Configuration file generation method and device, amount adjustment method and device, equipment, medium and product

Abstract

The invention provides a configuration file generation method and device, an amount adjustment method and device, equipment, a medium and a product, relates to the technical field of big data, and particularly can be applied to the field of financial science and technology. The method comprises the steps of obtaining authorization of a user for obtaining and using target information, inputting the target information of the user into a trained scoring model for processing after the authorization of the user is obtained, and outputting risk information of the user; the target information is input into a trained regression model to be processed, first index information is output, and the first index information represents the consumption potential of the user; based on the risk information, the first index information and the second index information, a configuration file is generated, the configuration file is used for packaging quota adjustment execution logic based on the risk information, the first index information and the second index information, and the configuration file is used for being called to execute quota adjustment business; the second index information represents the historical consumption condition of the user.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and can be specifically applied to the field of financial technology. More specifically, it relates to a configuration file generation method for adjusting credit limits, a credit limit adjustment method, an apparatus, equipment, a medium, and a program product. Background Art

[0002] As the credit business transitions from rapid growth to a competitive landscape for existing users, user credit limits are crucial to user experience. Scientifically managing credit limits for valuable existing users while balancing risk and revenue helps improve user experience and is a core competitive advantage for financial institutions in maximizing profitability. Current solutions for dynamic credit card limit adjustments suffer from low intelligence, poor user compatibility, and poor interpretability. Summary of the Invention

[0003] In view of the above problems, the present application provides a configuration file generation method, adjustment method, device, equipment, medium and program product for adjustment that improve the intelligence, matching degree and interpretability.

[0004] According to the first aspect of the present application, a method for generating a configuration file for credit limit adjustment is provided, the method comprising: obtaining the user's authorization to obtain and use target information, and after obtaining the user's authorization, inputting the user's target information into a trained scoring model for processing, and outputting the user's risk information; inputting the target information into a trained regression model for processing, and outputting first indicator information, the first indicator information representing the user's consumption potential; generating a configuration file based on the risk information, the first indicator information, and the second indicator information, the configuration file being used to encapsulate the credit limit execution logic based on the risk information, the first indicator information, and the second indicator information, and the configuration file being used to be called to execute the credit limit adjustment business; the second indicator information represents the user's historical consumption.

[0005] According to an embodiment of the present application, the method also includes: performing feature engineering processing on the stock information within a predetermined time period to obtain a first feature; constructing a first label, the first label characterizing the risk level of the user associated with the stock information; and using the first feature and the first label to train a scoring model to obtain a trained scoring model.

[0006] According to an embodiment of the present application, the method also includes: performing feature engineering processing on the inventory information within a predetermined time period to obtain a second feature, which is the same as or different from the first feature; constructing a second label, which characterizes the consumption capacity of users associated with the inventory information; and using the second feature and the second label to train a regression model to obtain a trained regression model.

[0007] According to an embodiment of the present application, a configuration file is generated based on risk information, first indicator information and second indicator information, including: defining trigger conditions based on risk information, first indicator information and second indicator information; defining an adjustment operation to be performed when the trigger conditions are met; defining global variables, which are used to record log information generated by the adjustment operation; and encapsulating the trigger conditions, adjustment operations and global variables to obtain a configuration file.

[0008] According to an embodiment of the present application, the method further includes: loading a configuration file into memory so that the rule engine uses multi-threading to perform quota adjustment operations on multiple users in parallel based on the configuration file.

[0009] According to a second aspect of the present application, a quota adjustment method is provided, comprising: in response to a quota adjustment instruction, processing information of a target user based on a configuration file to obtain a quota adjustment processing result; wherein the configuration file is generated based on the configuration file generation method of the present application.

[0010] According to an embodiment of the present application, the target user's information is processed based on the configuration file to obtain the quota adjustment processing result, including: obtaining the real-time configuration file from the memory; calling the rule engine using multi-threading, and performing quota adjustment operations on multiple users in parallel based on the configuration file to obtain the quota adjustment processing result for each user.

[0011] According to the third aspect of the present application, a configuration file generation device for credit limit adjustment is provided, and the method includes: an acquisition module for obtaining the user's authorization to obtain and use target information; a first processing module for inputting the user's target information into a trained scoring model for processing after obtaining the user's authorization, and outputting the user's risk information; a second processing module for inputting the target information into a trained regression model for processing, and outputting first indicator information, wherein the first indicator information represents the user's consumption potential; a generation module for generating a configuration file based on the risk information, the first indicator information and the second indicator information, wherein the configuration file is used to encapsulate the credit limit execution logic based on the risk information, the first indicator information and the second indicator information, and the configuration file is used to be called to execute the credit limit adjustment business; the second indicator information represents the user's historical consumption.

[0012] According to a fourth aspect of the present application, a quota adjustment device is provided, comprising: a response module for obtaining a configuration file in response to a quota adjustment instruction; a quota adjustment module for processing information of a target user based on the configuration file to obtain a quota adjustment result; wherein the configuration file is generated based on the configuration file generation method of the present application or based on the configuration file generation device.

[0013] The fifth aspect of the present application provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0014] The sixth aspect of the present application further provides a computer-readable storage medium having a computer program or instruction stored thereon, which implements the steps of the above method when the above computer program or instruction is executed by a processor.

[0015] The seventh aspect of the present application further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above contents and other objects, features and advantages of the present application will become more apparent through the following description of the embodiments of the present application with reference to the accompanying drawings, in which:

[0017] Figure 1 This is a schematic diagram of an exemplary system architecture of a configuration file generation method, a quota adjustment method, and an apparatus that can be applied to quota adjustment according to an embodiment of the present application;

[0018] Figure 2 A flowchart of a method for generating a configuration file for adjusting a credit limit according to an embodiment of the present application is schematically shown;

[0019] Figure 3 A flowchart of a credit limit adjustment method according to an embodiment of the present application is schematically shown;

[0020] Figure 4 The following schematically shows a structural block diagram of a configuration file generating device for adjusting quota according to an embodiment of the present application;

[0021] Figure 5 A schematic diagram of a structure of a credit limit adjustment device according to an embodiment of the present application is shown; and

[0022] Figure 6 A block diagram of an electronic device suitable for implementing a method for generating a configuration file for quota adjustment according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0023] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present application. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present application.

[0024] The terms used herein are only for describing specific embodiments and are not intended to limit this application. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0026] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0027] In the technical solution of this application, the target information involved (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0028] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided in the embodiments of the present application all provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0029] There are currently two main types of solutions for dynamically adjusting credit card limits: one is to add historical credit card consumption behavior characteristics to the original credit card credit limit scoring card, and update the scoring card to obtain a new credit limit; the other is to determine the credit limit increase based on historical consumption limit occupancy analysis and statistics. The first type of solution is to directly characterize the correlation between the latest user portrait and the credit limit, which is equivalent to establishing a new credit limit calculation framework parallel to the original credit limit. The new credit limit has no direct correlation with the current credit limit, and the business explainability of the adjustment amount is not strong. The second type of solution only adjusts the credit limit based on the current credit limit occupancy ratio, and does not consider user risks or user consumption potential, resulting in poor user matching. In addition, it is necessary to characterize the correlation between the latest user portrait and the credit limit, and repeatedly establish a new credit limit calculation framework, which consumes resources and is costly.

[0030] In view of this, an embodiment of the present application provides a method for generating a configuration file for credit limit adjustment, including: obtaining the user's authorization to obtain and use target information, and after obtaining the user's authorization, inputting the user's target information into a target scoring model for processing, and outputting the user's risk information; inputting the target information into a target regression model for processing, and outputting first indicator information, the first indicator information representing the user's consumption potential; generating a configuration file based on the risk information, the first indicator information, and the second indicator information, the configuration file being used to encapsulate the credit limit execution logic based on the risk information, the first indicator information, and the second indicator information, and the configuration file being used to be called to execute the credit limit adjustment business; the second indicator information representing the user's historical consumption situation. The following is an introduction in conjunction with the embodiments.

[0031] Figure 1 This is an exemplary system architecture diagram of a configuration file generation method, a quota adjustment method, and an apparatus that can be applied to quota adjustment according to an embodiment of the present application. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present application can be applied, to help those skilled in the art understand the technical content of the present application, but does not mean that the embodiments of the present application cannot be used in other devices, systems, environments or scenarios.

[0032] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0033] Terminal devices 101, 102, and 103 may be various electronic devices with display screens that support users initiating credit limit adjustment requests, viewing credit limit adjustment results, triggering configuration file generation, etc., including but not limited to smartphones, tablet computers, laptop computers, and desktop computers. Users can input operations on the display screens of terminal devices 101, 102, and 103 and interact with server 105 via network 104 to receive or transmit data or information.

[0034] Server 105 may be a server that provides various services, such as a background management server (for example only) that supports operations performed by users on terminal devices 101, 102, and 103. The background management server may generate a user-matched profile for credit limit adjustment based on information entered by the user on terminal devices 101, 102, and 103. When a credit limit adjustment operation is triggered, the user's credit limit is adjusted based on the profile and the result of the credit limit adjustment is sent to the display screens of terminal devices 101, 102, and 103 for display.

[0035] It should be noted that the configuration file generation method and the quota adjustment method for adjusting the quota provided in the embodiments of the present application can generally be executed by the server 105. Accordingly, the configuration file generation device and the quota adjustment device provided in the embodiments of the present application can generally be set in the server 105. The configuration file generation method and the quota adjustment method for adjusting the quota provided in the embodiments of the present application can also be executed by the terminal devices 101, 102, and 103. Accordingly, the configuration file generation device and the quota adjustment device provided in the embodiments of the present application can also be set in the terminal devices 101, 102, and 103. The configuration file generation method and the quota adjustment method for adjusting the quota provided in the embodiments of the present application can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, and 103 and / or the server 105. Accordingly, the configuration file generation device and the quota adjustment device provided in the embodiments of the present application can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, and 103 and / or the server 105.

[0036] I understand. Figure 1The number and types of terminal devices, networks, and servers in the embodiment are merely illustrative. Any number of terminal devices, networks, and servers may be provided as required.

[0037] It is understood that the system architecture of the present application is described above, and the method of the present application will be described below. It is also understood that the sequence numbers of the various operations in the following method are merely used to indicate the operation for the purpose of description and should not be regarded as indicating the order in which the various operations must be performed. Unless explicitly stated, the method does not need to be executed in the exact order shown.

[0038] It should be noted that the configuration file generation method and device for credit limit adjustment in the embodiment of the present application can be applied to the field of financial technology. As the credit business gradually enters the stage of stock competition from the high-speed development stage, the size of the user's credit limit is related to the user experience. How to carry out scientific credit limit management for existing value users while taking into account risks and income will help improve the user experience and is also the core competitiveness of whether financial institutions can maximize profits. In order to improve the intelligence, matching and interpretability of credit limit adjustment and reduce resource consumption and costs, the configuration file generation method and device for credit limit adjustment provided in the embodiment of the present application can be adopted to better meet the needs of financial services.

[0039] It should be noted that the configuration file generation method and device for credit limit adjustment in the embodiment of the present application can be used in scenarios such as software development and testing in the financial field, and can also be used in any field other than the financial field, such as e-commerce, logistics, etc. The application field of the configuration file generation method and device for credit limit adjustment in the present application is not limited.

[0040] The following will be based on Figure 1 The scene described by Figure 2 A method for generating a configuration file for quota adjustment according to an embodiment of the present application is described in detail.

[0041] Figure 2 The flowchart of the method for generating a configuration file for quota adjustment according to an embodiment of the present application is schematically shown.

[0042] like Figure 2 As shown, the method for generating a configuration file for quota adjustment in this embodiment may include operations S210 to S240.

[0043] In operation S210 , the user's authorization to obtain and use target information is obtained.

[0044] In operation S220 , after obtaining the user's authorization, the user's target information is input into the target scoring model for processing, and the user's risk information is output.

[0045] In operation S230 , the target information is input into a target regression model for processing, and first indicator information is output.

[0046] In operation S240 , a configuration file is generated based on the risk information, the first indicator information, and the second indicator information.

[0047] In an embodiment of the present application, before obtaining the user's target information, the user's consent or authorization may be obtained. For example, before operation S220, a request to obtain the target information may be issued to the user. If the user agrees or authorizes the acquisition of the target information, operation S220 is performed.

[0048] In an embodiment of the present application, a corresponding operation entry can be provided for the user to choose to agree or reject the automated decision result. That is, before the target information is subjected to risk information assessment processing / decision-making using the target scoring model, an instruction to agree or reject the processing / decision-making process can be obtained from the user through the corresponding operation entry. If the user agrees to the processing / decision-making process, the risk information assessment processing / decision-making process is performed on the target information, that is, step S230 is executed. If the user rejects the processing / decision-making process, the expert decision-making process is entered.

[0049] In an embodiment of the present application, a corresponding operation entry can be provided for the user to choose to agree or reject the automated decision result. That is, before the target information is evaluated for consumption potential using the target scoring model / decision-making process, an instruction to agree or reject the processing / decision-making process can be obtained from the user through the corresponding operation entry. If the user agrees to the processing / decision-making process, the target information is evaluated for consumption potential / decision-making process, i.e., step S230 is executed. If the user refuses to undergo the processing / decision-making process, the expert decision-making process is entered.

[0050] In an embodiment of the present application, a configuration file is used to encapsulate credit limit adjustment execution logic based on risk information, first indicator information, and second indicator information. The configuration file is called to execute credit limit adjustment services. The first indicator information represents a user's spending potential and the corresponding credit limit satisfaction. The second indicator information represents a user's historical spending and the corresponding credit limit satisfaction.

[0051] In an embodiment of the present application, target information may be, for example, information associated with a credit limit. Risk information may be, for example, information characterizing a user's credit risk. Consumption potential may be, for example, a user's spending limit within a preset time period in the future.

[0052] In the embodiments of the present application, the configuration file may be the core of defining the quota adjustment business logic, which directly affects the consistency, maintainability, and execution efficiency of the quota adjustment policy.

[0053] Through the configuration file generation method of the embodiment of the present application, by combining the user's risk information, historical consumption situation and credit limit satisfaction to jointly generate a configuration file, the credit limit adjustment system can directly perform the adjustment operation based on the logic defined in the configuration file when it needs to adjust the user's credit limit, without having to repeatedly build a new credit limit calculation framework. This can not only ensure that the adjusted credit limit matches the user, thereby improving the user experience, but also improve the intelligence of the credit limit adjustment, thereby improving efficiency, reducing resource consumption, and saving costs. In addition, the credit limit adjustment combines multiple indicators such as risk, historical consumption situation, consumption potential, and credit limit satisfaction. When the user's situation changes, the configuration file can be updated based on the changed information, making the credit limit adjustment highly interpretable.

[0054] In an embodiment of the present application, the configuration file generation method may further include:

[0055] Feature engineering is performed on the inventory information within a predetermined time period to obtain a first feature.

[0056] A first label is constructed, where the first label represents the risk level of the user associated with the stock information.

[0057] The scoring model is trained using the first feature and the first label to obtain a trained scoring model.

[0058] According to an embodiment of the present application, in order to accurately determine the risk information of a user, a scoring model for user risk quantification can be constructed to quantitatively assess the user's default risk.

[0059] For example, all current credit card users can be used as the overall modeling sample, and the first label of users with a risk higher than a preset level is set to 1, while the first label of users with a risk lower than the preset level is set to 0.

[0060] Before obtaining a user's existing information, the user's consent or authorization may be obtained. For example, a request may be sent to the user to obtain existing information. Upon the user's consent or authorization to obtain the target information, the existing information is obtained. Existing information may include, for example, basic information, credit card information, asset status, debit card transactions, credit card transactions, credit card repayment status, mobile banking activity, and loan status.

[0061] Feature construction is performed on the acquired inventory information to generate a feature vector, which serves as the input feature for the scoring model. Feature engineering is then performed on the generated feature vector. Feature screening is performed using various methods, such as outlier detection, null value analysis, correlation coefficients, and feature binning. The screened features are used as input to train the scoring model using the XGBoost algorithm. The model can be fine-tuned using factors such as the AUC indicator, ultimately forming a scoring model for user risk quantification. Subsequently, user target information can be obtained based on the prediction time observation point, and the scoring model used for user risk quantification can be used to determine the user's risk information, which may include a risk score.

[0062] Through the configuration file generation method of the embodiment of the present application, by utilizing the deep learning capabilities of the scoring model, the user's risk information can be accurately determined, and a configuration file with a higher degree of match with the user can be generated, which helps to improve the match between the adjustment result and the user, thereby improving the user experience.

[0063] In an embodiment of the present application, the configuration file generation method may further include:

[0064] Feature engineering is performed on the inventory information within a predetermined time period to obtain a second feature, which is the same as or different from the first feature.

[0065] A second tag is constructed, where the second tag represents the consumption capacity of the user associated with the inventory information.

[0066] The regression model is trained using the second feature and the second label to obtain a trained regression model.

[0067] According to an embodiment of the present application, a configuration file may be generated based on the user's current credit limit satisfaction and the predicted credit limit satisfaction to improve the explainability of credit limit adjustments.

[0068] For example, before obtaining a user's existing information, the user's consent or authorization may be obtained. For example, a request to obtain existing information may be issued to the user. Upon the user's consent or authorization to obtain the target information, the existing information is obtained. Existing information may include, for example, basic information, credit card information, asset status, debit card transactions, credit card transactions, credit card repayment status, mobile banking activity status, and loan status.

[0069] Based on the inventory information, the customer's consumption bill amount in the past three months can be counted and the monthly credit satisfaction can be calculated respectively. Calculation formula: Credit satisfaction = bill amount / credit limit. Calculate the average credit satisfaction of the customer in the past three months as the customer's objective credit satisfaction index evaluation value to obtain the first indicator information. The calculation formula can be: AVG (N-1 month credit satisfaction, N-2 month credit satisfaction, N-3 month credit satisfaction), where N is the current month credit satisfaction.

[0070] A regression model can be constructed to estimate the consumption amount within a preset time period in the future, calculate consumption potential, and predict the satisfaction of the quota. The regression model can use the customer's consumption bill amount during the performance period as the model output value.

[0071] The construction process can be:

[0072] Feature construction is performed on the acquired inventory information to generate a feature vector, which serves as the input feature for the consumption amount estimation model. This feature vector can be reused as a feature dimension in the user risk quantification scoring model. Feature engineering is then performed on the generated feature vector. Feature screening is performed using various methods, such as outlier detection, null value analysis, correlation coefficients, and feature binning. The filtered features are used as input to train a consumption amount estimation regression model using the LR algorithm. The model can be fine-tuned using factors such as the AUC indicator to ultimately generate a consumption amount estimation regression model. Samples and features are taken based on the observation points at the prediction time, and the consumption amount estimation regression model is used to estimate the customer's spending amount. The potential predicted credit limit satisfaction is calculated as the second indicator. The calculation formula can be: potential predicted credit limit satisfaction = estimated consumption amount / credit limit. A credit limit satisfaction index can be generated by combining the first and second indicator information. The calculation formula can be: credit limit satisfaction index = 50% * objective credit limit satisfaction index evaluation value + 50% * potential predicted credit limit satisfaction.

[0073] Through the configuration file generation method of the embodiment of the present application, by utilizing the deep learning ability of the estimated regression model, it is possible to deeply learn the relationship between the objective credit limit satisfaction index and the potential predicted credit limit satisfaction, so that the estimated regression model can accurately determine the user's credit limit satisfaction index, thereby enhancing the interpretability of the credit limit adjustment and improving the user experience.

[0074] In an embodiment of the present application, generating a configuration file based on risk information, first indicator information, and second indicator information includes:

[0075] A trigger condition is defined based on the risk information, the first indicator information, and the second indicator information.

[0076] Define the quota adjustment operation to be performed when the trigger conditions are met.

[0077] Define global variables, which are used to record log information generated by quota adjustment operations.

[0078] Encapsulate the trigger conditions, quota adjustment operations, and global variables to obtain a configuration file.

[0079] According to the embodiments of the present application, the file name of the configuration file needs to express the business intent to facilitate subsequent applications. Through the configuration file design, risk information, first indicator information, and second indicator information can be integrated into the automatic adjustment limit logic to achieve a flexible and maintainable adjustment limit strategy. In addition, the configuration file supports dynamic updates and expansions to facilitate rapid response to changes in business needs. When the user situation changes, the configuration file can be dynamically updated based on the updated risk information, first indicator information, and second indicator information to meet changes in the adjustment limit needs.

[0080] In an embodiment of the present application, the configuration file generation method may further include:

[0081] Load the configuration file into memory so that the rule engine can use multi-threading to perform quota adjustment operations on multiple users in parallel based on the configuration file.

[0082] According to an embodiment of the present application, when the application is started, the configuration file is loaded into the memory and the rule engine is initialized.

[0083] The system retrieves user risk information, primary indicator information, and secondary indicator information from memory and encapsulates them into a target object. The target object is then inserted into the rule engine, which matches the conditions in the configuration file and triggers the corresponding rules. Credit limit adjustments (such as freezing or increasing credit limits) are performed based on the output of the rule engine. Log information about rule triggering is recorded to monitor rule execution and system performance.

[0084] You can use thread pools to manage concurrent requests and avoid frequent creation and destruction of instances.

[0085] Through the configuration file generation method of the embodiment of the present application, in a high concurrency scenario, the rule file is loaded into the memory, and the rule engine can be used to execute the adjustment logic when the adjustment is subsequently performed, which can significantly improve performance and response speed.

[0086] Based on the above-mentioned configuration file generation method for quota adjustment, the present application also provides a quota adjustment method.

[0087] Figure 3 The flowchart of the credit limit adjustment method according to an embodiment of the present application is schematically shown.

[0088] like Figure 3 As shown, the method for generating a configuration file for quota adjustment in this embodiment may include operations S310 to S320.

[0089] In operation S310 , in response to the quota adjustment instruction, a configuration file is obtained.

[0090] In operation S320, the target user's information is processed based on the configuration file to obtain a credit limit adjustment result.

[0091] According to an embodiment of the present application, the configuration file is generated based on the configuration file generation method of the present application.

[0092] In an embodiment of the present application, the target user's information is processed based on the configuration file to obtain a credit limit adjustment result, which may include:

[0093] Get the live profile from memory.

[0094] The rule engine is called using multiple threads to perform quota adjustment operations on multiple users in parallel based on the configuration file to obtain the quota adjustment results for each user.

[0095] According to an embodiment of the present application, a user's risk information, first indicator information, and second indicator information can be retrieved from memory and encapsulated into a target object. The target object is then inserted into a rule engine, which matches and triggers corresponding rules based on the conditions in the configuration file. Credit limit adjustments (such as freezing or increasing credit limits) are performed based on the output of the rule engine. Log information about rule triggering is recorded to monitor rule execution and system performance.

[0096] You can use thread pools to manage concurrent requests and avoid frequent creation and destruction of instances.

[0097] It should be noted that the implementation details and technical effects of the adjustment method embodiment are the same as or similar to those of the configuration file generation method, and will not be repeated here.

[0098] Based on the above configuration file generation method for adjusting the quota, the present application also provides a configuration file generation device for adjusting the quota. Figure 4 The device is described in detail.

[0099] Figure 4 The structural block diagram of the configuration file generating device for quota adjustment according to an embodiment of the present application is schematically shown.

[0100] like Figure 4 As shown, the configuration file generating apparatus 400 for quota adjustment in this embodiment includes an acquisition module 410 , a first processing module 420 , a second processing module 430 and a generation module 440 .

[0101] The acquisition module 410 is used to obtain the user's authorization to obtain and use the target information. In one embodiment, the acquisition module 410 can be used to perform the operation S210 described above, which will not be repeated here.

[0102] The first processing module 420 is configured to, after obtaining the user's authorization, input the user's target information into the trained scoring model for processing and output the user's risk information. In one embodiment, the first processing module 420 may be configured to perform the operation S220 described above, which will not be described in detail here.

[0103] The second processing module 430 is used to input the target information into the trained regression model for processing and output first indicator information, which represents the user's consumption potential. In one embodiment, the second processing module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0104] Generating module 440 is configured to generate a configuration file based on the risk information, the first indicator information, and the second indicator information. The configuration file encapsulates the credit limit adjustment execution logic based on the risk information, the first indicator information, and the second indicator information, and is invoked to execute the credit limit adjustment service. The second indicator information represents the user's historical spending. In one embodiment, generating module 440 can be used to perform operation S240 described above and will not be further described here.

[0105] According to an embodiment of the present application, the configuration file generating apparatus 400 for adjusting a quota may further include a first training module 450 configured to perform the following operations:

[0106] Feature engineering is performed on the inventory information within a predetermined time period to obtain a first feature.

[0107] A first label is constructed, where the first label represents the risk level of the user associated with the stock information.

[0108] Use the first feature and the first label to train the scoring model to obtain a trained scoring model

[0109] According to an embodiment of the present application, the configuration file generating apparatus 400 for adjusting the credit limit may further include a second training module 460 configured to perform the following operations:

[0110] Feature engineering is performed on the inventory information within a predetermined time period to obtain a second feature, which is the same as or different from the first feature.

[0111] A second tag is constructed, where the second tag represents the consumption capacity of the user associated with the inventory information.

[0112] The regression model is trained using the second feature and the second label to obtain a trained regression model.

[0113] According to an embodiment of the present application, the generation module 440 generates a configuration file based on the risk information, the first indicator information, and the second indicator information, which may include:

[0114] A trigger condition is defined based on the risk information, the first indicator information, and the second indicator information.

[0115] Define the quota adjustment operation to be performed when the trigger conditions are met.

[0116] Define global variables, which are used to record log information generated by quota adjustment operations.

[0117] Encapsulate the trigger conditions, quota adjustment operations, and global variables to obtain a configuration file.

[0118] According to an embodiment of the present application, the configuration file generating apparatus 400 for adjusting the quota may further include a loading module 470 configured to perform the following operations:

[0119] Load the configuration file into memory so that the rule engine can use multi-threading to perform quota adjustment operations on multiple users in parallel based on the configuration file.

[0120] Based on the above quota adjustment method, this application also provides a quota adjustment device. Figure 5 The device is described in detail.

[0121] Figure 5 The structural block diagram of the credit limit adjustment device according to an embodiment of the present application is schematically shown.

[0122] like Figure 5 As shown, the credit limit adjustment device 500 of this embodiment includes a response module 510 and a credit limit adjustment module 520 .

[0123] The response module 510 is configured to obtain a configuration file in response to the quota adjustment instruction. In one embodiment, the response module 510 may be configured to execute the operation S310 described above, which will not be described in detail here.

[0124] The quota adjustment module 520 processes the target user's information based on the configuration file to obtain a quota adjustment result. In one embodiment, the quota adjustment module 520 can be used to perform the operation S320 described above, which will not be repeated here.

[0125] According to an embodiment of the present application, the configuration file is generated based on a configuration file generation method for quota adjustment or based on a configuration file generation device for quota adjustment.

[0126] It should be noted that the implementation details of the device embodiment and the technical effects and methods brought about are the same or similar and will not be repeated here.

[0127] According to embodiments of the present application, any multiple modules among the acquisition module 410, the first processing module 420, the second processing module 430, the generation module 440, the first training module 450, the second training module 460, and the loading module 470 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present application, at least one of the acquisition module 410, the first processing module 420, the second processing module 430, the generation module 440, the first training module 450, the second training module 460, and the loading module 470 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware by any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination thereof. Alternatively, at least one of the acquisition module 410, the first processing module 420, the second processing module 430, the generation module 440, the first training module 450, the second training module 460 and the loading module 470 can be at least partially implemented as a computer program module, which can perform the corresponding function when it is executed.

[0128] Figure 6 A block diagram of an electronic device suitable for implementing a method for generating a configuration file for quota adjustment according to an embodiment of the present application is schematically shown.

[0129] like Figure 6 As shown, an electronic device 600 according to an embodiment of the present application includes a processor 601, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 602 or a program loaded from a storage unit 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present application.

[0130] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to the embodiment of the present application by executing the programs in the ROM 602 and / or RAM 603. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations of the method flow according to the embodiment of the present application by executing the programs stored in the one or more memories.

[0131] According to an embodiment of the present application, electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to bus 604. Electronic device 600 may also include one or more of the following components connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or modem. Communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 610 as needed, so that computer programs read from the removable media can be installed into storage section 608 as needed.

[0132] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of this application is implemented.

[0133] According to an embodiment of the present application, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present application, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0134] The embodiments of the present application also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided in the embodiments of the present application.

[0135] The computer program executes the above functions defined in the system / device of the embodiment of the present application when the computer program is executed by the processor 601. According to the embodiment of the present application, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0136] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0137] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-mentioned functions defined in the system of the embodiment of the present application are performed. According to the embodiment of the present application, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0138] According to an embodiment of the present application, the program code for executing the computer program provided by the embodiment of the present application can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of 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 an order different from that 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 or flowchart, and the combination of the boxes in the block diagram or flowchart, 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.

[0140] Those skilled in the art will appreciate that the features described in the various embodiments of this application may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in this application. In particular, the features described in the various embodiments of this application may be combined and / or coupled in various ways without departing from the spirit and teachings of this application. All such combinations and / or couplings fall within the scope of this application.

Claims

1. A method for generating a configuration file for adjusting a credit limit, characterized in that: The method comprises: Obtaining the user's authorization to obtain and use the target information, and after obtaining the user's authorization, inputting the user's target information into the target scoring model for processing, and outputting the user's risk information; Inputting the target information into a target regression model for processing and outputting first indicator information, wherein the first indicator information represents the credit satisfaction corresponding to the user's consumption potential; Based on the risk information, the first indicator information and the second indicator information, a configuration file is generated. The configuration file is used to encapsulate the credit limit adjustment execution logic based on the risk information, the first indicator information and the second indicator information. The configuration file is used to be called to execute the credit limit adjustment business; the second indicator information represents the user's historical consumption and the corresponding credit limit satisfaction.

2. The method according to claim 1, characterized in that The method further comprises: Perform feature engineering on the inventory information within a predetermined time period to obtain a first feature; Constructing a first label, wherein the first label represents a risk level of a user associated with the stock information; A scoring model is trained using the first feature and the first label to obtain a target scoring model.

3. The method according to claim 2, characterized in that The method further comprises: Performing feature engineering on the inventory information within a predetermined time period to obtain a second feature, where the second feature is the same as or different from the first feature; Constructing a second tag, wherein the second tag represents the consumption capacity of the user associated with the inventory information; The regression model is trained using the second feature and the second label to obtain a target regression model.

4. The method according to any one of claims 1 to 3, characterized in that The generating a configuration file based on the risk information, the first indicator information, and the second indicator information includes: defining a trigger condition based on the risk information, the first indicator information, and the second indicator information; Define the quota adjustment operation to be performed when the trigger conditions are met; Define a global variable, which is used to record log information generated by the quota adjustment operation; The trigger condition, the quota adjustment operation and the global variable are encapsulated to obtain the configuration file.

5. The method according to any one of claims 1 to 3, characterized in that The method further comprises: The configuration file is loaded into the memory so that the rule engine uses multi-threading to perform quota adjustment operations on multiple users in parallel based on the configuration file.

6. A credit limit adjustment method, characterized in that: include: In response to the quota adjustment instruction, processing the target user's information based on the configuration file to obtain a quota adjustment result; The configuration file is generated based on the method described in any one of claims 1 to 5.

7. The credit limit adjustment method according to claim 6, characterized in that: The processing of the target user's information based on the configuration file to obtain a credit limit adjustment result includes: Get the real-time configuration file from memory; The rule engine is called using multiple threads to perform quota adjustment operations on multiple users in parallel based on the configuration file to obtain the quota adjustment results for each user.

8. A configuration file generating device for adjusting quota, characterized in that: The device comprises: The acquisition module is used to obtain the user's authorization to obtain and use the target information; A first processing module is configured to input the user's target information into a target scoring model for processing after obtaining the user's authorization, and output the user's risk information; a second processing module, configured to input the target information into a target regression model for processing and output first indicator information, wherein the first indicator information represents the consumption potential of the user; A generation module is used to generate a configuration file based on the risk information, the first indicator information and the second indicator information. The configuration file is used to encapsulate the credit limit adjustment execution logic based on the risk information, the first indicator information and the second indicator information. The configuration file is used to be called to execute the credit limit adjustment business; the second indicator information represents the user's historical consumption.

9. A credit limit adjustment device, characterized in that: The device comprises: Response module, used to respond to the quota adjustment instruction and obtain the configuration file a quota adjustment module, configured to process the target user's information based on the configuration file to obtain a quota adjustment result; The configuration file is generated based on the method according to any one of claims 1 to 5 or based on the device according to claim 8.

10. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

11. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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