Financial credit extension scheme generation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a financial credit extension scheme generation method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring credit granting business data of a target customer; inputting the credit granting service data into a preset credit granting analysis model, and performing funnel analysis calculation on the credit granting service data by the credit granting analysis model to obtain credit granting gear information; and generating a financial credit extension scheme of the target customer based on the credit extension gear information. According to the embodiment of the invention, the efficiency of credit granting of financial institutions such as banks to clients to generate credit granting schemes can be improved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a method and device for generating a financial credit scheme, an electronic device, and a storage medium. Background Art

[0002] At present, when banks and other financial institutions grant credit to customers, they manually compare and screen the table data, and then determine the credit plan for the customer based on manual experience. However, since a lot of business data is needed when granting credit to customers, and staff are required to accurately control the financial risks of the credit plan, the manual processing of table data to generate credit plans usually takes a lot of time and human resources, resulting in very low efficiency in generating corresponding credit plans for customers. Therefore, how to improve the efficiency of generating credit plans for customers has become a technical problem that needs to be solved urgently in the field of financial technology. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a method and device for generating a financial credit plan, an electronic device and a storage medium, aiming to improve the efficiency of banks and other financial institutions in generating credit plans for customers.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for generating a financial credit scheme, the method comprising:

[0005] Obtain credit business data of target customers;

[0006] Inputting the credit business data into a preset credit analysis model, and having the credit analysis model perform funnel analysis calculation on the credit business data to obtain credit level information;

[0007] A financial credit plan for the target customer is generated based on the credit level information.

[0008] In some embodiments, the credit level information includes a credit level type; the credit analysis model performs a funnel analysis calculation on the credit business data to obtain the credit level information, including:

[0009] Obtaining credit configuration parameters of the credit analysis model;

[0010] The credit analysis model performs funnel analysis calculation on the credit business data based on the credit configuration parameters to determine the credit level type.

[0011] In some embodiments, the credit business data includes customer data and credit target data; performing funnel analysis calculation on the credit business data based on the credit configuration parameters by the credit analysis model includes:

[0012] Analyze the customer data through the credit analysis model to obtain a customer information analysis result corresponding to the customer data;

[0013] Perform a funnel analysis calculation on the credit target data through the credit analysis model based on the customer information analysis result and the credit configuration parameters.

[0014] In some embodiments, the credit target data includes product data and financing financial data; the performing a funnel analysis calculation on the credit target data through the credit analysis model based on the customer information analysis result and the credit configuration parameters includes:

[0015] Determine the customer type of the target customer indicated by the customer information analysis result, where the customer type is a new customer or a historical customer;

[0016] Call a target analysis logic applicable to the customer type through the credit analysis model;

[0017] Analyze the product data through the credit analysis model according to the target analysis logic and the credit configuration information to obtain a credit limit analysis result of the brand corresponding to the product data;

[0018] Perform a risk exposure calculation through the credit analysis model based on the credit limit analysis result, the customer data, and the financing financial data.

[0019] In some embodiments, the analyzing the product data through the credit analysis model according to the target analysis logic and the credit configuration information to obtain a credit limit analysis result of the brand corresponding to the product data includes:

[0020] Analyze the product data through the credit analysis model according to the target analysis logic and the target credit configuration information in the credit configuration information to obtain a shared limit status information of the brand corresponding to the product data; where the target credit configuration information is the credit limit configuration information of the brand corresponding to the product data;

[0021] Perform a credit limit calculation through the credit analysis model based on the shared limit status information to obtain a credit limit analysis result of the brand corresponding to the product data.

[0022] In some embodiments, the method further includes:

[0023] Obtain model configuration data;

[0024] Update the credit configuration parameters of the credit analysis model based on the model configuration data to obtain new credit configuration parameters;

[0025] Obtaining the credit configuration parameters of the credit analysis model includes:

[0026] Determining the new credit configuration parameters as the credit configuration parameters of the credit analysis model.

[0027] In some embodiments, before inputting the credit business data into a preset credit analysis model, the method further includes:

[0028] Obtaining credit business sample data;

[0029] Performing model training with funnel analysis calculation based on the business sample data and preset model configuration data to obtain a credit analysis model.

[0030] To achieve the above object, a second aspect of the embodiments of the present application provides a device for generating a financial credit scheme, the device includes:

[0031] A data acquisition module, configured to acquire credit business data of a target customer;

[0032] A funnel analysis module, configured to input the credit business data into a preset credit analysis model, and perform funnel analysis calculation on the credit business data by the credit analysis model to obtain credit grade information;

[0033] A scheme generation module, configured to generate a financial credit scheme for the target customer based on the credit grade information.

[0034] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the method for generating a financial credit scheme described in the first aspect above is implemented.

[0035] To achieve the above object, a fourth aspect of the embodiments of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for generating a financial credit scheme described in the first aspect above is implemented.

[0036] The method and device for generating a financial credit scheme, the electronic device, and the storage medium provided by the embodiments of the present application first acquire the credit business data of the target customer to be evaluated for financial credit, and then input the credit business data into a pre-designed and trained credit analysis model, so that the credit analysis model performs funnel analysis calculation on the credit business data to obtain corresponding credit grade information, and finally generate a financial credit scheme for the target customer based on the credit grade information output by the model.

[0037] Thus, compared with the method of manually comparing and screening tabular data to determine the generation of a credit granting plan, in scenarios such as the bank and other financial institutions recommending a pre-rental credit granting plan for customers, the embodiments of the present application use a credit analysis model based on funnel analysis to perform funnel analysis calculations on credit business data including parameters such as products, credit limits, and customer information, so as to determine the corresponding credit levels to generate a financial credit granting plan for customers, realizing the automatic screening and recommendation of the credit granting plan level for customers based on funnel analysis, thereby improving the efficiency of processing credit granting business for customers to generate a credit granting plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flowchart of the steps in some embodiments of the method for generating a financial credit granting plan provided by the embodiments of the present application;

[0039] Figure 2 is Figure 1 a schematic flowchart of the refined steps of step S102 in

[0040] Figure 3 It is a schematic diagram of the model configuration page involved in some embodiments of the method for generating a financial credit granting plan provided by the embodiments of the present application;

[0041] Figure 4 is Figure 2 a schematic flowchart of the refined steps of step S202 in

[0042] Figure 5 is Figure 4 a schematic flowchart of the refined steps of step S402 in

[0043] Figure 6 It is a schematic flowchart of the steps in some other embodiments of the method for generating a financial credit granting plan provided by the embodiments of the present application;

[0044] Figure 7 It is a schematic flowchart of the working process of the credit analysis model involved in some embodiments of the method for generating a financial credit granting plan provided by the embodiments of the present application;

[0045] Figure 8 It is a schematic diagram of the structure of the device for generating a financial credit granting plan provided by the embodiments of the present application;

[0046] Figure 9 It is a schematic diagram of the hardware structure of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] It should be noted that although functional module division is carried out in the device schematic diagram and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in a different module division in the device or a different sequence in the flowchart. Terms such as "first" and "second" in the specification, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0050] First, several nouns involved in the embodiments of the present application are analyzed:

[0051] Funnel analysis: As one of the most important analysis methods in the field of data analysis, funnel analysis plays an irreplaceable role in business process decomposition and problem link positioning. In the field of financial risk control, funnel analysis is mainly applied to credit process analysis. By quantitatively analyzing the conversion rate and loss rate at each stage from application to approval and then to loan disbursement, it can quickly locate the problem links in the process. This enables the risk control team to timely adjust risk control rules, optimize the review process or introduce more advanced risk control technologies, thereby effectively reducing risks and improving business efficiency. At the same time, in the analysis of user behavior paths in financial platforms / systems (which can be insurance systems, banking systems, trading systems, order systems, etc.), funnel analysis can also help the risk control system evaluate the behavior patterns of different user groups and identify potential fraud behaviors or high-risk operations. For example, abnormal high-frequency application behaviors, behaviors such as modifying personal information multiple times in a short period may indicate potential risks.

[0052] However, due to the specific funnel analysis requirements of different businesses (that is, each business has relatively high customization requirements and configuration requirements for funnel analysis), and each analysis node involves numerous parameters, and the attribute associations between the parameters are relatively complex, and the final analysis result presentation form also shows diverse characteristics. However, traditional product analysis tools often have single functions, making it difficult to meet the above-mentioned complex requirements of businesses for funnel analysis, resulting in poor user experience and difficulty in effectively supporting business development.

[0053] Natural Language Processing (NLP): NLP uses computers to process, understand, and apply human languages (such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary field of computer science and linguistics, and is often referred to as computational linguistics. Natural language processing includes syntactic analysis, semantic analysis, discourse understanding, etc. Natural language processing is commonly used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intention recognition, information extraction and filtering, text classification and clustering, public opinion analysis, and opinion mining. It involves data mining, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research related to language processing, and linguistic research related to language computing, etc.

[0054] Information Extraction: A text processing technology that extracts factual information such as specified types of entities, relationships, events, etc. from natural language texts and forms structured data for output. Information extraction is a technology for extracting specific information from text data. Text data is composed of some specific units, such as sentences, paragraphs, and passages. Text information is exactly composed of some small specific units, such as characters, words, phrases, sentences, paragraphs, or combinations of these specific units. Extracting noun phrases, personal names, place names, etc. from text data are all text information extraction. Of course, the information extracted by text information extraction technology can be various types of information.

[0055] Next, the overall technical concept of the embodiments of the present application is proposed.

[0056] In the actual business applications of current financial institutions such as banks for granting credit to customers, it still mainly relies on risk control managers to manually compare and screen the conditions in the form, and then recommend the credit granting plan for customers based on this. However, this way of manually processing form data to recommend credit granting plans for customers has problems of low efficiency and high error rate. Because in the business of granting credit to customers, a large amount of business data is often required, and it also requires staff to accurately control the financial risks of the credit granting plan based on their own experience. Therefore, the method of manually processing form data to generate credit granting plans usually requires a large amount of time and human resources, resulting in very low efficiency in generating corresponding credit granting plans for customers and a relatively high error rate.

[0057] Based on this, the embodiments of the present application provide a method and device for generating a financial credit granting plan, an electronic device, and a storage medium, aiming to improve the efficiency of financial institutions such as banks in generating credit granting plans for customers.

[0058] The method and apparatus for generating a financial credit-granting scheme, an electronic device, and a storage medium provided by the embodiments of the present application will be specifically described through the following embodiments. First, the method for generating a financial credit-granting scheme in the embodiments of the present application will be described.

[0059] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0060] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0061] It should be noted that in each specific implementation manner of the present application, when it comes to relevant processing that needs to be performed based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or separate consent will be obtained through methods such as pop-up windows or redirecting to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0062] The method for generating a financial credit granting scheme provided by the embodiments of the present application relates to the field of fintech. The method for generating a financial credit granting scheme provided by the embodiments of the present application can be applied to a terminal, or to a server side, or can be software running on a terminal or a server side. In some embodiments, the terminal can be a terminal device of a financial platform / system (such as a financial credit granting service, etc.) provided by a financial institution such as a bank to customers, and can specifically be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application for implementing the method for generating a financial credit granting scheme, etc., but is not limited to the above forms.

[0063] The present application can be used in many general or specific computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet-type devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] For the convenience of understanding and elaboration, in the following text, the method for generating a financial credit granting scheme provided by the embodiments of the present application applied to a terminal device will be taken as an example for detailed description. The implementation of the method for generating a financial credit granting scheme provided by the embodiments of the present application by any of the above-mentioned form entities can refer to the process of the method for generating a financial credit granting scheme applied to a terminal device elaborated later.

[0065] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the step flow of the method for generating a financial credit granting scheme provided by the embodiments of the present application in some embodiments. It should be understood that although Figure 1The execution order of some method steps is shown, but based on different design requirements of actual applications, the method for generating a financial credit granting scheme provided by the embodiments of the present application can of course adopt an execution order different from that shown in the figure. That is, Figure 1 the order of the shown method steps does not constitute a limitation on the execution logic order of the method for generating a financial credit granting scheme provided by the embodiments of the present application. Any other reasonable changes based on Figure 1 the order of the shown method steps should be included within the protection scope of the method for generating a financial credit granting scheme provided by the embodiments of the present application.

[0066] For example Figure 1 as shown, in some embodiments, the method for generating a financial credit granting scheme provided by the embodiments of the present application may include but is not limited to steps S101 to S103 shown below.

[0067] Step S101: Obtain the credit business data of the target customer.

[0068] The terminal device can obtain the credit business data including parameters such as products, credit limits, and customer information by receiving a credit application and parsing the credit application. And the terminal device can determine the credit business data obtained by parsing the credit application initiated by the target customer as the credit business data of the target customer. Or, when it is clearly indicated in the credit application initiated by other customers received by the terminal device that it is for financial credit assessment of one or more target customers, the terminal device uses the credit business data obtained by parsing the credit application initiated by the other customers as the credit business data of the one or more target customers.

[0069] Exemplarily, in scenarios such as pre-rental credit granting scheme recommendation for customers by financial institutions such as banks, customer A actively initiates a credit application for its own financial credit assessment based on the human-computer interaction interface provided by the terminal device. After the terminal device receives the credit application initiated by customer A, it parses the credit application based on natural language processing (NLP) to obtain the credit business data including parameters such as products, credit limits, and customer information. And the terminal device uses this credit business data as the credit business data of customer A. In addition, if a businessperson of a financial institution such as a bank initiates a credit application for customer B based on the human-computer interaction interface provided by the terminal device, after the terminal device receives the credit application initiated by the staff, it parses the credit application based on natural language processing (NLP) to obtain the credit business data including parameters such as products, credit limits, and customer information. And the terminal device uses this credit business data as the credit business data of customer B.

[0070] In some embodiments, after receiving a credit application from a target customer, the terminal device may also communicate with the target customer in real time through a human-computer interaction interface to obtain information feedback by the target customer based on the communication inquiry, such as input voice information, text information, or uploaded file data, etc., and process the obtained information (such as performing information processing based on natural language processing NLP technology and / or information extraction technology, etc.) to obtain the credit business data of the target customer.

[0071] Step S102: Input the credit business data into a preset credit analysis model, and the credit analysis model performs funnel analysis calculation on the credit business data to obtain credit grade information.

[0072] After the terminal device obtains the credit business data of the target customer, it further calls a pre-designed and trained credit analysis model based on funnel analysis, so as to input the credit business data into the credit analysis model. The credit analysis model performs funnel analysis calculation on the credit business data and outputs the corresponding credit grade information. In this way, the terminal device can obtain the credit grade information of the target customer for financial credit assessment of the target customer.

[0073] It should be noted that in some other embodiments, the credit analysis model can also be a credit scheme recommendation model. The method for generating a financial credit scheme provided in the embodiments of the present application does not limit the specific name of the credit analysis model. As long as a certain model has the function of performing funnel analysis calculation on credit business data based on funnel analysis and outputting the corresponding credit grade information, it belongs to the credit analysis model provided in the embodiments of the present application, and thus should be included in the protection scope of the method for generating a financial credit scheme provided in the embodiments of the present application.

[0074] In addition, the credit analysis model is obtained by pre-training a model based on funnel analysis using credit business sample data. The specific training process of the credit analysis model will be introduced in detail in the embodiments proposed later in this article.

[0075] Step S103: Generate a financial credit scheme for the target customer based on the credit grade information.

[0076] After the terminal device obtains the credit grade information of the target customer output by the credit analysis model, it can directly generate a financial credit scheme for the target customer according to the credit grade information. For example, the terminal device calls a pre-configured and locally saved credit scheme template file, fills in the credit grade information of the target customer output by the credit analysis model at the corresponding position in the template file, so as to automatically generate a financial credit scheme for the target customer.

[0077] In some embodiments, after the terminal device obtains the credit-granting level information of the target customer output by the credit-granting analysis model, it can further screen the credit-granting level information to determine the final template credit file information, and then generate a financial credit-granting plan for the target customer according to the target credit-granting level information. For example, the credit-granting level information output by the credit-granting analysis model obtained by the terminal device includes the corresponding credit-granting level and the worst credit-granting level with a lower level. In this case, the terminal device can further combine other risk control information of the target customer (including credit rating information, etc.) to determine the corresponding credit-granting level as the target credit-granting level information, or determine the worst credit-granting level as the target credit-granting level information, and then automatically generate a financial credit-granting plan for the target customer by calling the credit-granting plan template file to fill in the credit-granting level information.

[0078] In the embodiments of the present application, for the steps S101 to S103 shown, the terminal device obtains credit-granting business data of the target customer including parameters such as products, credit limits, and customer information, and then calls a pre-designed and trained credit-granting analysis model based on funnel analysis. The credit-granting analysis model performs funnel analysis calculations on the credit-granting business data and outputs the corresponding credit-granting level information. Thus, the terminal device can automatically generate a financial credit-granting plan for the target customer according to the credit-granting level information.

[0079] In this way, compared with the method of manually comparing and screening tabular data to determine and generate a credit-granting plan, in scenarios such as pre-rental credit-granting plan recommendation for customers by financial institutions such as banks in the embodiments of the present application, through the credit-granting analysis model based on funnel analysis, funnel analysis calculations are performed on credit-granting business data including parameters such as products, credit limits, and customer information to determine the corresponding credit-granting level to generate a financial credit-granting plan for the customer, realizing automatic screening and recommendation of the credit-granting plan level for the customer based on funnel analysis, thereby improving the efficiency of processing credit-granting business for customers to generate a credit-granting plan.

[0080] In addition, in the embodiments of the present application, since the credit-granting business data can be automatically obtained by the terminal device and funnel analysis calculations are performed based on the credit-granting analysis model, it is thus possible to eliminate the need for business personnel to manually compare and screen tabular data and communicate with the target customer for a long time. In this way, compared with the traditional method of manually determining a credit-granting plan, the embodiments of the present application can also effectively reduce the communication cost between business personnel and customers when granting financial credit to customers.

[0081] In some embodiments, the credit-granting level information obtained by the terminal device calling the credit-granting analysis model to perform funnel analysis calculations on the credit-granting business data may include credit-granting level types, such as credit-granting classifications of type A, type B, type C, or type D, etc. Each credit-granting classification can be increased and configured as needed according to actual business requirements.

[0082] Please refer to Figure 2 , Figure 2 which is Figure 1 a schematic flow diagram of the refined steps of step S102 in

[0083] As Figure 2 shown, in some embodiments, the step of "performing funnel analysis calculation on the credit business data by the credit analysis model to obtain credit level information" in the above step S102 may include, but is not limited to, the following steps S201 and S202:

[0084] Step S201: Obtain the credit configuration parameters of the credit analysis model.

[0085] It should be noted that the credit configuration parameters include, but are not limited to, the maximum limits of various types of credit (such as credit level classifications such as Class A credit, Class B credit, Class C credit, or Class D credit), brand-level credit limits, whether to share limits, and product types, etc.

[0086] During the process of the terminal device invoking the credit analysis model to perform funnel analysis calculation on the credit business data of the target customer, it first obtains the credit configuration parameters of the credit analysis model.

[0087] In some embodiments, the terminal device can pre-design and save a configuration table for the credit analysis model. In this way, the terminal device can obtain the credit configuration parameters of the credit analysis model by reading the configuration table.

[0088] In some embodiments, the terminal device can also receive the model configuration operations actively performed by the business personnel through the human-computer interaction interface, and then generate the above-mentioned credit configuration parameters of the credit analysis model in response to the model configuration operations, or update the above-mentioned credit configuration parameters in response to the model configuration operations.

[0089] Based on this, the method for generating a financial credit scheme provided by the embodiments of the present application may further include the following steps:

[0090] Obtain model configuration data;

[0091] Update the credit configuration parameters of the credit analysis model based on the model configuration data to obtain new credit configuration parameters.

[0092] The terminal device receives the model configuration data selected by the business personnel actively performing the model configuration operation through the human-computer interaction interface provided for business personnel of financial institutions such as banks. Then, the terminal device can update the credit configuration parameters of the previously saved credit analysis model based on the received model configuration data, thereby obtaining new credit configuration parameters.

[0093] It should be noted that the credit analysis model can increase or decrease relevant configuration parameters based on business needs to support rapid iteration. At the same time, the credit analysis model can also support rapid deployment and provide a standard call method for convenient access. In addition, the background of the terminal device also supports querying call logs, analyzing and statistics of call results, and analyzing the daily operation of the model, etc., to support multi-dimensional monitoring of the model operation.

[0094] In this case, the above step S201 may include the following steps:

[0095] Determine the new credit configuration parameter as the credit configuration parameter of the credit analysis model.

[0096] After the terminal device updates the credit configuration parameter of the credit analysis model to obtain a new credit configuration parameter, in the subsequent process of calling the credit analysis model to perform funnel analysis calculation on the credit business data of the target customer, that is, obtain the new credit configuration parameter as the credit configuration parameter of the credit analysis model.

[0097] Exemplarily, business personnel of financial institutions such as banks can configure or adjust the configuration of the credit analysis model in real time according to the differences in products and amounts in the actual use process of credit business. For example, business personnel can use the configuration page provided by the terminal device, such as Figure 3 shown in the configuration page, to adjust the maximum limit of various credit grades in real time according to the development of the business, adjust the shared quota information of different brands in real time, and / or adjust different brand information in real time according to the business development. After the business personnel complete the operation on the configuration page, the terminal device generates a configuration table for the credit analysis model based on the data information selected by the business personnel on the configuration page. In this way, the terminal device can obtain the credit configuration parameter of the credit analysis model or the new credit configuration parameter after updating by reading the configuration table.

[0098] Step S202: Perform funnel analysis calculation on the credit business data based on the credit configuration parameter through the credit analysis model to determine the credit grade type.

[0099] After the terminal device obtains the credit configuration parameter of the credit analysis model, it further calls the credit analysis model to gradually perform funnel analysis calculation on the credit business data of the target customer based on various information such as the maximum credit limit, brand level credit limit, whether to share the quota, and product type in the credit configuration parameter, so as to determine the corresponding credit grade type.

[0100] In this embodiment, the illustrated steps S201 and S202 obtain the credit configuration parameters of the credit analysis model through the terminal device: various credit maximum limits, brand level credit limits, whether to share limits, product types, etc. Then, based on these credit configuration parameters, the credit analysis model performs a funnel analysis calculation on the credit business data to determine the corresponding credit grade types. In this way, it is no longer necessary for business personnel to manually compare tabular data, consuming a large amount of time and human resources, improving the efficiency of generating credit prevention for customer credit assessment and reducing the cost of repeated communication between business personnel and customers.

[0101] In addition, this embodiment also obtains model configuration data through the terminal device and updates the credit configuration parameters of the credit analysis model based on the model configuration data to obtain new credit configuration parameters. In this way, the terminal device can call the credit analysis model and perform a funnel analysis calculation on the credit business data using the new credit configuration parameters. That is to say, by real-time monitoring data changes, business personnel can quickly adjust the configuration of the credit analysis model using the configuration page, and can timely respond to market changes and changes in user needs to conduct credit assessment and analysis of customers, thus ensuring the timeliness and effectiveness of financial risk management by financial institutions such as banks based on the final financial credit plan of target customers.

[0102] In some embodiments, the credit business data includes customer data and credit target data. Customer data includes customer information such as name, mobile phone number, ID number, residential address, and company name. The credit target data includes information related to the credit application initiated by the target customer, such as brand name and the amount of this financing.

[0103] Please refer to Figure 4 , Figure 4 For Figure 2 the detailed step flow diagram of step S202 in

[0104] As Figure 4 shown, in some embodiments, the above step S202 may include but is not limited to the following steps S401 and S402:

[0105] Step S401: Analyze the customer data through the credit analysis model to obtain the customer information analysis result corresponding to the customer data.

[0106] It should be noted that the customer information analysis result is the parameters such as whether it is a new customer, historical stock exposure, and whether to reorder obtained by the credit analysis model based on the above customer information.

[0107] When the terminal device calls the credit analysis model to perform funnel analysis calculation on the credit business data based on the credit configuration parameters, the credit analysis model first analyzes the customer data in the credit business data to obtain the customer information analysis result corresponding to the customer data.

[0108] Step S402: Perform funnel analysis calculation on the credit target data based on the customer information analysis result and the credit configuration parameters through the credit analysis model.

[0109] After obtaining the customer information analysis result, the credit analysis model called by the terminal device further performs funnel analysis calculation on the credit target data in the credit business data step by step based on the customer information analysis result and the credit configuration parameters.

[0110] It should be noted that based on different customer information analysis results, the credit analysis model will use different judgment logic links to compare the credit configuration parameters and the credit target data, so as to perform funnel analysis calculation on the credit target data step by step.

[0111] In this embodiment, steps S401 and S402 shown analyze the customer data in the credit business data through the terminal device to obtain the customer information analysis result corresponding to the customer data, and then use different judgment logic links to compare the credit configuration parameters and the credit target data to perform funnel analysis calculation on the credit target data step by step. In this way, the terminal device can call the credit analysis model to perform funnel analysis calculation on the credit business data based on the credit configuration parameters, so as to determine the corresponding credit level information, and then the terminal device can automatically generate the financial credit plan for the target customer, improving the credit efficiency.

[0112] In some embodiments, the above-mentioned credit target data may include product data and financing financial data. Among them, the product data such as brand names (such as Joyoung, Xiaomi, etc.), and the financing financial data such as the current financing amount in the credit application initiated by the customer.

[0113] Please refer to Figure 5 , Figure 5 For Figure 4 the detailed step flow schematic diagram of step S402 in

[0114] As Figure 5 shown, in some embodiments, the above-mentioned step S402 may include but is not limited to steps S501 to S504 shown below.

[0115] Step S501: Determine the customer type of the target customer indicated by the customer information analysis result, and the customer type is a new customer or a historical customer.

[0116] When the terminal device performs a funnel analysis calculation on the credit target data step by step based on the customer information analysis result and the credit configuration parameters through the credit analysis model, it first determines the customer type of the target customer indicated by the customer information analysis result, that is, whether the customer information analysis result indicates that the target customer is a new customer or a historical customer.

[0117] Step S502: Invoke the target analysis logic applicable to the customer type through the credit analysis model.

[0118] After determining whether the customer information analysis result indicates that the target customer is a new customer or a historical customer, the terminal device invokes the target analysis logic applicable to the customer type through the credit analysis model. For example, when the customer information analysis result indicates that the target customer is a new customer, the first analysis logic applicable to new customers is invoked through the credit analysis model as the target analysis logic. Another example is that when the customer information analysis result indicates that the target customer is a historical customer, the second analysis logic applicable to historical customers is invoked through the credit analysis model as the target analysis logic.

[0119] It should be noted that the first analysis logic can be to judge according to the brand input by the target customer: whether sharing of the quota is not allowed, or the sharing quota is low; if sharing is not allowed, skip the sharing quota calculation; if the sharing quota is low, use the lower quota for calculation. The second analysis logic can be to judge according to the brand input by the target customer: whether sharing of the quota is allowed, or the sharing quota is high.

[0120] Step S503: Analyze the product data through the credit analysis model according to the target analysis logic and the credit configuration information to obtain the credit quota analysis result of the brand corresponding to the product data.

[0121] After the terminal device invokes the second analysis logic applicable to historical customers as the target analysis logic through the credit analysis model, it can further analyze the product data through the credit analysis model according to the target analysis logic and the credit configuration information, that is, judge the brand input by the target customer in the product data, so as to obtain the credit quota analysis result of the brand corresponding to the product data.

[0122] In some embodiments, step S503 may include but is not limited to the following steps:

[0123] Analyze the product data through the credit analysis model according to the target analysis logic and the target credit configuration information in the credit configuration information to obtain the sharing quota status information of the brand corresponding to the product data; wherein, the target credit configuration information is the credit quota configuration information of the brand corresponding to the product data;

[0124] Based on the shared quota status information, the credit limit calculation is performed through the credit analysis model to obtain the credit limit analysis result of the brand corresponding to the product data.

[0125] It should be noted that the credit limit configuration information can be whether the brand does not allow sharing of quotas, or the shared quota is relatively low, or whether the brand allows sharing of quotas, or the shared quota is relatively high. The shared quota status information is determined as "does not allow sharing of quotas, or the shared quota is relatively low", or "allows sharing of quotas, or the shared quota is relatively high" based on the credit limit configuration information.

[0126] When the terminal device analyzes the product data according to the target analysis logic and the credit configuration information through the credit analysis model, it can first determine the credit limit configuration information of all brands corresponding to the product data from the credit configuration information, and then use this credit limit configuration information as the target credit configuration information, so as to analyze the product data according to the target analysis logic and the target credit configuration information, thereby determining the shared quota status information of the brand corresponding to the product data. After that, the terminal device further performs credit limit calculation through the credit analysis model based on the shared quota status information, so as to obtain the credit limit analysis result of the brand corresponding to the product data.

[0127] Exemplarily, in the case where the customer information analysis result indicates that the target customer is a new customer, the terminal device first determines whether the brand input by the target customer in the product data does not allow sharing of quotas, or the shared quota is relatively low, based on the credit limit configuration information through the credit analysis model, to obtain the corresponding shared quota status information. And in the case where the customer information analysis result indicates that the target customer is a historical customer, the terminal device first determines whether the brand input by the target customer in the product data allows sharing of quotas, or the shared quota is relatively high, based on the credit limit configuration information through the credit analysis model, to obtain the corresponding shared quota status information.

[0128] After that, the terminal device further maps the brand level according to the brand input by the target customer in the product data through the credit analysis model, and then finds the corresponding credit limit according to the level. And based on the shared quota status information of the brand and whether there is a repeat order (checking whether the number of repeat orders exceeds the limit and generating a quota impact coefficient according to the number of repeat orders), the final credit limit of the brand is obtained according to a certain formula, so as to use the final credit limit as the credit limit analysis result. Among them, in the case where the target customer is a new customer, if it is determined that the brand input by the target customer in the product data does not allow sharing, the shared quota calculation is skipped; if the shared quota is relatively low, the lower quota is used for calculation.

[0129] Step S504: Perform risk exposure calculation through the credit analysis model based on the credit limit analysis result, the customer data, and the financing financial data.

[0130] After the terminal device obtains the credit limit analysis result of the brand corresponding to the product data, it further calculates the risk exposure through the credit analysis model based on the credit limit analysis result (the final brand credit limit), customer data (customer information), and financing financial data (the amount of this financing applied by the customer, also known as the credit limit). In this way, the terminal device can obtain the optimal and lowest credit level types by mapping the credit level according to the calculated risk exposure through the credit analysis model, and then output the credit level information.

[0131] In this embodiment, for steps S501 and S504 shown, the terminal device calls the credit analysis model to perform funnel analysis calculation using the analysis logic link applicable to different customer types, so as to determine the corresponding credit level information. After that, the terminal device can automatically generate a financial credit plan for the target customer according to the credit level information. Compared with the traditional method of manually comparing and screening tabular data to generate a customer credit plan, the credit efficiency is effectively improved and the communication cost is reduced.

[0132] In some embodiments, before the method for generating a financial credit plan provided in the embodiments of the present application actually performs a financial credit assessment on the target customer to generate a credit plan, the credit analysis model is also pre-trained, and the credit analysis model is used to perform funnel analysis calculation on the credit business data of the target customer.

[0133] Please refer to Figure 6 , Figure 6 , which is a schematic diagram of the step flow of the method for generating a financial credit plan provided in the embodiments of the present application in some other embodiments.

[0134] As Figure 6 shown, in some embodiments, before step S102 above: inputting the credit business data into a preset credit analysis model, and the credit analysis model performs funnel analysis calculation on the credit business data to obtain credit level information, the method for generating a financial credit plan provided in the embodiments of the present application may further include, but is not limited to, steps S601 and S602 shown below.

[0135] Step S601: Obtain credit business sample data.

[0136] It should be noted that the credit business sample data may include multiple groups of information, such as: brand name, financing amount, and customer information, etc. In addition, each business sample in the credit business sample data may be pre-added with corresponding tag data, and the tag data is at least used to indicate the credit level type of the business sample.

[0137] The terminal device can receive the credit business sample data that has been pre-organized by the business personnel for training the credit analysis model based on funnel analysis through the human-computer interaction interface provided for the business personnel of financial institutions such as banks.

[0138] In some embodiments, the terminal device can also perform data interaction with the financial platform / system of financial institutions such as banks, so as to automatically crawl the data related to credit business from the financial platform / system, and organize the crawled data according to the data organization method configured by the business personnel to generate the credit business sample data for training the credit analysis model.

[0139] Step S602: Perform model training of funnel analysis calculation based on the business sample data and the preset model configuration data to obtain the credit analysis model.

[0140] After the terminal device obtains the credit business sample data, it takes the credit business sample data as the model input parameter, and sets the model output parameter, such as credit rating information such as brand name, corresponding credit rating, and / or worst credit rating, etc. Then, according to the preset model configuration data (such as various model configuration information selected by the business personnel based on the above configuration page), perform model training based on funnel analysis calculation, so as to train and obtain the credit analysis model.

[0141] In this embodiment, steps S601 and S602 shown obtain the credit business sample data through the terminal device, and then perform model training of funnel analysis calculation based on the business sample data and the model configuration data to obtain the credit analysis model. Thus, when performing financial credit assessment on the target customer subsequently, the terminal device can input the credit business data into the credit analysis model, so that the credit analysis model performs funnel analysis calculation on the credit business data to obtain the corresponding credit rating information, and finally generate the financial credit plan for the target customer based on the credit rating information output by the model. In this way, it realizes the automatic screening and recommendation of the credit plan level for customers based on funnel analysis, thereby improving the efficiency of generating the credit plan for handling credit business for customers.

[0142] Next, a complete embodiment of the method for generating a financial credit plan provided by the embodiments of the present application is presented.

[0143] Please refer to Figure 7 , Figure 7 , which is a schematic diagram of the working process of the credit analysis model involved in the method for generating a financial credit plan provided by the embodiments of the present application in some embodiments.

[0144] Such as Figure 7As shown, in the application scenarios where the terminal device recommends the optimal credit granting scheme for customers and risk control managers based on information such as the product type, expected amount, and credit granting type required by the customer during product selection before renting through the credit granting analysis model, the terminal device uses multiple sets of information: brand name, the current financing amount, and customer information as the model input parameters, and then performs the following calculation process through the credit granting analysis model:

[0145] Obtain parameters based on customer information: whether it is a new customer, historical stock exposure, and whether it is a repeat order.

[0146] Read the configuration table to obtain parameters: the maximum limit for various types of credit granting, the credit granting amount for brand levels, whether to share the quota, and the product type.

[0147] Among them, the credit granting analysis model specifically performs funnel analysis calculations according to the following steps a to e:

[0148] Step a: According to the incoming customer information, determine whether it is a repeat order (a repeat credit application for an existing customer) and whether it is a new customer.

[0149] Step b: If it is a new customer: Based on the input brand, determine whether sharing the quota is not allowed or the shared quota is relatively low. If sharing the quota is not allowed, skip the shared quota calculation. If the shared quota is relatively low, use the lower quota for calculation.

[0150] Step c: If it is an existing customer: Based on the input brand, determine whether sharing the quota is allowed or the shared quota is relatively high.

[0151] Step d: Map the brand level according to the input brand, and then find the corresponding credit granting amount according to the level. And based on the results of the second and third steps and whether it is a repeat order (check whether the number of repeat orders exceeds the limit and generate a quota impact coefficient according to the number of repeat orders), obtain the final credit granting amount for the brand according to a certain formula.

[0152] Step e: According to the customer's applied credit granting amount, customer information, and the final credit granting amount of the brand, calculate and generate the final risk exposure, map the credit granting grades to obtain the optimal and minimum credit granting grades, and give a recommendation.

[0153] Please refer to Figure 8 , The embodiment of the present application also provides a device for generating a financial credit granting scheme, which can implement the above method for generating a financial credit granting scheme. The device includes:

[0154] A data acquisition module, configured to acquire the credit granting business data of the target customer;

[0155] A funnel analysis module, configured to input the credit granting business data into a preset credit granting analysis model, and the credit granting analysis model performs funnel analysis calculations on the credit granting business data to obtain credit granting grade information;

[0156] A solution generation module, configured to generate a financial credit solution for the target customer based on the credit rating information.

[0157] In some embodiments, the credit rating information includes a credit rating type; the funnel analysis module is further configured to obtain credit configuration parameters of the credit analysis model; and perform funnel analysis calculation on the credit business data based on the credit configuration parameters through the credit analysis model to determine the credit rating type.

[0158] In some embodiments, the credit business data includes customer data and credit target data; the funnel analysis module is further configured to analyze the customer data through the credit analysis model to obtain a customer information analysis result corresponding to the customer data; and perform funnel analysis calculation on the credit target data based on the customer information analysis result and the credit configuration parameters through the credit analysis model.

[0159] In some embodiments, the credit target data includes product data and financing financial data; the funnel analysis module is further configured to determine the customer type of the target customer indicated by the customer information analysis result, where the customer type is a new customer or a historical customer; call a target analysis logic applicable to the customer type through the credit analysis model; analyze the product data according to the target analysis logic and the credit configuration information through the credit analysis model to obtain a credit limit analysis result of the brand corresponding to the product data; and perform a risk exposure calculation based on the credit limit analysis result, the customer data, and the financing financial data through the credit analysis model.

[0160] In some embodiments, the funnel analysis module is further configured to analyze the product data according to the target analysis logic and the target credit configuration information in the credit configuration information through the credit analysis model to obtain shared limit status information of the brand corresponding to the product data; where the target credit configuration information is the credit limit configuration information of the brand corresponding to the product data; and perform a credit limit calculation based on the shared limit status information through the credit analysis model to obtain a credit limit analysis result of the brand corresponding to the product data.

[0161] In some embodiments, the data acquisition module is further configured to obtain model configuration data; update the credit configuration parameters of the credit analysis model based on the model configuration data to obtain new credit configuration parameters; and determine the new credit configuration parameters as the credit configuration parameters of the credit analysis model.

[0162] In some embodiments, the apparatus for generating a financial credit solution provided in the embodiments of the present application further includes:

[0163] A model training module, configured to obtain sample data of credit-granting services; and perform model training for funnel analysis calculation based on the service sample data and preset model configuration data to obtain a credit-granting analysis model.

[0164] It should be noted that the specific implementation manners of the financial credit-granting scheme generation device provided in the embodiments of the present application are basically the same as the specific embodiments of the above-mentioned financial credit-granting scheme generation method, and will not be elaborated herein.

[0165] The embodiments of the present application further provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above-mentioned financial credit-granting scheme generation method is implemented. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0166] Please refer to Figure 9 , Figure 9 which schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0167] A processor 901, which can be implemented by using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0168] A memory 902, which can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 902 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 902, and the processor 901 is used to call and execute the financial credit-granting scheme generation method of the embodiments of the present application;

[0169] An input / output interface 903, configured to implement information input and output;

[0170] A communication interface 904, configured to implement communication interaction between this device and other devices, and can implement communication through a wired manner (such as USB, network cable, etc.) or through a wireless manner (such as a mobile network, WIFI, Bluetooth, etc.);

[0171] A bus 905 transmits information between various components of the device (such as a processor 901, a memory 902, an input / output interface 903, and a communication interface 904);

[0172] Among them, the processor 901, the memory 902, the input / output interface 903, and the communication interface 904 achieve communication connections with each other inside the device through the bus 905.

[0173] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the generation method of the above-mentioned financial credit-granting scheme.

[0174] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0175] The generation method of the financial credit-granting scheme, the generation device of the financial credit-granting scheme, the electronic device, and the computer-readable storage medium provided by the embodiment of the present application first obtain the credit-granting business data of the target customer to be evaluated for financial credit-granting, and then input the credit-granting business data into a pre-designed and trained credit-granting analysis model. Thus, the credit-granting analysis model performs funnel analysis calculation on the credit-granting business data to obtain the corresponding credit-granting level information, and finally generates the financial credit-granting scheme of the target customer based on the credit-granting level information output by the model. In this way, it realizes the automatic screening and recommendation of the credit-granting scheme level for customers based on funnel analysis, thereby improving the efficiency of generating the credit-granting scheme for handling the credit-granting business for customers.

[0176] The embodiments described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0177] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown, or combine certain steps, or different steps.

[0178] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0180] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of this application and the above-mentioned drawings are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0181] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or a similar expression means any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0182] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0183] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, in each embodiment of the present application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0185] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM for short), random access memories (RAM for short), magnetic disks or optical discs that can store programs.

[0186] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of rights of the embodiments of the present application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of rights of the embodiments of the present application.

Claims

1. A method for generating a financial credit scheme, characterized in that: The method comprises: Obtain credit business data of target customers; Inputting the credit business data into a preset credit analysis model, and having the credit analysis model perform funnel analysis calculation on the credit business data to obtain credit level information; A financial credit plan for the target customer is generated based on the credit level information.

2. The method according to claim 1, characterized in that The credit level information includes the credit level type; the credit analysis model performs funnel analysis calculation on the credit business data to obtain the credit level information, including: Obtaining credit configuration parameters of the credit analysis model; The credit analysis model performs funnel analysis calculation on the credit business data based on the credit configuration parameters to determine the credit level type.

3. The method according to claim 2, characterized in that The credit business data includes customer data and credit target data; the credit analysis model is used to perform funnel analysis calculation on the credit business data based on the credit configuration parameters, including: Analyze the customer data through the credit analysis model to obtain customer information analysis results corresponding to the customer data; The credit analysis model performs funnel analysis calculation on the credit target data based on the customer information analysis results and the credit configuration parameters.

4. The method according to claim 3, characterized in that The credit target data includes product data and financing financial data; the credit analysis model performs funnel analysis calculation on the credit target data based on the customer information analysis result and the credit configuration parameters, including: Determine the customer type of the target customer indicated by the customer information analysis result, the customer type being a new customer or a historical customer; Calling the target analysis logic suitable for the customer type through the credit analysis model; Analyze the product data according to the target analysis logic and the credit configuration information through the credit analysis model to obtain a credit limit analysis result of the brand corresponding to the product data; The credit analysis model is used to calculate risk exposure based on the credit line analysis results, the customer data and the financing financial data.

5. The method according to claim 4, characterized in that The step of analyzing the product data according to the target analysis logic and the credit configuration information by the credit analysis model to obtain a credit limit analysis result of the brand corresponding to the product data includes: The product data is analyzed by the credit analysis model according to the target analysis logic and the target credit configuration information in the credit configuration information to obtain the shared credit limit status information of the brand corresponding to the product data; wherein the target credit configuration information is the credit limit configuration information of the brand corresponding to the product data; The credit limit is calculated based on the shared credit limit status information through the credit analysis model to obtain a credit limit analysis result of the brand corresponding to the product data.

6. The method according to claim 2, characterized in that The method further comprises: Get model configuration data; Based on the model configuration data, the credit configuration parameters of the credit analysis model are updated to obtain new credit configuration parameters; The step of obtaining the credit configuration parameters of the credit analysis model includes: The new credit configuration parameters are determined as the credit configuration parameters of the credit analysis model.

7. The method according to any one of claims 1 to 6, characterized in that: Before inputting the credit business data into a preset credit analysis model, the method further includes: Obtain credit business sample data; Model training for funnel analysis calculation is performed based on the business sample data and preset model configuration data to obtain a credit analysis model.

8. A device for generating a financial credit scheme, characterized in that: The device comprises: Data acquisition module, used to obtain credit business data of target customers; A funnel analysis module, used for inputting the credit business data into a preset credit analysis model, and the credit analysis model performs a funnel analysis calculation on the credit business data to obtain credit level information; A scheme generating module is used to generate a financial credit scheme for the target customer based on the credit level information.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method for generating a financial credit plan according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for generating a financial credit scheme according to any one of claims 1 to 7 is implemented.

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