enterprise is executed to obtain a target ecosystem health re-scoring. A pre-set allowable threshold for the difference between the old and new scores is obtained. The absolute value of the difference between the current ecosystem health score and the target ecosystem health re-scoring is calculated, and it is determined whether the calculated absolute value is less than the allowable threshold. If it is less, the current enterprise interest rate is maintained; if it is not less, the enterprise re-scoring interest rate is calculated based on the target ecosystem health re-scoring, and the enterprise re-scoring interest rate is provided to the user in real-time. This allows for dynamic adjustment of the interest rate during the loan process after the target enterprise has completed credit, thus ensuring asset security, timely risk response, improving the real-time nature and flexibility of interest rate adjustments, and enhancing the user experience.

[0083] Example 3

[0084] Figure 3 This is a schematic diagram of a dynamic interest rate calculation device based on credit risk assessment provided in Embodiment 3 of the present invention. The dynamic interest rate calculation device based on credit risk assessment provided in this embodiment can be implemented by software and / or hardware, and can be configured in a terminal device or server to implement a dynamic interest rate calculation method based on credit risk assessment in this embodiment of the present invention. Figure 3 As shown, the device includes: a standard joint dimension enterprise description data determination module 310, a current enterprise description knowledge graph generation module 320, a current node risk probability and current ecological health score determination module 330, and a current enterprise calculation interest rate determination and feedback module 340.

[0085] The standard joint dimension enterprise description data determination module 310 is used to obtain the target enterprise for the interest rate to be calculated, and the joint dimension enterprise description data corresponding to the target enterprise, and to process the joint dimension enterprise description data using a pre-built multi-source data fusion engine processing module to obtain the standard joint dimension enterprise description data.

[0086] The dynamic interest rate calculation system based on credit risk assessment includes a pre-loan assessment stage; the pre-loan assessment stage includes a multi-source data fusion engine processing module, a dynamic ecological knowledge graph builder, and an ecological health profile engine processing module.

[0087] The current enterprise description knowledge graph generation module 320 is used to construct the graph based on the standard joint dimension enterprise description data and a pre-built dynamic ecological knowledge graph builder to generate the current enterprise description knowledge graph.

[0088] The current node risk probability and current ecological health score determination module 330 is used to use the ecological health profile engine processing module to parse the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph, and obtain the current node risk probability and current ecological health score.

[0089] The current enterprise calculation interest rate determination and feedback module 340 is used to calculate the current ecological health score through a preset interest rate calculation method, obtain the current enterprise calculation interest rate, and provide real-time feedback of the current enterprise calculation interest rate to the user.

[0090] The technical solution of this invention involves acquiring the target enterprise for which the interest rate is to be calculated, and the corresponding joint-dimensional enterprise description data. A pre-built multi-source data fusion engine processing module processes the joint-dimensional enterprise description data to obtain standard joint-dimensional enterprise description data. Based on this standard joint-dimensional enterprise description data, a pre-built dynamic ecological knowledge graph builder is used to construct the graph, generating a current enterprise description knowledge graph. The ecological health profiling engine processing module analyzes the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score. A preset interest rate calculation method is used to calculate the current ecological health score, resulting in the current enterprise's calculated interest rate, which is then fed back to the user in real time. This solves the problems of low efficiency and inaccuracy in interest rate determination caused by information silos and static analysis, improving the timeliness and accuracy of dynamic interest rate calculation. It enables dynamic interest rate calculation by combining multi-dimensional data, enhancing the flexibility, timeliness, and comprehensiveness of credit interest rate calculation.

[0091] Based on the above embodiments, the pre-loan assessment stage also includes an intelligent monitoring and early warning center module.

[0092] Based on the above embodiments, an anomaly alarm processing module is also included, which can be specifically used to: after the ecological health profile engine processing module parses the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score, when the intelligent monitoring and early warning center module receives the current node risk probability, obtain a pre-set early warning node risk probability threshold; determine whether the current node risk probability is greater than the early warning node risk probability threshold, and if so, perform node risk anomaly alarm processing to the user; or through the intelligent monitoring and early warning center module, obtain the target cash flow score value within a preset continuous time period corresponding to the target enterprise and the preset minimum threshold of the required cash flow score value in real time, and if the target cash flow score value is less than the minimum threshold of the required cash flow score value, perform cash flow score anomaly alarm processing to the user.

[0093] Based on the above embodiments, the standard joint dimension enterprise description data determination module 310 can be specifically used for: obtaining the target enterprise for which the interest rate to be calculated; obtaining joint dimension enterprise description data corresponding to the target enterprise through a preset federated learning framework and ecosystem partner server interaction system; wherein, the joint dimension enterprise description data includes enterprise description data of multiple dimensions, and the enterprise description data of each dimension is collected from different platforms connected to the federated learning framework and ecosystem partner server interaction system; using the multi-source data fusion engine processing module to calculate the data missing rate of the joint dimension enterprise description data, and removing data fields that do not meet the missing rate requirements to obtain joint dimension enterprise description missing rate removed data; and performing data standardization processing on the joint dimension enterprise description missing rate removed data to obtain standard joint dimension enterprise description data.

[0094] Based on the above embodiments, the current enterprise description knowledge graph generation module 320 can be specifically used to: use the dynamic ecological knowledge graph builder to extract the target enterprise and at least one transaction-related enterprise associated with the target enterprise from the standard joint dimension enterprise description data, and determine the target enterprise and each of the transaction-related enterprises as target nodes; wherein, in the standard joint dimension enterprise description data, the node annotation attributes corresponding to each target node are obtained, and attribute annotation processing is performed on each target node; the target transaction relationship between the target enterprise and each transaction-related enterprise in the standard joint dimension enterprise description data is extracted, and each target directed edge is constructed according to each target transaction relationship; and the target nodes and each target directed edge are used to construct a graph to generate the current enterprise description knowledge graph.

[0095] Based on the above embodiments, the current enterprise calculation interest rate determination and feedback module 340 can be specifically used to: obtain a preset risk premium weight parameter, and use a preset risk premium coefficient calculation method to process the current ecological health score to obtain the current risk premium coefficient; obtain the current enterprise credit benchmark interest rate; and calculate the current enterprise credit benchmark interest rate and the current risk premium coefficient using a preset enterprise interest rate calculation method to obtain the current enterprise calculation interest rate.

[0096] Based on the above embodiments, the dynamic interest rate calculation system based on credit risk assessment also includes a dynamic interest rate calculation stage during the loan period.

[0097] Based on the above embodiments, an automatic re-scoring processing module is also included, which can be specifically used to: periodically trigger an automatic re-scoring operation during the loan dynamic interest rate calculation stage after calculating the current ecological health score using a preset interest rate calculation method to obtain the current enterprise calculated interest rate and providing real-time feedback of the current enterprise calculated interest rate to the user; return to execute the operation of the joint dimension enterprise description data corresponding to the target enterprise to obtain the target ecological health re-scoring; obtain a preset allowable threshold for the difference between the old and new scores; calculate the absolute value of the difference between the current ecological health score and the target ecological health re-scoring, and determine whether the calculated absolute value is less than the allowable threshold for the difference between the old and new scores. If it is less, the current enterprise calculated interest rate is maintained; if it is not less, the enterprise re-scoring calculated interest rate is calculated based on the target ecological health re-scoring, and the enterprise re-scoring calculated interest rate is provided to the user in real-time.

[0098] Based on the above embodiments, the dynamic interest rate calculation system based on credit risk assessment also includes a post-loan monitoring stage.

[0099] Based on the above embodiments, the system further includes an enterprise description knowledge graph processing module, which can be specifically used for: after constructing the graph using a pre-built dynamic ecological knowledge graph builder based on the standard joint dimension enterprise description data to generate the current enterprise description knowledge graph, periodically acquiring and parsing new joint dimension enterprise description data, and using the parsing results to periodically update the current enterprise description knowledge graph; converting the updated current enterprise description knowledge graph into a currently updated enterprise description heatmap knowledge graph; acquiring and traversing each real-time scanning risk transmission path corresponding to the currently updated enterprise description heatmap knowledge graph, and counting the number of high-risk nodes corresponding to the determined high-risk transmission paths; when the number of high-risk nodes is greater than or equal to a preset high-risk node number threshold, then determining it as a target high-risk cluster based on the high-risk transmission path; and jointly feeding back the target high-risk cluster and the currently updated enterprise description heatmap knowledge graph to the user.

[0100] The dynamic interest rate calculation device based on credit risk assessment provided in the embodiments of the present invention can execute the dynamic interest rate calculation method based on credit risk assessment provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0101] Example 4

[0102] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement Embodiment 4 of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0103] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0104] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0105] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a dynamic interest rate calculation method based on credit risk assessment.

[0106] In some embodiments, the dynamic interest rate calculation method based on credit risk assessment can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the dynamic interest rate calculation method based on credit risk assessment described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the dynamic interest rate calculation method based on credit risk assessment by any other suitable means (e.g., by means of firmware).

[0107] The method includes: acquiring the target enterprise for which the interest rate is to be calculated, and the joint dimension enterprise description data corresponding to the target enterprise; processing the joint dimension enterprise description data using a pre-built multi-source data fusion engine processing module to obtain standard joint dimension enterprise description data; wherein, the dynamic interest rate calculation system based on credit risk assessment includes a pre-loan assessment stage; the pre-loan assessment stage includes a multi-source data fusion engine processing module, a dynamic ecological knowledge graph builder, and an ecological health profile engine processing module; based on the standard joint dimension enterprise description data, constructing the graph using the pre-built dynamic ecological knowledge graph builder to generate a current enterprise description knowledge graph; using the ecological health profile engine processing module, parsing the current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score; calculating the current ecological health score using a preset interest rate calculation method to obtain the current enterprise calculated interest rate, and providing real-time feedback of the current enterprise calculated interest rate to the user.

[0108] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0110] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0111] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0112] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0113] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0114] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0115] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0116] Example 5

[0117] Embodiment 5 of the present invention also provides a computer-readable storage medium, wherein the computer-readable instructions, when executed by a computer processor, are used to execute a dynamic interest rate calculation method based on credit risk assessment. The method includes: acquiring a target enterprise for which the interest rate to be calculated, and joint-dimensional enterprise description data corresponding to the target enterprise; processing the joint-dimensional enterprise description data using a pre-built multi-source data fusion engine processing module to obtain standard joint-dimensional enterprise description data; wherein the dynamic interest rate calculation system based on credit risk assessment includes a pre-loan assessment stage; the pre-loan assessment stage includes a multi-source data fusion engine processing module, a dynamic ecological knowledge graph builder, and an ecological health profile engine processing module; constructing a graph using the pre-built dynamic ecological knowledge graph builder based on the standard joint-dimensional enterprise description data to generate a current enterprise description knowledge graph; parsing the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph using the ecological health profile engine processing module to obtain the current node risk probability and current ecological health score; calculating the current ecological health score using a preset interest rate calculation method to obtain the current enterprise calculated interest rate, and providing real-time feedback of the current enterprise calculated interest rate to the user.

[0118] Of course, the computer-executable instructions provided in the embodiments of the present invention, which include a computer-readable storage medium, are not limited to the method operations described above, but can also perform related operations in the dynamic interest rate calculation based on credit risk assessment provided in any embodiment of the present invention.

[0119] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0120] It is worth noting that in the above embodiments of dynamic interest rate calculation based on credit risk assessment, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0121] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A dynamic interest rate calculation method based on credit risk assessment, characterized in that, include: Obtain the target company for which the interest rate is to be calculated, and the joint dimension company description data corresponding to the target company, and use the pre-built multi-source data fusion engine processing module to process the joint dimension company description data to obtain standard joint dimension company description data; The dynamic interest rate calculation system based on credit risk assessment includes a pre-loan assessment stage; the pre-loan assessment stage includes a multi-source data fusion engine processing module, a dynamic ecological knowledge graph builder, and an ecological health profile engine processing module. Based on the standard joint dimension enterprise description data, a pre-built dynamic ecological knowledge graph builder is used to construct the graph and generate the current enterprise description knowledge graph. The ecological health profile engine processing module is used to analyze the current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score. The current ecological health score is calculated using a preset interest rate calculation method to obtain the current enterprise calculation interest rate, and this current enterprise calculation interest rate is fed back to the user in real time.

2. The method according to claim 1, characterized in that, The pre-loan assessment stage also includes an intelligent monitoring and early warning center module; After the ecological health profiling engine processing module parses the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph to obtain the current node risk probability and current ecological health score, the process further includes: When the intelligent monitoring and early warning center module receives the risk probability of the current node, it obtains the pre-set risk probability threshold of the early warning node; Determine whether the current node risk probability is greater than the warning node risk probability threshold. If so, issue a node risk anomaly alarm to the user. Alternatively, the intelligent monitoring and early warning center module can obtain the target cash flow score value for the target enterprise within a preset continuous time period and the preset minimum threshold for the required cash flow score value in real time. If the target cash flow score value is less than the minimum threshold for the required cash flow score value, an alarm will be triggered to the user to handle the abnormal cash flow score.

3. The method according to claim 1, characterized in that, The process involves acquiring the target company for which the interest rate to be calculated, and the joint dimension company description data corresponding to the target company. A pre-built multi-source data fusion engine processing module is then used to process the joint dimension company description data to obtain standard joint dimension company description data, including: Obtain the target company for the interest rate to be calculated; By interacting with the ecosystem partner server system through a pre-set federated learning framework, joint dimension enterprise description data corresponding to the target enterprise is obtained; The joint dimension enterprise description data includes enterprise description data of multiple dimensions, and the enterprise description data of each dimension is collected from different platforms connected to the federated learning framework and the ecosystem partner server interaction system. The multi-source data fusion engine processing module is used to calculate the missing rate of the joint dimension enterprise description data, and the data fields that do not meet the missing rate requirements are removed to obtain the joint dimension enterprise description missing rate removed data. The data with missing enterprise descriptions in the joint dimension are standardized to obtain standard joint dimension enterprise description data.

4. The method according to claim 3, characterized in that, The process of constructing a knowledge graph based on the standard joint dimension enterprise description data, using a pre-built dynamic ecological knowledge graph builder, to generate the current enterprise description knowledge graph includes: Using the dynamic ecological knowledge graph builder, the target enterprise and at least one transaction-related enterprise associated with the target enterprise are extracted from the standard joint dimension enterprise description data, and the target enterprise and each of the transaction-related enterprises are identified as target nodes; Specifically, in the standard joint dimension enterprise description data, the node annotation attributes corresponding to each target node are obtained, and the attribute annotation processing is performed on each target node; Extract the target transaction relationships between the target enterprises and each transaction-related enterprise from the standard joint dimension enterprise description data, and construct each target directed edge based on each target transaction relationship; By constructing a graph from each target node and each target directed edge, the current enterprise description knowledge graph is generated.

5. The method according to claim 4, characterized in that, The calculation of the current ecological health score using a preset interest rate calculation method to obtain the current enterprise's calculated interest rate includes: Obtain the preset risk premium weight parameters, and use the preset risk premium coefficient calculation method to process the current ecological health score to obtain the current risk premium coefficient; Obtain the current benchmark interest rate for corporate lending; The current corporate interest rate is calculated by using a preset corporate interest rate calculation method, which calculates the current corporate credit benchmark interest rate and the current risk premium coefficient.

6. The method according to claim 2 or 5, characterized in that, The dynamic interest rate calculation system based on credit risk assessment also includes a dynamic interest rate calculation stage during the loan period; After calculating the current ecological health score using a preset interest rate calculation method to obtain the current enterprise calculated interest rate, and providing real-time feedback of the current enterprise calculated interest rate to the user, the process further includes: During the dynamic interest rate calculation phase of the loan, an automatic re-scoring operation is periodically triggered, and the operation of the joint dimension enterprise description data corresponding to the target enterprise is returned to obtain the target ecological health re-scoring. Obtain the pre-set threshold for the difference between old and new ratings; Calculate the absolute value of the difference between the current ecological health score and the target ecological health re-score, and determine whether the calculated absolute value is less than the allowable threshold for the difference between the old and new scores. If it is less, maintain the current enterprise calculation rate. If it is not less than, then the enterprise re-scoring calculation rate is calculated based on the target ecological health, and the enterprise re-scoring calculation rate is fed back to the user in real time.

7. The method according to claim 2 or 5, characterized in that, The dynamic interest rate calculation system based on credit risk assessment also includes a post-loan monitoring phase; After generating the current enterprise description knowledge graph by constructing the graph using a pre-built dynamic ecosystem knowledge graph builder based on the standard joint dimension enterprise description data, the process further includes: New joint dimension enterprise description data are periodically acquired and parsed, and the parsing results are used to periodically update the current enterprise description knowledge graph. The updated current enterprise description knowledge graph is converted into a current updated enterprise description heatmap knowledge graph. Get and traverse each real-time scanning risk transmission path corresponding to the currently updated enterprise description heatmap knowledge graph, and count the number of high-risk nodes corresponding to the identified high-risk transmission paths; When the number of high-risk nodes is greater than or equal to a preset threshold for the number of high-risk nodes, it is determined as a target high-risk cluster based on the high-risk transmission path; The target high-risk cluster and the currently updated enterprise description heatmap knowledge graph are jointly fed back to the user.

8. A dynamic interest rate calculation device based on credit risk assessment, characterized in that, include: The standard joint dimension enterprise description data determination module is used to obtain the target enterprise for which the interest rate is to be calculated, and the joint dimension enterprise description data corresponding to the target enterprise, and to process the joint dimension enterprise description data using a pre-built multi-source data fusion engine processing module to obtain the standard joint dimension enterprise description data. The dynamic interest rate calculation system based on credit risk assessment includes a pre-loan assessment stage; the pre-loan assessment stage includes a multi-source data fusion engine processing module, a dynamic ecological knowledge graph builder, and an ecological health profile engine processing module. The current enterprise description knowledge graph generation module is used to construct the graph based on the standard joint dimension enterprise description data and a pre-built dynamic ecological knowledge graph builder to generate the current enterprise description knowledge graph. The current node risk probability and current ecological health score determination module is used to use the ecological health profile engine processing module to parse the input current node feature vector and current edge weight matrix corresponding to the current enterprise description knowledge graph, and obtain the current node risk probability and current ecological health score. The current enterprise calculation interest rate determination and feedback module is used to calculate the current ecological health score through a preset interest rate calculation method, obtain the current enterprise calculation interest rate, and provide real-time feedback of the current enterprise calculation interest rate to the user.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a dynamic interest rate calculation method based on credit risk assessment as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute a dynamic interest rate calculation method based on credit risk assessment as described in any one of claims 1-7.

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