Enterprise and financial product matching method and system and storage medium
By using a method that matches businesses with financial products, and calculating the overall matching degree based on tag sets and priority rules, this approach solves the problem of the single matching method in existing technologies. It achieves efficient and accurate product recommendations, improving the accuracy of recommendations and the financial experience for businesses.
Patent Information
- Application Number
- CN202511859945.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-10
- Publication Date
- 2026-02-10
AI Technical Summary
Existing technologies use a single method to match businesses with financial products, resulting in low accuracy and effectiveness of recommendations. They cannot dynamically reflect changes in business status, and the matching degree calculation does not consider tag weights or priorities, making it impossible to distinguish between highly matched and poorly matched businesses.
By employing a matching method between enterprises and financial products, information on enterprises and products is collected to form a tag set, the comprehensive matching degree is calculated, and dynamic grading is performed based on multi-dimensional tag features and priority rules to achieve efficient, accurate, and real-time product matching and recommendation.
It improves the accuracy and efficiency of financial product recommendations, provides enterprises with personalized financial service solutions, and enhances their financial experience and satisfaction.
Smart Images
Figure CN121504575A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of financial product recommendation technology, and in particular to methods, systems and storage media for matching enterprises with financial products. Background Technology
[0002] In the field of financial product recommendation technology, how to efficiently and accurately recommend suitable financial products to enterprises with needs has always been a key focus of the industry. Traditional recommendation methods are often based on simple rules or fixed matching patterns, which are difficult to cope with the complex and ever-changing needs of enterprises and the characteristics of financial products. For example, in government and enterprise investment and financing platforms, financial institutions often recommend financial products based on basic enterprise information or manual screening. The matching between enterprises and products is only based on static tags or fixed rules, which cannot dynamically reflect changes in the enterprise's status.
[0003] With the continuous development of big data and artificial intelligence technologies, how to utilize these advanced technologies to improve the accuracy and efficiency of matching between enterprises and financial products has become an urgent problem to be solved. Summary of the Invention
[0004] The main purpose of this application is to provide a method, system, and storage medium for matching enterprises with financial products, aiming to solve the technical problem that the single matching method for enterprises and financial products in the prior art leads to low accuracy and effectiveness of recommendations.
[0005] To achieve the above objectives, this application proposes a method for matching enterprises with financial products. The method is applied to the matching module of an enterprise-financial product matching system, which includes: an enterprise information module, a product information module, a matching module, and a recommendation output module. The method for matching enterprises with financial products includes: Collect information stored in the enterprise information module and the product information module to obtain the current enterprise tag set and the current product tag set; Based on the information, determine whether the current enterprise meets the preset priority rules; When the current enterprise does not meet the preset priority rules, the comprehensive matching degree between the current enterprise and the current product is calculated based on the current enterprise tag set and the current product tag set. The matching level between the current enterprise and the current product is determined based on the comprehensive matching degree. Matching information is determined based on the overall matching degree and the matching level, and the matching information is sent to the recommendation output module for display.
[0006] In one embodiment, the step of calculating the comprehensive matching degree between the current enterprise and the current product based on the current enterprise tag set and the current product tag set when the current enterprise does not meet the preset priority rule includes: When the current enterprise does not meet the preset priority rules, traverse the current enterprise tag set and the current product tag set; When the same semantic tag exists in the current enterprise tag set and the current product tag set, the similarity of the same semantic tag is set to a first preset value; When there is a semantic relationship between the tags in the current enterprise tag set and the current product tag set, the similarity of the semantically related tags is set to a second preset value; When there is no semantic relationship between the tags in the current enterprise tag set and the current product tag set, the similarity of the tags that do not have semantic relationship is set to the third preset value; The overall matching degree between the current enterprise and the current product is calculated based on the first preset value, the second preset value, and the third preset value.
[0007] In one embodiment, the step of calculating the overall matching degree between the current enterprise and the current product based on the first preset value, the second preset value, and the third preset value includes: Obtain the first weight of the same semantic tag, the second weight of the semantically related tag, and the third weight of the tag that has no semantically related tag or the same semantic tag; Calculate the first matching degree based on the first preset value and the first weight; The second matching degree is calculated based on the second preset value and the second weight; The third matching degree is calculated based on the third preset value and the third weight; The first matching degree, the second matching degree, and the third matching degree are weighted to obtain the comprehensive matching degree between the current enterprise and the current product.
[0008] In one embodiment, the step of determining whether the current enterprise meets the preset priority rule based on the information includes: The current product whitelist and target status are obtained according to the preset priority rules; Based on the information, we can determine the current enterprise category and the current business status. When the category to which the current enterprise belongs is within the whitelist of the current product or the current business status of the enterprise is the target status, it is determined that the current enterprise meets the preset priority rules; If the current enterprise's category is not in the current product whitelist and the enterprise's current business status is not the target status, then the current enterprise is determined not to meet the preset priority rule.
[0009] In one embodiment, after the step of determining whether the current enterprise meets the preset priority rule based on the information, the method further includes: When the current enterprise meets the preset priority rules and the category to which the enterprise belongs is within the current product whitelist, the product to be recommended is determined to be the current product; When the current enterprise meets the preset priority rules, the enterprise's category is not in the current product whitelist, and the enterprise's current business status is the target status, the product to be recommended is determined to be the first financial product; The current product or the first financial product is sent to the recommendation output module so that the recommendation output module recommends the current product or the first financial product to the current enterprise.
[0010] In one embodiment, the step of determining matching information based on the overall matching degree and the matching level, and sending the matching information to the recommendation output module for display includes: When the matching level is the first matching level, a recommendation reason is generated; Get the current company name and the current product name; Matching information is generated using the current company name, the current product name, the overall matching degree, the matching level, and the recommendation reason. The matching information is sent to the recommendation output module for display.
[0011] In one embodiment, before the step of determining matching information based on the overall matching degree and the matching level, and sending the matching information to the recommendation output module for display, the method further includes: Check whether the current company is on a preset priority list; When the current enterprise is within the preset priority list, the matching level is raised to the preset matching level; Check if the target tag exists in the current enterprise tag set for the current enterprise tag; When the target tag exists in the current enterprise tag, obtain the target weight factor; The overall matching degree is adjusted according to the target weight factor to obtain an updated overall matching degree.
[0012] In one embodiment, the step of determining the matching level between the current enterprise and the current product based on the overall matching degree includes: The overall matching degree is compared with a first preset matching degree threshold and a second preset matching degree threshold; When the overall matching degree is greater than the first preset matching degree threshold, the matching level between the current enterprise and the current product is determined to be the first matching level; When the overall matching degree is less than the first preset matching degree threshold and greater than the second preset matching degree threshold, the matching level between the current enterprise and the current product is determined to be the second matching level; When the overall matching degree is less than the second preset matching degree threshold, the matching level between the current enterprise and the current product is determined to be the third matching level, wherein the first matching level is greater than the second matching level, and the second matching level is greater than the third matching level.
[0013] Furthermore, to achieve the above objectives, this application also proposes a matching system for enterprises and financial products, which includes: an enterprise information module, a product information module, a matching module, and a recommendation output module connected in sequence. The enterprise information module is also connected to the matching module, and the matching module performs the steps of the enterprise and financial product matching method described above.
[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the enterprise and financial product matching method described above.
[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the enterprise and financial product matching method described above.
[0016] This application proposes one or more technical solutions, wherein the enterprise-financial product matching method is applied to the matching module of an enterprise-financial product matching system. The enterprise-financial product matching system includes: an enterprise information module, a product information module, a matching module, and a recommendation output module. The enterprise-financial product matching method includes: collecting information stored in the enterprise information module and the product information module to obtain a current enterprise tag set and a current product tag set; determining whether the current enterprise meets a preset priority rule based on the information; when the current enterprise does not meet the preset priority rule, calculating the comprehensive matching degree between the current enterprise and the current product based on the current enterprise tag set and the current product tag set; determining the matching level between the current enterprise and the current product based on the comprehensive matching degree; determining matching information based on the comprehensive matching degree and the matching level, and sending the matching information to the recommendation output module for display. This achieves accurate judgment and recommendation of the matching relationship between enterprises and financial products. By collecting relevant information about enterprises and products to form a tag set, and then using these tag sets to calculate the comprehensive matching degree, the matching level is finally determined and the matching information is displayed. This not only improves the accuracy and efficiency of financial product recommendations but also provides enterprises with more personalized financial service solutions, helping to improve their financial experience and satisfaction. Attached Figure Description
[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 A flowchart illustrating the method for matching enterprises with financial products in this application (Example 1). Figure 2 This is a schematic diagram of the matching system between the applicant company and financial products. Figure 3 This is a flowchart illustrating the second embodiment of the method for matching enterprises with financial products in this application. Figure 4 This is a flowchart illustrating the third embodiment of the method for matching enterprises with financial products in this application. Figure 5 This is a simplified flowchart illustrating one embodiment of the method for matching enterprises with financial products in this application.
[0020] Explanation of icon numbers: Enterprise Information Module 100, Product Information Module 200, Matching Module 300, Recommendation Output Module 400.
[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.
[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.
[0024] The main solution of this application embodiment is as follows: the enterprise-financial product matching method is applied to the matching module of the enterprise-financial product matching system. The enterprise-financial product matching system includes: an enterprise information module, a product information module, a matching module, and a recommendation output module. The enterprise-financial product matching method includes: collecting information stored in the enterprise information module and the product information module to obtain a current enterprise tag set and a current product tag set; determining whether the current enterprise meets a preset priority rule based on the information; when the current enterprise does not meet the preset priority rule, calculating the comprehensive matching degree between the current enterprise and the current product based on the current enterprise tag set and the current product tag set; determining the matching level between the current enterprise and the current product based on the comprehensive matching degree; determining matching information based on the comprehensive matching degree and the matching level, and sending the matching information to the recommendation output module for display.
[0025] Because existing technology companies and products are matched only through static tags or fixed rules, they cannot dynamically reflect changes in the company's status. The matching degree calculation does not take into account tag weights or priorities, and cannot distinguish between companies with high matching degree and those with low matching degree. Furthermore, matching is often performed only at fixed points in time and cannot be adjusted in real time as company information is updated.
[0026] This application provides a solution, a matching system for enterprises and financial products, which achieves efficient, accurate, and real-time product matching and recommendation based on multi-dimensional tag features, priority rules, and dynamic grading.
[0027] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, or a matching module in a matching system for enterprises and financial products. The following description uses a matching module in a matching system for enterprises and financial products as an example to illustrate this embodiment and the subsequent embodiments.
[0028] Based on this, the embodiments of this application provide a method for matching enterprises with financial products, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for matching enterprises with financial products in this application.
[0029] In this embodiment, the method for matching enterprises with financial products includes steps S10 to S50: Step S10: Collect the information stored in the enterprise information module and the product information module to obtain the current enterprise tag set and the current product tag set.
[0030] The enterprise-financial product matching method is applied to the matching module of an enterprise-financial product matching system. This system includes: an enterprise information module 100, a product information module 200, a matching module 300, and a recommendation output module 400. For example... Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a matching system for enterprises and financial products. The enterprise information module 100 collects and stores the enterprise's business registration information, business characteristics, credit data, and tag information. The product information module 200 collects and stores the basic attributes, categories, affiliated institutions, and tag information of financial products. The matching module 300 includes a priority rule submodule, a tag matching submodule, a dynamic matching degree submodule, and a triggering mechanism submodule. The matching module 300 performs matching calculations between enterprises and financial products based on the information stored in the enterprise information module 100 and the product information module 200, obtains the calculation results, and sends the results to the recommendation output module 400 for output. The recommendation output module 400 displays the output information, such as outputting the matching information from the matching module 300 to the government and enterprise platform's front-end recommendation list, the "Recommended Financial Products" panel of the enterprise profile module, and the back-end database. The back-end database is used for continuous training of the analysis model, enabling matching degree analysis or tag analysis through the analysis model.
[0031] It is understood that the matching of enterprises and products in this embodiment can be performed on a timed basis, or it can be triggered by specific events, such as enterprises adding or modifying tag information, financial institutions adding or updating products, enterprises logging into the government and enterprise service platform, and management modifying priority rules. Through the event triggering mechanism, dynamic and intelligent recommendations are updated in real time.
[0032] In practical implementation, the enterprise information module stores enterprise information and enterprise tags. Enterprise information may include the enterprise name, operational information, etc., and the enterprise tag set is represented as E={e1,e2...en}. The product information module stores product information and product tags. Product information may include the product name, detailed product information, etc. For example, the product tag set for product B is P={p1,p2...pm}. Tag sources include, but are not limited to, business registration data, industry classifications, policy-related tag libraries, and financial product feature tag libraries.
[0033] Step S20: Determine whether the current enterprise meets the preset priority rules based on the information.
[0034] Pre-defined priority rules can be set in advance. For example, these rules could be set based on whether a company is on a product's whitelist, whether its industry is a key supported industry, or whether its credit rating meets a preset standard. The priority rules sub-module within the matching module analyzes and judges the company information provided by the company information module based on these preset conditions. Specifically, the priority rules sub-module first obtains key information such as the company's industry and credit rating data from the company information module, and then compares this information with the preset priority rules. For example, if a company's industry is a key supported industry, or its credit rating meets any one or more of the preset standards, then the company is determined to meet the preset priority rules. Conversely, if a company's industry is not a key supported industry and its credit rating does not meet the preset standards, then the company is determined not to meet the preset priority rules. This method allows for the rapid filtering of companies that meet specific conditions, providing a foundation for subsequent accurate matching and recommendations.
[0035] In one feasible implementation, step S20 may include steps A11 to A14: Step A11: Obtain the current product whitelist and target status according to the preset priority rules; It should be noted that the whitelist of the current product can be determined based on the current product and preset priority rules. For example, if the current product is a risk subsidy product, the whitelist of risk subsidy funds can be obtained, and the target status is "to be improved" or "to be submitted".
[0036] Step A12: Obtain the current enterprise category and current business status based on the information provided; In practice, since the enterprise information module stores enterprise information, it can determine the current enterprise category and its current business status based on that information. For example, Enterprise A belongs to the state-owned enterprise category, and its current status is that it has won bids for projects within the past year.
[0037] Step A13: When the category to which the current enterprise belongs is within the whitelist of the current product or the current business status of the enterprise is the target status, determine that the current enterprise meets the preset priority rule; In specific implementation, for example, if the current product whitelist is the risk subsidy fund whitelist, then the current enterprise's category of state-owned enterprises is within the risk subsidy fund whitelist, indicating that the current enterprise meets this preset priority rule. Or, if the current enterprise status is that the enterprise has won a bid and the bid time is less than 1 year, it indicates that the current business status of the enterprise is in the state of being to be improved or to be applied for, which also indicates that the current enterprise meets this preset priority rule.
[0038] In one feasible implementation, if an enterprise meets a preset priority rule, the corresponding product can be directly recommended to the current enterprise. Therefore, the method further includes: when the current enterprise meets the preset priority rule and the enterprise's category is within the current product whitelist, determining the product to be recommended as the current product; when the current enterprise meets the preset priority rule, the enterprise's category is not within the current product whitelist, and the enterprise's current business status is the target status, determining the product to be recommended as the first financial product; and sending the current product or the first financial product to the recommendation output module so that the recommendation output module recommends the current product or the first financial product to the current enterprise.
[0039] If the current enterprise meets the preset priority rules, it is necessary to determine which level of rules the current enterprise meets, as shown in Table 1, which is the preset priority rule table.
[0040] Table 1
[0041] For example, if a company meets the preset priority rules and is in the first-level priority category (i.e., it is on the risk subsidy fund whitelist), then the product to be recommended is the current product, i.e., the risk subsidy product. The risk subsidy product is then directly sent to the recommendation output module, which recommends it to the company. A recommendation reason can also be generated, such as "The company is on the risk subsidy fund whitelist and meets the recommendation requirements." If the company meets the preset priority rules but not the first-level priority category, and is in the second-level priority category (i.e., the company has projects that are "to be improved" or "to be submitted" and the winning bid time is less than one year), then the product to be recommended is a transaction loan type product, i.e., the first financial product. Therefore, the first financial product is directly sent to the recommendation output module, which recommends it to the company. A recommendation reason can also be generated, such as "The company has projects that are to be improved or submitted and the winning bid time is less than one year, meeting the recommendation conditions for transaction loan products."
[0042] Step A14: When the category to which the current enterprise belongs is not in the current product whitelist and the current business status of the enterprise is not the target status, it is determined that the current enterprise does not meet the preset priority rule.
[0043] If the current enterprise does not meet the preset priority rules, it will directly enter the third priority level, that is, perform normal matching. Specifically, if the current enterprise is neither in the current product whitelist nor has a winning project, it is determined that the current enterprise does not meet the preset priority rules, and it is necessary to further calculate the comprehensive matching degree between the current enterprise and the current product based on the current enterprise tag set and the current product tag set.
[0044] Step S30: When the current enterprise does not meet the preset priority rules, calculate the comprehensive matching degree between the current enterprise and the current product based on the current enterprise tag set and the current product tag set.
[0045] It should be noted that the similarity between tags can be calculated based on the current enterprise tag set and the current product tag set to obtain the overall matching score. When calculating the overall matching score, the weight of the tags is considered; different tags have different degrees of influence on the matching score. For example, a company's credit tag may have a higher weight than its size tag in matching financial products. Furthermore, dynamic factors, such as the company's recent business growth and changes in the market environment, are also taken into account to dynamically adjust the tags before calculating the matching score.
[0046] The overall matching degree between the current enterprise and the current product is the degree of overlap of the tags of the current enterprise and the current product. This determines whether the current product and the current enterprise are a match. If they are not a match, the current product will not be recommended to the current enterprise. At the same time, the overall matching degree between the current enterprise and other products can also be calculated to find the most suitable financial product for the current enterprise and make a recommendation.
[0047] Step S40: Determine the matching level between the current enterprise and the current product based on the comprehensive matching degree.
[0048] In practice, the matching score between the current enterprise and the current product can be obtained based on the comprehensive matching degree. The higher the matching score, the higher the matching level. Therefore, a threshold can be set to dynamically classify the matching level between the current enterprise and the current product based on the relationship between the comprehensive matching degree and the threshold, thus obtaining the matching level between the current enterprise and the current product.
[0049] In one feasible implementation, step S40 may include steps B11-B14: Step B11: Compare the overall matching degree with the first preset matching degree threshold and the second preset matching degree threshold; The first preset matching threshold and the second preset matching threshold can be set according to requirements. For example, the first preset matching threshold can be set to 0.7 and the second preset matching threshold can be set to 0.4.
[0050] The overall matching degree can be compared with the first preset matching degree threshold and the second preset matching degree threshold to determine the matching level between the current enterprise and different products.
[0051] Step B12: When the overall matching degree is greater than the first preset matching degree threshold, determine the matching level between the current enterprise and the current product as the first matching level; When the overall matching degree is greater than or equal to the first preset matching degree threshold of 0.7, it indicates that the matching degree between the current enterprise and the current product is very high, and its matching level can be set to high matching, i.e., the first matching level.
[0052] Step B13: When the overall matching degree is less than the first preset matching degree threshold and greater than the second preset matching degree threshold, determine the matching level between the current enterprise and the current product as the second matching level; When the overall matching degree is less than the first preset matching degree threshold of 0.7 and greater than or equal to the second preset matching degree threshold of 0.4, it indicates that the current enterprise and the current product have a certain degree of matching, but are not highly matched. At this time, the matching level can be set to medium matching, that is, the second matching level.
[0053] Step B14: When the overall matching degree is less than the second preset matching degree threshold, determine the matching level between the current enterprise and the current product as the third matching level, wherein the first matching level is greater than the second matching level, and the second matching level is greater than the third matching level.
[0054] When the overall matching degree is less than the second preset matching degree threshold of 0.4, it means that the matching degree between the current enterprise and the current product is low, and the matching level is set to low matching. By classifying the matching level according to different thresholds, this method can more accurately reflect the matching degree between enterprises and financial products, providing a more detailed basis for subsequent recommendations.
[0055] Specifically, the system can also automatically categorize products based on their overall matching degree. For example, the top 30% is considered "high matching," 30%-70% is "medium matching," and 70%-100% is "low matching." If the overall matching degree between the company and products A, B, C, D, E, and F is 0.8, 0.6, 0.7, 0.68, 0.5, and 0.3 respectively, then according to the automatic grading method, product A's overall matching degree of 0.8 falls within the top 30%, so its matching degree is "high matching." Product C's overall matching degree of 0.7 also falls within the top 30%, so its matching degree is also "high matching." Product D's overall matching degree of 0.68 falls within the 30%-70% range, so its matching degree is "medium matching." Product B's overall matching degree of 0.68 falls within the 30%-70% range, so its matching degree is "medium matching." Within 70%, the matching level is "medium match"; Product E's overall matching degree of 0.5 is within the range of 30% - 70%, and the matching level is "medium match"; Product F's overall matching degree of 0.3 is within the range of 70% - 100%, and the matching level is "low match".
[0056] It should be noted that, to improve the accuracy of matching, after determining the matching level, further confirmation can be made based on the actual situation of the enterprise. For example, it can be further checked whether the enterprise is in the preset priority list. The preset priority list is the preset priority rule mentioned above. Therefore, after step S40, steps S41 to S45 are also included: Step S41: Detect whether the current enterprise is in the preset priority list; It should be noted that you can double-check whether the current company is on the preset priority list to prevent situations where company information has changed. For example, a company may have added tags that meet the preset priority criteria after the previous matching process, or a company that was not originally on the list may have been added to the priority list due to policy adjustments. By double-checking, you can ensure the timeliness and accuracy of the matching results.
[0057] Step S42: When the current enterprise is in the preset priority list, the matching level is raised to the preset matching level; If a company is detected as being on a pre-defined priority list, it means that the company meets specific priority criteria. In this case, the matching level between the company and the product needs to be adjusted. For example, if the original overall matching score was 0.65, which is at the "medium matching" level, but the company is included in the pre-defined priority list because it belongs to a key supported industry, its matching level will be raised to "high matching" to reflect the impact of policy support or specific priority criteria on the company's recommendation results. The pre-defined matching level is higher than the current matching level. For example, if the current matching level is low, the pre-defined matching level is medium; if the current matching level is medium, the pre-defined matching level is high.
[0058] If the detection result indicates that the current enterprise is not in the preset priority list, it means that the enterprise does not meet the additional priority conditions. In this case, the matching level determined in step S40 will be maintained, and no further adjustments will be made. For example, if the enterprise's overall matching degree is 0.5, which is at the "medium matching" level, and it is not included in any priority list, then the final matching level will still be "medium matching".
[0059] Step S43: Check if the target tag exists in the current enterprise tag set; The target label is a special label that can be set in advance, such as a high credit rating or a green enterprise label. It can detect whether the target label exists in the current enterprise label set, for example, to determine whether the current enterprise is a green enterprise.
[0060] Step S44: When the target tag exists in the current enterprise tag, obtain the target weight factor; If a target label is detected within the current enterprise's tags, such as the enterprise being identified as a green enterprise, then the corresponding target weight factor needs to be obtained. The target weight factor is a pre-set parameter used to adjust the matching degree; it reflects the importance of a specific label in matching financial products. For example, the green enterprise label might correspond to a higher target weight factor to reflect the impact of environmental policies on financial product recommendations.
[0061] After obtaining the target weighting factor, it can be applied to calculate the overall matching degree between the current enterprise and the current product. Through weighted adjustment, the matching degree can better reflect the enterprise's specific attributes and policy orientation. For example, if the original overall matching degree is 0.6 and the target weighting factor is 0.1, the adjusted overall matching degree may become 0.66 (the specific calculation method needs to be determined according to the actual weighting rules), which may improve the enterprise's matching level.
[0062] Step S45: Adjust the overall matching degree according to the target weight factor to obtain an updated overall matching degree.
[0063] After obtaining the target weighting factor, the overall matching degree between the current enterprise and the current product needs to be adjusted based on this factor. The adjustment method can be a simple weighted sum or a more complex functional relationship, depending on the actual needs and system design. For example, if the target weighting factor is 1.1, the overall matching degree can be multiplied by 1.1 to obtain the updated overall matching degree.
[0064] The adjusted overall matching score will more accurately reflect the matching relationship between enterprises and financial products, especially after considering the enterprise's special labels and policy factors. Subsequently, the matching level is re-determined based on the adjusted overall matching score, using the same method as step S40, i.e., by comparing with a preset matching score threshold or ranking proportionally. For example, if the adjusted overall matching score increases from 0.6 to 0.66, an enterprise that was originally at the "medium matching" level may be upgraded to the "high matching" level, thus receiving higher-quality financial product recommendations. Through this step, the system can respond more flexibly to policy changes and changes in enterprise attributes, improving the accuracy and practicality of recommendations.
[0065] In practice, after updating the matching level or overall matching degree, step S50 can be executed, which determines the matching information based on the matching level and overall matching degree, and then outputs the matching information to the recommendation output module for display, thereby recommending financial products to the current enterprise.
[0066] Step S50: Determine matching information based on the overall matching degree and the matching level, and send the matching information to the recommendation output module for display.
[0067] In practice, the matching information may include the specific numerical value of the overall matching degree between the current enterprise and the target financial product, the matching level description (such as high matching, medium matching, low matching), and the recommendation reasons, such as the high degree of overlap between enterprise tags and product tags, the enterprise being in a preset priority list, or the enterprise possessing specific target tags.
[0068] After receiving matching information, the recommendation output module will present the results to businesses in a clear and intuitive way, such as tables, charts, and text descriptions. This helps businesses quickly understand suitable financial products and related information, providing strong support for their decision-making. Simultaneously, the system also offers interactive features, allowing businesses to further query product details, application processes, and other detailed information, enhancing the user experience.
[0069] This embodiment provides a method for matching enterprises with financial products. This method is applied to the matching module of an enterprise-financial product matching system, which includes an enterprise information module, a product information module, a matching module, and a recommendation output module. The method includes: collecting information stored in the enterprise information module and the product information module to obtain a current enterprise tag set and a current product tag set; determining whether the current enterprise meets a preset priority rule based on the information; when the current enterprise does not meet the preset priority rule, calculating the comprehensive matching degree between the current enterprise and the current product based on the current enterprise tag set and the current product tag set; determining the matching level between the current enterprise and the current product based on the comprehensive matching degree; determining matching information based on the comprehensive matching degree and the matching level, and sending the matching information to the recommendation output module for display. This achieves accurate judgment and recommendation of the matching relationship between enterprises and financial products. By collecting relevant information about enterprises and products to form tag sets, and then using these tag sets to calculate the comprehensive matching degree, the matching level is finally determined and the matching information is displayed. This not only improves the accuracy and efficiency of financial product recommendations but also provides enterprises with more personalized financial service solutions, helping to improve their financial experience and satisfaction.
[0070] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S30 includes steps S301 to S305: Step S301: When the current enterprise does not meet the preset priority rule, traverse the current enterprise tag set and the current product tag set.
[0071] Understandably, if the current enterprise does not meet the preset priority rules, it is necessary to match the tags of the current enterprise with the tags of the current product. Therefore, the tag matching submodule can be used to traverse the tag set of the current enterprise and the tag set of the current product.
[0072] Step S302: When the same semantic tag exists in the current enterprise tag set and the current product tag set, the similarity of the same semantic tag is set to a first preset value.
[0073] In practice, if there are identical semantic tags in the tag set, their similarity can be set to the first preset value. The first preset value can be set according to actual needs. For example, setting it to 1 indicates that the two tags are completely matched, which can play an important role in the subsequent calculation of the comprehensive matching degree.
[0074] For example, if the current enterprise tag set is {new energy, equipment procurement, state-owned enterprise} and the current product tag set is {wind power subsidy, manufacturing support, new energy}, and there is a common semantic tag "new energy", then the similarity of the new energy tag is set to similarity=1.
[0075] Step S303: When there is a semantic relationship between the tags in the current enterprise tag set and the current product tag set, the similarity of the semantically related tags is set to a second preset value.
[0076] In practice, if there is a semantic relationship between the tags in the current enterprise tag set and the tags in the current product tag set, such as "manufacturing support" and "equipment procurement", although the descriptions are not completely consistent, the semantics are similar. In this case, the similarity of the semantically related tags is set to the second preset value. The second preset value is less than the first preset value, for example, it is set to 0.7, so as to reflect that there is a certain degree of matching between them but it is not as high as the matching degree of the same semantic tags.
[0077] For example, similarity can be calculated using a semantic vector model to obtain a second preset value.
[0078] Step S304: When there is no semantic relationship between the tags in the current enterprise tag set and the current product tag set, the similarity of the tags that do not have semantic relationship is set to the third preset value.
[0079] Understandably, if there is neither semantic similarity nor semantic association, it means that there is no connection between enterprise tags and product tags. For example, there is no connection between state-owned enterprises and venture capital subsidies. In this case, the similarity between the two is set to 0, indicating that the two tags contribute little to the matching degree calculation.
[0080] Step S305: Calculate the overall matching degree between the current enterprise and the current product based on the first preset value, the second preset value, and the third preset value.
[0081] In practice, the overall matching degree between the current enterprise and the current product can be calculated based on the first preset value, the second preset value, and the third preset value.
[0082] In one feasible implementation, step S305 may include steps C11 to C15: Step C11: Obtain the first weight of the same semantic label, the second weight of the semantically related label, and the third weight of the label that has no semantically related label or the same semantic label; It should be noted that different weights can be set separately for different tags. For example, the first weight of the same semantic tag is set to 0.4, the second weight of semantically related tags is set to 0.3, and the third weight of tags with no semantically related tags or the same semantic tags is set to 0.3.
[0083] Step C12: Calculate the first matching degree based on the first preset value and the first weight; In practice, the first matching degree can be calculated based on the first preset value and the first weight. For example, if the first preset value is 1 and the first weight is 0.4, then the first matching degree is 1×0.4=0.4.
[0084] Step C13: Calculate the second matching degree based on the second preset value and the second weight; In practice, the second matching degree can be calculated based on the second preset value and the second weight. For example, if the second preset value is 0.7 and the second weight is 0.3, then the second matching degree is 0.7 × 0.3 = 0.21.
[0085] Step C14: Calculate the third matching degree based on the third preset value and the third weight; In practice, the third matching degree can be calculated based on the third preset value and the third weight. For example, if the third preset value is 0 and the second weight is 0.3, then the second matching degree is 0 × 0.3 = 0.
[0086] Step C15: Weight the first matching degree, the second matching degree, and the third matching degree to obtain the comprehensive matching degree between the current enterprise and the current product.
[0087] It should be noted that the first matching degree, the second matching degree, and the third matching degree can be weighted to obtain the final comprehensive matching degree. The comprehensive matching degree = first matching degree + second matching degree + third matching degree = 0.4 + 0.21 + 0 = 0.61.
[0088] As shown in Table 2, Table 2 contains data on the labels between enterprises and products, as well as the correspondence and similarity between the labels.
[0089] Table 2
[0090] By comprehensively matching and calculating the tags, the overall matching degree between company A and product B is S=0.61.
[0091] In this embodiment, when the current enterprise does not meet the preset priority rules, the current enterprise tag set and the current product tag set are traversed. When the current enterprise tag set and the current product tag set contain the same semantic tags, the similarity of the same semantic tags is set to a first preset value. When the tags in the current enterprise tag set and the current product tag set are semantically related, the similarity of the semantically related tags is set to a second preset value. When the tags in the current enterprise tag set and the current product tag set are not semantically related, the similarity of the non-semantic tags is set to a third preset value. The comprehensive matching degree between the current enterprise and the current product is calculated based on the first preset value, the second preset value, and the third preset value. Through the above steps, the degree of association between enterprise tags and product tags can be analyzed in detail, thus providing an accurate basis for the calculation of the comprehensive matching degree. In practical applications, this tag semantic matching degree calculation method has high flexibility and adaptability. On the one hand, it can handle various types of tags, whether directly related or indirectly related, and can reasonably reflect their contribution to the matching degree calculation through the setting of preset values and weights. On the other hand, this method can flexibly adjust the preset values and weights according to actual needs and changes in business scenarios, thereby achieving dynamic optimization of the matching degree calculation rules.
[0092] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 Step S50 includes steps S501 to S504: Step S501: When the matching level is the first matching level, generate a recommendation reason.
[0093] The first matching level is the highest matching level. If the matching level between the current company and the current product is the first matching level, it means that the product and the company are highly compatible and very suitable for the company. In this case, the system needs to generate a recommendation reason to clearly and accurately tell the company why this product is the best choice. The recommendation reason can be based on multiple factors. Specifically, it can be generated based on the company's specific information and the product's specific information. For example, the recommendation reason could be "Company A is a whitelisted company, the tag 'new energy' is a perfect match, and the semantic relevance is high."
[0094] Step S502: Obtain the current company name and the current product name.
[0095] In practice, the specific names of enterprises and products can also be obtained, which facilitates the generation of matching content and makes it easier for users to understand intuitively.
[0096] Step S503: Generate matching information using the current company name, the current product name, the overall matching degree, the matching level, and the recommendation reason.
[0097] In practice, the matching information should include the company's basic information, overall matching score, initial matching level, whether it is in the preset priority list, the adjusted matching level, if there is an adjustment, and the basis for the adjustment, such as the specific terms of the preset priority rules. Therefore, the current company name, current product name, overall matching score, matching level, and recommendation reason can be used as the matching information.
[0098] For example, the matching information is: Company Name: Company A; Product Name: Venture Capital Matching Product for Enterprises and Financial Products; Match score: 0.61; Match rating: High; Recommendation reason: Company A is a whitelisted company, and the tag "new energy" is a perfect match, with high semantic relevance.
[0099] Step S504: Send the matching information to the recommendation output module for display.
[0100] In practice, after receiving the matching information, the recommendation output module can display the specific content.
[0101] If the matching level is medium, it means that the current product and the current company have a certain degree of matching, but it is not the best match. In this case, a recommendation suggestion can be generated to explain to the company that although the product is not the perfect match, it still has certain advantages and applicability. The recommendation suggestion can mention the matching situation of the product with some tags of the company, and can also suggest that the company consider its own actual situation and whether to choose the product, providing the company with a more comprehensive decision-making reference.
[0102] When the matching level is level three, it means that the current product and the current company have a low match degree and may not be able to meet the company's needs well. At this point, the system generates a cautious selection suggestion, reminding the company to carefully consider its choice of the product. The suggestion can analyze specific aspects of the mismatch between the product and the company's tags. For example, the company's tag is "small private enterprise," while the product is mainly targeted at "large state-owned enterprises," showing a significant difference in the applicable enterprise types. Through this detailed analysis, the company clearly understands the mismatch between the product and its business, avoiding unnecessary risks and losses due to blind selection.
[0103] Step S504: Integrate the generated recommendation reasons, recommendation tips or suggestions for careful selection with the comprehensive matching degree and matching level description into matching information.
[0104] After generating the information for different matching levels, the system integrates this information with the specific numerical value of the overall matching degree and the matching level description (e.g., high matching, medium matching, low matching) to form complete matching information. During the integration process, it's crucial to ensure that all information is arranged in an orderly and logically clear manner. For example, the matching level description can be presented first, followed by the specific numerical value of the overall matching degree, and then the reasons for recommendation, recommendation tips, or cautionary suggestions can be displayed for each level. This integrated matching information comprehensively and accurately reflects the matching situation between the enterprise and the product, providing the enterprise with intuitive and easy-to-understand decision-making support. Finally, the integrated matching information is sent to the recommendation output module for display, helping enterprises quickly understand suitable financial products and related information, providing strong support for enterprise decision-making.
[0105] Additionally, the matching process can be automatically re-executed when enterprise or product labels are updated.
[0106] In this embodiment, when the matching level is the first matching level, a recommendation reason is generated; the current company name and current product name are obtained; matching information is generated using the current company name, the current product name, the overall matching degree, the matching level, and the recommendation reason; and the matching information is sent to the recommendation output module for display. The matching information explains the matching logic between the product and the company, allowing the company to clearly understand the basis for product recommendations and enhancing their trust in the recommendation results.
[0107] For example, to help understand the implementation process of the enterprise-financial product matching method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 5 , Figure 5 A simplified flowchart of a method for matching enterprises with financial products is provided, specifically including data collection, rule judgment, tag matching, dynamic grading, and output of matching information.
[0108] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the matching method between the applicant company and financial products. Any simple modifications based on this technical concept are within the scope of protection of this application.
[0109] This application also provides a matching system for enterprises and financial products, comprising: an enterprise information module, a product information module, a matching module, and a recommendation output module connected sequentially. The enterprise information module is also connected to the matching module, and the matching module executes the steps of the enterprise-financial-product matching method described above. The enterprise-financial product matching system provided in this application, employing the enterprise-financial product matching method described in the above embodiments, can solve the technical problem of low accuracy and effectiveness of recommendations due to the simplistic matching methods in the prior art. Compared with the prior art, the beneficial effects of the enterprise-financial product matching system provided in this application are the same as those of the enterprise-financial product matching method provided in the above embodiments, and other technical features of the enterprise-financial product matching system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.
[0110] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the enterprise and financial product matching method described in the above embodiments.
[0111] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
[0112] The aforementioned computer-readable storage medium may be included in a matching system for enterprises and financial products; or it may exist independently and not be incorporated into a matching system for enterprises and financial products.
[0113] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the enterprise-financial product matching system, the enterprise-financial product matching system: collects information stored in the enterprise information module and the product information module to obtain the current enterprise tag set and the current product tag set; and determines whether the current enterprise meets the preset priority rules based on the information. When the current enterprise does not meet the preset priority rules, the comprehensive matching degree between the current enterprise and the current product is calculated based on the current enterprise tag set and the current product tag set; the matching level between the current enterprise and the current product is determined based on the comprehensive matching degree; the matching information is determined based on the comprehensive matching degree and the matching level, and the matching information is sent to the recommendation output module for display.
[0114] Computer program code for performing the operations of this application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0116] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.
[0117] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described enterprise-financial product matching method. This solves the technical problem in the prior art where the single method of matching enterprises and financial products leads to low accuracy and effectiveness of recommendations. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the enterprise-financial product matching method provided in the above embodiments, and will not be elaborated upon here.
[0118] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the enterprise and financial product matching method described above.
[0119] The computer program product provided in this application can solve the technical problem that the single matching method between enterprises and financial products in the prior art leads to low accuracy and effectiveness of recommendations. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the enterprise and financial product matching method provided in the above embodiments, and will not be repeated here.
[0120] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.
Claims
1. A method for matching enterprises with financial products, characterized in that, The enterprise-financial product matching method is applied to the matching module of the enterprise-financial product matching system. The enterprise-financial product matching system includes: an enterprise information module, a product information module, a matching module, and a recommendation output module. The method for matching enterprises with financial products includes: Collect information stored in the enterprise information module and the product information module to obtain the current enterprise tag set and the current product tag set; Based on the information, determine whether the current enterprise meets the preset priority rules; When the current enterprise does not meet the preset priority rules, the comprehensive matching degree between the current enterprise and the current product is calculated based on the current enterprise tag set and the current product tag set. The matching level between the current enterprise and the current product is determined based on the comprehensive matching degree. Matching information is determined based on the overall matching degree and the matching level, and the matching information is sent to the recommendation output module for display.
2. The method as described in claim 1, characterized in that, The step of calculating the comprehensive matching degree between the current enterprise and the current product based on the current enterprise tag set and the current product tag set when the current enterprise does not meet the preset priority rules includes: When the current enterprise does not meet the preset priority rules, traverse the current enterprise tag set and the current product tag set; When the same semantic tag exists in the current enterprise tag set and the current product tag set, the similarity of the same semantic tag is set to a first preset value; When there is a semantic relationship between the tags in the current enterprise tag set and the current product tag set, the similarity of the semantically related tags is set to a second preset value; When there is no semantic relationship between the tags in the current enterprise tag set and the current product tag set, the similarity of the tags that do not have semantic relationship is set to the third preset value; The overall matching degree between the current enterprise and the current product is calculated based on the first preset value, the second preset value, and the third preset value.
3. The method as described in claim 2, characterized in that, The step of calculating the overall matching degree between the current enterprise and the current product based on the first preset value, the second preset value, and the third preset value includes: Obtain the first weight of the same semantic tag, the second weight of the semantically related tag, and the third weight of the tag that has no semantically related tag or the same semantic tag; Calculate the first matching degree based on the first preset value and the first weight; The second matching degree is calculated based on the second preset value and the second weight; The third matching degree is calculated based on the third preset value and the third weight; The first matching degree, the second matching degree, and the third matching degree are weighted to obtain the comprehensive matching degree between the current enterprise and the current product.
4. The method as described in claim 1, characterized in that, The step of determining whether the current enterprise meets the preset priority rule based on the information includes: The current product whitelist and target status are obtained according to the preset priority rules; Based on the information, we can determine the current enterprise category and the current business status. When the category to which the current enterprise belongs is within the whitelist of the current product or the current business status of the enterprise is the target status, it is determined that the current enterprise meets the preset priority rules; If the current enterprise's category is not in the current product whitelist and the enterprise's current business status is not the target status, then the current enterprise is determined not to meet the preset priority rule.
5. The method as described in claim 1, characterized in that, After the step of determining whether the current enterprise meets the preset priority rule based on the information, the method further includes: When the current enterprise meets the preset priority rules and the category to which the enterprise belongs is within the current product whitelist, the product to be recommended is determined to be the current product; When the current enterprise meets the preset priority rules, the enterprise's category is not in the current product whitelist, and the enterprise's current business status is the target status, the product to be recommended is determined to be the first financial product; The current product or the first financial product is sent to the recommendation output module so that the recommendation output module recommends the current product or the first financial product to the current enterprise.
6. The method as described in claim 1, characterized in that, The step of determining matching information based on the overall matching degree and the matching level, and sending the matching information to the recommendation output module for display includes: When the matching level is the first matching level, a recommendation reason is generated; Get the current company name and the current product name; Matching information is generated using the current company name, the current product name, the overall matching degree, the matching level, and the recommendation reason. The matching information is sent to the recommendation output module for display.
7. The method as described in claim 1, characterized in that, Before the step of determining matching information based on the comprehensive matching degree and the matching level, and sending the matching information to the recommendation output module for display, the method further includes: Check whether the current company is on a preset priority list; When the current enterprise is within the preset priority list, the matching level is raised to the preset matching level; Check if the target tag exists in the current enterprise tag set for the current enterprise tag; When the target tag exists in the current enterprise tag, obtain the target weight factor; The overall matching degree is adjusted according to the target weight factor to obtain an updated overall matching degree.
8. The method according to any one of claims 1 to 7, characterized in that, The step of determining the matching level between the current enterprise and the current product based on the comprehensive matching degree includes: The overall matching degree is compared with a first preset matching degree threshold and a second preset matching degree threshold; When the overall matching degree is greater than the first preset matching degree threshold, the matching level between the current enterprise and the current product is determined to be the first matching level; When the overall matching degree is less than the first preset matching degree threshold and greater than the second preset matching degree threshold, the matching level between the current enterprise and the current product is determined to be the second matching level; When the overall matching degree is less than the second preset matching degree threshold, the matching level between the current enterprise and the current product is determined to be the third matching level, wherein the first matching level is greater than the second matching level, and the second matching level is greater than the third matching level.
9. A matching system for enterprises and financial products, characterized in that, The enterprise-financial product matching system includes: an enterprise information module, a product information module, a matching module, and a recommendation output module connected in sequence. The enterprise information module is also connected to the matching module. The matching module performs the steps of the enterprise-financial product matching method as described in any one of claims 1 to 8.
10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the enterprise and financial product matching method as described in any one of claims 1 to 8.