Big data-based financing service intelligent recommendation method, electronic equipment and computer readable medium

Abstract

The invention discloses a financing service intelligent recommendation method based on big data, electronic equipment and a computer readable medium. The method comprises the following steps: receiving an enterprise financing application; obtaining related data of the enterprise, dynamically evaluating the credit of the enterprise, and if the credit score of the enterprise reaches a preset value, continuing to execute a financing recommendation process; the financing recommendation process comprises the following steps: preliminarily screening out financial products matched with enterprise financing demands, and scoring the financial products according to a preset recommendation rule; and sorting the financial products according to the scores, selecting at least one financial product according to the ranking, pushing enterprise financing application data to a financial institution corresponding to the selected financial product and obtaining a pre-credit result, and if data of successful pre-credit exists, analyzing the pre-credit result, selecting a target financial product and pushing the target financial product to an enterprise client. The financing service intelligent recommendation method can intelligently recommend financial service products accurately matched with enterprise financing requirements for enterprises.

Description

Technical Field

[0001] The present invention relates to the field of financial service technology, and in particular to a big data-based intelligent recommendation method for financing services, an electronic device, and a computer-readable medium. Background Art

[0002] In traditional financial services, a common model can be vividly called a "financial product supermarket". The main function of a "financial product supermarket" is to publish, display, retrieve and transmit product and policy information. In this model, financial institutions will provide a series of diversified financial products, just like a variety of commodities displayed in a supermarket, for consumers to choose according to their needs and preferences.

[0003] However, the main problem with traditional financial services is that it is difficult to match corporate financing needs with financial institutions' products. Faced with a large number of financial institution products, companies do not know how to choose and need to spend a lot of manpower and material resources to match their needs with products, which is inefficient. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes a financing service intelligent recommendation method based on big data, an electronic device and a computer-readable medium. The financing service intelligent recommendation method based on big data of the present invention can intelligently recommend financial service products that accurately match the financing needs of the enterprise to the enterprise, saving manpower and time costs.

[0005] In a first aspect, the present invention provides a method for intelligently recommending financing services based on big data, comprising the following steps:

[0006] Receive corporate financing applications;

[0007] Financing recommendation, including: preliminarily screening out financial products that match the financing needs of the enterprise, scoring the financial products according to preset recommendation rules, sorting the financial products according to the scores, and selecting at least one financial product according to the ranking;

[0008] The enterprise financing application data is pushed to the financial institution corresponding to the selected financial product and the pre-credit results are obtained. If there is data of successful pre-credit, the pre-credit results are analyzed, and the target financial products are selected and pushed to the enterprise client; if all pre-credit results fail, the information that the pre-credit failed is fed back to the enterprise client.

[0009] Furthermore, the intelligent recommendation method for financing services based on big data of the present invention also includes the following steps: obtaining enterprise-related data, dynamically evaluating the credit of the enterprise, and if the enterprise credit score reaches a preset value, continuing the financing recommendation process, otherwise, ending the process.

[0010] Furthermore, relevant data of the enterprise is obtained to dynamically evaluate the credit of the enterprise, including:

[0011] S21. Obtain enterprise-related data and pre-process the enterprise-related data:

[0012] S22. Build a risk calculation model, train the model to eventually form a mature data model, label and index the enterprise-related data, and calculate the enterprise credit score based on the data model.

[0013] Furthermore, financial products that match the financing needs of the enterprise are preliminarily screened out, and the financial products are scored according to the preset recommendation rules, the financial products are sorted according to the scores, and at least one financial product is selected according to the ranking, including:

[0014] S31. Obtain enterprise data and build enterprise portraits: Obtain the financing needs of enterprises, analyze and summarize enterprise data into various indicators related to financing services, and create user portraits for enterprises based on various indicators;

[0015] S32. Financial product data acquisition and preprocessing: collect financial product data provided by various financial institutions, preprocess the financial product data, and label the financial product data. Through text and semantic analysis, classify the labels and obtain the threshold, interval and range of each label, and store the financial product data and labels.

[0016] S33. Compare the labels of financial products one by one according to the enterprise portrait: According to the various indicator characteristics of the enterprise portrait, obtain the label data of the financial products, compare the labels under the financial products with the enterprise financing information, if the enterprise financing information does not match the threshold or interval range of a certain label of the financial product, the financial product will not pass the screening, compare all financial products one by one, and obtain financial products that meet the financing needs of the enterprise;

[0017] S34. Score and sort the financial products according to preset recommendation rules: The preset recommendation rules include at least interest rate, credit limit, loan term, region, and guarantee method. Set corresponding conditions, scores, and weights for the recommendation rules according to their attributes. Compare the labels of the financial products that have passed the preliminary screening with the recommendation rules one by one, calculate the scores according to the conditions of the recommendation rules, calculate the total score by multiplying the scores of all recommendation rules by the sum of the corresponding weights, sort the financial products according to the total score, and select the top M financial product data.

[0018] Furthermore, the financial product data is pre-processed, specifically including: cleaning, classifying, integrating and processing the financial product data.

[0019] Furthermore, the financial product data includes at least financial institution information, credit limit, credit period, loan interest rate, guarantee method and application conditions.

[0020] Furthermore, the enterprise financing application data is pushed to the financial institution corresponding to the selected financial product and the pre-credit results are obtained. If there is data of successful pre-credit, the pre-credit results are analyzed, and the target financial products are selected and pushed to the enterprise client; if all pre-credit results fail, the information that the pre-credit fails is fed back to the enterprise client, including:

[0021] S41, push the enterprise financing application data to the financial institutions corresponding to the financial products ranked 1 to N and obtain the pre-credit results. If all the pre-credit results fail, go to step S42; if there is data of successful pre-credit, go to step S43;

[0022] S42, push the enterprise financing information to the financial institutions corresponding to the financial products ranked N+1 to M and obtain the credit results, where M and N are positive integers and M is greater than N;

[0023] S43. Analyze the pre-credit results. If there is successful data in the pre-credit results, select the target financial product and push it to the enterprise client. If all the pre-credit results fail, feedback the information that the pre-credit is not passed to the enterprise, and the target financial product is the optimal financial product.

[0024] Furthermore, the pre-credit results are analyzed, and target financial products are selected and pushed to the enterprise client, specifically including: if there is only one successful data in the pre-credit results, the financial product corresponding to the successful data is directly selected as the target financial product, i.e., the optimal financial product; if there are multiple successful data in the pre-credit results, multiple target indicators of the financial product data are compared, and the target financial product, i.e., the optimal financial product, is selected according to the weighted calculation of the indicators, and the credit interface of the financial institution corresponding to the target financial product, i.e., the optimal financial product, is called to obtain the formal credit limit information, and the credit result is pushed to the enterprise client.

[0025] In a second aspect, an embodiment of the present disclosure provides an electronic device, including:

[0026] at least one processor; and

[0027] a memory communicatively connected to the at least one processor; wherein,

[0028] The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor so that the at least one processor can execute the intelligent recommendation method for financing services as described in the first aspect.

[0029] In a third aspect, an embodiment of the present disclosure further provides a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps in the method for intelligent recommendation of financing services as described in the first aspect.

[0030] The present invention has at least the following beneficial effects:

[0031] The present invention provides a financing service intelligent recommendation method based on big data, which makes full use of big data technology to intelligently recommend financial service products that accurately match the financing needs of enterprises, saving manpower and time costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of a method for intelligently recommending financing services based on big data provided in an embodiment of the present disclosure;

[0033] Figure 2 A specific flow chart of step S2 provided in the embodiment of the present disclosure;

[0034] Figure 3 A specific flow chart of step S3 provided in the embodiment of the present disclosure;

[0035] Figure 4 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the present disclosure is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0037] Unless otherwise defined, the technical terms or scientific terms used in the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, similar words such as "one", "one" or "the" do not indicate quantitative restrictions, but indicate that there is at least one. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0038] In each of the accompanying drawings, identical elements are represented by similar reference numerals. For the sake of clarity, the various parts in the accompanying drawings are not all drawn to scale. In addition, some well-known parts may not be shown in the drawings.

[0039] Many specific details of the present disclosure are described below to provide a clearer understanding of the present disclosure. However, as those skilled in the art will appreciate, the present disclosure may be implemented without following these specific details.

[0040] Figure 1 The following is a flow chart of a method for intelligently recommending financing services based on big data provided by an embodiment of the present disclosure. Figure 1 As shown, the present invention provides a method for intelligent recommendation of financing services based on big data, comprising the following steps:

[0041] S1. Receive corporate financing applications.

[0042] When an enterprise applies for financing, it needs to fill in financing information. Users can initiate a financing application after filling in the financing information through the enterprise client.

[0043] Financing information includes basic company information, uploading of supporting documents, and filling in the expected financing amount and financing period.

[0044] S2: Obtain enterprise-related data and dynamically evaluate the enterprise's credit. If the enterprise's credit score reaches the preset value, continue to S3, otherwise the process ends;

[0045] S3. Preliminarily screen out financial products that match the financing needs of the enterprise, score the financial products according to the preset recommendation rules, rank the financial products according to the scores (from high to low), and select at least one financial product according to the ranking;

[0046] S4. Push the enterprise financing application data to the financial institution corresponding to the selected financial product and obtain the pre-credit result;

[0047] If there is data on successful pre-credit approval, analyze the pre-credit approval results, select the target financial products and push them to the corporate client; if all pre-credit approval results fail, feedback the information that the pre-credit approval failed is given to the corporate client.

[0048] Step S2 obtains enterprise-related data and conducts dynamic assessment of the enterprise. Through multi-dimensional, in-depth, and wide-angle modeling analysis and risk measurement of the enterprise, the credit of the enterprise is dynamically assessed. Figure 2 , step S2 comprises the following steps:

[0049] S21. Collect relevant data of enterprises, pre-process and store relevant data of enterprises: obtain the information filled in when the enterprise applies for financing, obtain the information disclosed by the enterprise, and collect the business data, log data, user behavior data and other data of the enterprise through external source data, screen, clean, classify, integrate, process and store the collected data, so that the collected data can form data assets;

[0050] S22. Risk measurement and credit scoring: Build a risk measurement model, train the model and eventually form a mature data model; label and index the enterprise-related data, and calculate the credit score based on the data model;

[0051] S23. Dynamic evaluation results: Conduct decision analysis on the evaluation results. If the enterprise credit score does not meet the requirements, the application process ends.

[0052] Of course, in some embodiments, step S2 may be omitted, that is, step S3 may be directly executed after receiving the enterprise financing application.

[0053] Step S3 preliminarily screens financial products that match the enterprise's financing needs and scores the financial products according to the preset recommendation rules, see Figure 3 , step S3 comprises the following steps:

[0054] S31. Collect and organize enterprise data and build enterprise portraits: Collect the financing needs of enterprises, and analyze and summarize the enterprise data into various indicators related to financing services, such as basic information, financial information, financing preferences, etc., and create user portraits for the enterprises based on various indicators.

[0055] S32. Financial product data collection and preprocessing: Collect financial product data provided by various financial institutions. Financial products include financial institution information, credit line, credit period, loan interest rate, guarantee method and application conditions, etc. Clean, classify, integrate and process financial product data, and label financial product data such as credit line, interest rate, period, guarantee method, industry and region, organize and classify various labels through text and semantic analysis, obtain thresholds, intervals and ranges of various labels, and store financial product data and various labels;

[0056] S33. Compare the labels of financial products one by one according to the corporate portrait: According to the various indicator characteristics of the corporate portrait, obtain the label data of the financial products, and compare the labels under the financial products with the corporate financing information. If the corporate financing information does not match the threshold or range of a certain label of the financial product, the financial product will not pass the screening; compare all financial products one by one, and store the financial products that meet the corporate financing needs.

[0057] S34. Score and sort the financial products according to the preset system recommendation rules: The preset system recommendation rules include interest rate, credit limit, loan term, region, guarantee method, etc. Set corresponding conditions, scores and weights for them according to the attributes of the system recommendation rules, compare the labels of the financial products that have passed the preliminary screening with the system recommendation rules one by one, calculate the scores according to the system recommendation rule conditions, and calculate the total score by multiplying the sum of the scores of all system recommendation rules by the corresponding weights, which is the score of the financial product. Sort the financial products according to the total score, that is, the score of the financial product, and store the data of the top M financial products.

[0058] Furthermore, step S4 specifically includes:

[0059] S41, push the enterprise financing application data to the financial institutions corresponding to the financial products ranked 1 to N and obtain the pre-credit results. If all the pre-credit results fail, go to step S42; if there is data of successful pre-credit, go to step S43;

[0060] S42, push the enterprise financing information to the financial institutions corresponding to the financial products ranked from N+1 to M and obtain the credit results, where M and N are positive integers and M is greater than N;

[0061] S43. Analyze the pre-credit results. If there is successful data in the pre-credit results, select the target financial product and push it to the enterprise client. If all the pre-credit results fail, feedback the information that the pre-credit is not passed to the enterprise, and the target financial product is the optimal financial product.

[0062] The values ​​of M and N are set as needed. For example, in one embodiment, M is 6 and N is 3. The specific process of step S4 is as follows:

[0063] S41. Push the enterprise financing application data to the financial institutions corresponding to the top three financial products and obtain the pre-credit results: the system queries the financial institution information of the top three financial products, packages the enterprise financing application data, and sends it to the financial institution synchronously, calls the pre-credit interface of the financial institution and obtains the pre-credit results. If all the pre-credit results fail, go to step S42; if there is data of successful pre-credit, go to step S43;

[0064] S42. Push the enterprise financing information to the financial institutions corresponding to the next three financial products and obtain the credit results: The system queries the financial institution information of the fourth to sixth ranked financial products, packages the enterprise financing application data, and sends it to the financial institution synchronously, calls the pre-credit interface of the financial institution and obtains the pre-credit results.

[0065] S43 analyzes the pre-credit results, selects the target financial product, i.e. the best financial product, and obtains the official credit limit and pushes it to the enterprise: If all pre-credit results fail, the system will feedback the information of pre-credit failure (i.e. the reason) to the enterprise, so that the enterprise can modify the materials according to the feedback reason and reapply for financing.

[0066] Furthermore, the pre-credit results are analyzed, and the target financial products are selected and pushed to the enterprise client, specifically including: if there is only one successful data in the pre-credit results, the financial product corresponding to the successful data is directly selected as the target financial product, i.e., the optimal financial product; if there are multiple successful data in the pre-credit results, multiple target indicators of the financial product data (including credit limit, credit period, loan interest rate and other indicators) are compared, and the target financial product, i.e., the optimal financial product, is selected according to the weighted calculation of the indicators, and the credit interface of the financial institution corresponding to the target financial product, i.e., the optimal financial product, is called to obtain the formal credit limit information, and the credit result is pushed to the enterprise client.

[0067] The present invention provides a method for intelligently recommending financing services based on big data, which makes full use of big data technology to intelligently recommend financial service products that accurately match the financing needs of enterprises, saving manpower and time costs, enabling government agencies and financial institutions to jointly launch more customized financial products to accurately benefit enterprises, improve the level of inclusive financial services, and more fully mobilize financial institutions to support small, medium and micro enterprises in accelerating their development, alleviating the problems of "difficult, expensive and slow financing" for enterprises.

[0068] Based on the same inventive concept, an embodiment of the present disclosure also provides an electronic device. Figure 4 FIG. 1 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Figure 4 As shown, an electronic device includes: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement any intelligent recommendation method for financing services in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, and are configured to implement information interaction between the processor and the memory.

[0069] Among them, the processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU), etc.; the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); the I / O interface (read-write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus), etc.

[0070] In some embodiments, the processor 101 , the memory 102 , and the I / O interface 103 are connected to each other via a bus 104 , and further connected to other components of the computing device.

[0071] In some embodiments, the one or more processors 101 include a field programmable gate array.

[0072] Based on the same inventive concept, the embodiment of the present disclosure further provides a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps in any of the intelligent recommendation methods for financing services in the above embodiments are implemented.

[0073] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, including a computer program carried on a machine-readable medium, the computer program containing a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the system of the present disclosure are executed.

[0074] It should be noted that the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0075] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of the code, and the aforementioned module, program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0076] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A method for intelligent recommendation of financing services based on big data, characterized in that: The steps include: Receive corporate financing applications; Financing recommendation, including: preliminarily screening out financial products that match the financing needs of the enterprise, scoring the financial products according to preset recommendation rules, sorting the financial products according to the scores, and selecting at least one financial product according to the ranking; The enterprise financing application data is pushed to the financial institution corresponding to the selected financial product and the pre-credit results are obtained. If there is data of successful pre-credit, the pre-credit results are analyzed, and the target financial products are selected and pushed to the enterprise client; if all pre-credit results fail, the information that the pre-credit failed is fed back to the enterprise client.

2. The intelligent recommendation method for financing services based on big data according to claim 1, characterized in that: It also includes the following steps: obtaining enterprise-related data, dynamically evaluating the enterprise's credit, and if the enterprise's credit score reaches a preset value, continuing the financing recommendation process, otherwise, ending the process.

3. The intelligent recommendation method for financing services based on big data according to claim 2, characterized in that: Obtain enterprise-related data and conduct dynamic assessment of the enterprise's credit, including: S21. Obtain enterprise-related data and pre-process the enterprise-related data: S22. Build a risk calculation model, train the model to eventually form a mature data model, label and index the enterprise-related data, and calculate the enterprise credit score based on the data model.

4. The intelligent recommendation method for financing services based on big data according to claim 1, characterized in that: Preliminarily screen out financial products that match the financing needs of the enterprise, score the financial products according to the preset recommendation rules, sort the financial products according to the scores, and select at least one financial product based on the ranking, including: S31. Obtain enterprise data and build enterprise portraits: Obtain the financing needs of enterprises, analyze and summarize enterprise data into various indicators related to financing services, and create user portraits for enterprises based on various indicators; S32. Financial product data acquisition and preprocessing: collect financial product data provided by various financial institutions, preprocess the financial product data, and label the financial product data. Through text and semantic analysis, classify the labels and obtain the threshold, interval and range of each label, and store the financial product data and labels. S33. Compare the labels of financial products one by one according to the enterprise portrait: According to the various indicator characteristics of the enterprise portrait, obtain the label data of the financial products, compare the labels under the financial products with the enterprise financing information, if the enterprise financing information does not match the threshold or interval range of a certain label of the financial product, the financial product will not pass the screening, compare all financial products one by one, and obtain financial products that meet the financing needs of the enterprise; S34. Score and sort the financial products according to preset recommendation rules: The preset recommendation rules include at least interest rate, credit limit, loan term, region, and guarantee method. Set corresponding conditions, scores, and weights for the recommendation rules according to their attributes. Compare the labels of the financial products that have passed the preliminary screening with the recommendation rules one by one, calculate the scores according to the recommendation rule conditions, and calculate the total score of the financial product by multiplying the scores of all recommendation rules by the corresponding weights. Sort the financial products according to the scores, and select the top M financial product data.

5. The intelligent recommendation method for financing services based on big data according to claim 4 is characterized by: Pre-process the financial product data, including: cleaning, classification, integration and processing the financial product data.

6. The intelligent recommendation method for financing services based on big data according to claim 4, characterized in that: Financial product data shall at least include financial institution information, credit limit, credit period, loan interest rate, guarantee method and application conditions.

7. The intelligent recommendation method for financing services based on big data according to claim 1, characterized in that: Push the enterprise financing application data to the financial institution corresponding to the selected financial product and obtain the pre-credit results. If there is data of successful pre-credit, analyze the pre-credit results, select the target financial product and push it to the enterprise client; If all pre-credit results fail, the information that the pre-credit fails will be fed back to the enterprise client, including: S41, push the enterprise financing application data to the financial institutions corresponding to the financial products ranked 1 to N and obtain the pre-credit results. If all the pre-credit results fail, go to step S42; if there is data of successful pre-credit, go to step S43; S42, push the enterprise financing information to the financial institutions corresponding to the financial products ranked N+1 to M and obtain the credit results, where M and N are positive integers and M is greater than N; S43. Analyze the pre-credit results. If there is successful data in the pre-credit results, select the target financial product and push it to the enterprise client. If all the pre-credit results fail, feedback the information that the pre-credit is not passed to the enterprise, and the target financial product is the optimal financial product.

8. The intelligent recommendation method for financing services based on big data according to claim 1 or 7, characterized in that: Analyze the pre-credit results and select the target financial product to push to the enterprise client, specifically including: if there is only one successful data in the pre-credit results, directly select the financial product corresponding to the successful data as the target financial product, i.e. the optimal financial product; if there are multiple successful data in the pre-credit results, compare multiple target indicators of the financial product data, select the target financial product, i.e. the optimal financial product, by weighted calculation of the indicators, call the credit interface of the financial institution corresponding to the target financial product, i.e. the optimal financial product, to obtain the formal credit limit information, and push the credit result to the enterprise client.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor so that the at least one processor can execute the intelligent recommendation method for financing services as described in any one of claims 1-8.

10. A computer-readable medium, characterized in that The computer-readable medium stores a computer program, wherein when the program is executed by a processor, the steps in the intelligent recommendation method for financing services as described in any one of claims 1 to 8 are implemented.

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