Limit data adjustment method and device, computer equipment and storage medium

Abstract

The invention belongs to the field of artificial intelligence and financial science and technology, and relates to a quota data adjustment method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the quota qualification information of an application customer within a preset period according to an application signal when the application signal for a target quota adjustment project is detected; acquiring historical behavior data of the application client, and analyzing according to the historical behavior data and the quota qualification information to obtain a quota adjustment willingness value; matching a quota adjustment strategy according to the quota adjustment intention value, generating authorization confirmation information and sending the authorization confirmation information to the client; when the receiving signal is received, a specific consumption item needing to be detected is determined; and when the consumption behavior is detected, adjusting the target quota data according to the quota adjustment strategy. In addition, the invention also relates to a block chain technology, and data such as quota qualification information and historical behavior data can be stored in a block chain. According to the invention, flexible quota adjustment can be realized, and the user experience and bank income are improved.

Description

Technical Field

[0001] The present application relates to the fields of artificial intelligence and financial technology, and in particular to a method, device, computer equipment and storage medium for adjusting credit limit data. Background Art

[0002] In the field of credit card services, in the existing credit limit inquiry process, the system will immediately conduct a real-time credit report inquiry when the user visits the credit limit page, lacking an effective inquiry control mechanism. This design conducts credit inquiries without clarifying whether the user has a real credit limit demand, which not only increases the bank's operating costs, but may also lead to frequent credit inquiries due to the user's unconscious operations, which in turn has an adverse impact on the user's loan risk assessment.

[0003] On the other hand, for users who have real credit limit needs, the credit limit approval process is often limited by multiple factors such as personal conditions of applicants, such as income level, years of work experience, and industry. For some high-potential users, due to the credit limit restrictions, these people may be hindered in the consumption process of certain specific projects, which not only affects the user's consumption experience, but also further limits the bank's business development and revenue.

[0004] Therefore, it is necessary to improve and optimize the credit card limit management plan to enhance user experience and bank profits. Summary of the invention

[0005] The purpose of the embodiments of the present application is to propose a credit limit data adjustment method, device, computer equipment and storage medium, which can flexibly adjust the credit limit and improve user experience and bank profits.

[0006] In order to solve the above technical problems, the present application embodiment provides a method for adjusting credit limit data, which adopts the following technical solution:

[0007] When an application signal for a target credit limit adjustment item sent by a client is detected, the credit limit qualification information of the applicant within a preset period is obtained according to the application signal;

[0008] Acquire the historical behavior data of the applicant, and analyze and obtain the credit limit adjustment willingness value of the applicant based on the historical behavior data and the credit limit qualification information;

[0009] Matching a credit limit adjustment policy according to the credit limit adjustment willingness value, generating authorization confirmation information according to the credit limit adjustment policy and sending it to the client;

[0010] When receiving an acceptance signal for the authorization confirmation information returned by the client, the specific consumption item to be detected is obtained by parsing the target quota adjustment item;

[0011] When the consumption behavior of the applicant customer for the specific consumption item is detected, the target credit limit data of the applicant customer is adjusted according to the credit limit adjustment strategy.

[0012] In order to solve the above technical problems, the embodiment of the present application further provides a quota data adjustment device, which adopts the following technical solution:

[0013] An acquisition module, configured to acquire the credit limit qualification information of the applicant within a preset period according to the application signal when an application signal for a target credit limit adjustment item sent by the client is detected;

[0014] An analysis module, used to obtain the historical behavior data of the applicant, and analyze and obtain the credit limit adjustment willingness value of the applicant based on the historical behavior data and the credit limit qualification information;

[0015] A matching module, used for matching a credit limit adjustment policy according to the credit limit adjustment willingness value, generating authorization confirmation information according to the credit limit adjustment policy and sending it to the client;

[0016] A parsing module, configured to, when receiving an acceptance signal for the authorization confirmation information returned by the client, parse the target quota adjustment item to obtain a specific consumption item that needs to be detected;

[0017] The adjustment module is used to adjust the target credit limit data of the applicant customer according to the credit limit adjustment strategy when the consumption behavior of the applicant customer for the specific consumption item is detected.

[0018] In order to solve the above technical problems, an embodiment of the present application also provides a computer device, which adopts the technical solution described below: the computer device includes a memory and a processor, the memory stores computer-readable instructions, and the processor implements the steps of the quota data adjustment method as described in any one of the above items when executing the computer-readable instructions.

[0019] In order to solve the above technical problems, an embodiment of the present application also provides a computer-readable storage medium, which adopts the following technical solution: the computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the quota data adjustment method as described in any one of the above items are implemented.

[0020] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0021] The embodiment of the present application obtains the credit limit qualification information and historical behavior data of the applicant customer when detecting the application signal for the target credit limit adjustment project sent by the client, and analyzes the credit limit adjustment willingness value of the applicant customer based on the credit limit qualification information and historical behavior data, so as to clarify the customer's real demand for credit limit adjustment, and at the same time avoid frequent credit inquiries caused by the customer's unconscious operation, thereby reducing the bank's operating costs; by matching the corresponding credit limit adjustment strategy according to the credit limit adjustment willingness value, and generating authorization confirmation information according to the credit limit adjustment strategy for the customer to make a decision, it ensures that the customer agrees and accepts the credit limit adjustment strategy, and improves the accuracy and rationality of the credit limit adjustment; when the customer's consumption behavior for a specific consumption project is detected, the customer's target credit limit data is adjusted according to the credit limit adjustment strategy, and the credit limit adjustment can be automatically reached based on the customer's actual consumption behavior, thereby enhancing the customer's consumption experience. The present application scheme meets the customer's consumption needs through a flexible credit limit adjustment strategy, promotes the development of banking business and revenue growth, and at the same time, reduces the risk of potential customer loss due to credit limit restrictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0024] Figure 2 It is a flowchart of an embodiment of the method for adjusting the quota data of the present application;

[0025] Figure 3 It is a structural diagram of an embodiment of the quota data adjustment device of the present application;

[0026] Figure 4 It is a structural diagram of an embodiment of a computer device of the present application. DETAILED DESCRIPTION

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0028] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0030] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0031] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0032] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0033] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0034] It should be noted that the credit limit data adjustment method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the credit limit data adjustment device is generally set in the server / terminal device.

[0035] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0036] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for adjusting the credit limit data according to the present application. The credit limit data adjustment method comprises the following steps:

[0037] Step S201, when an application signal for a target credit limit adjustment item sent by a client is detected, the credit limit qualification information of the applicant within a preset period is obtained according to the application signal;

[0038] In this embodiment, the electronic device (eg Figure 1 The server / terminal device shown in the figure) can obtain the application signal for the target amount adjustment project sent by the client through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 2G / 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0039] In this embodiment, a monitoring mechanism is pre-set to capture the application signal for the target credit limit adjustment project sent by the client. Among them, the target credit limit adjustment project refers to the specific request made by the customer to the bank or financial institution according to his own needs to adjust his existing credit limit data in the credit card or loan service. For example, the target credit limit adjustment project can be a credit limit increase project (such as the customer needs a higher credit limit to support his consumption or business needs due to increased consumption, increased income, etc.). The application signal refers to the electronic information or instruction sent by the client (such as mobile banking APP, online banking, telephone banking, etc.) when the customer submits a credit limit adjustment project request to the bank or financial institution. The application signal contains specific information of the customer's request for credit limit adjustment, such as the customer's identity information, adjustment type (increase / decrease), adjustment amount, adjustment reason, etc.

[0040] Specifically, when an application signal for a target credit limit adjustment project sent by a client is detected, relevant information is extracted from the application signal, such as the project information of the target credit limit adjustment project and the identity information of the applicant. The credit limit qualification information of the applicant within a preset period is obtained according to the application signal. For example, according to the identity information extracted from the application signal, a database storing credit limit qualification information of each customer is accessed, and the credit limit qualification information of the identity information within a preset period is screened from the database. Among them, credit limit qualification information refers to a series of key data sets used to evaluate whether a customer is qualified to adjust the credit limit. The data set may include but is not limited to the credit record of the customer within a preset period (the record reflects the customer's economic ability and credit status), the current credit limit data, the credit limit usage history, the repayment record, and the comprehensive credit score.

[0041] In some optional implementations, the step of obtaining the credit limit qualification information of the applicant within a preset period according to the application signal may further include the following steps:

[0042] Obtaining identity information of the applicant according to the application signal analysis;

[0043] Specifically, based on the detected application signal, the identity information of the applicant is parsed and obtained. The identity information may include but is not limited to the name, ID number, mobile phone number, bank account number and identity identifier of the applicant.

[0044] Determine whether the applicant is in the fast charge quota whitelist or the fast charge quota application list according to the identity information;

[0045] Specifically, fast charging is a financial service that provides fast approval and loan disbursement, aiming to improve customer experience and shorten the waiting time for credit limit adjustment. A fast charging credit application refers to a credit limit adjustment application submitted by a customer to a financial institution for fast approval and loan disbursement services. The fast charging credit whitelist refers to a list of customers who have submitted fast charging credit applications to financial institutions and meet the fast charging service conditions and have passed the approval. Customers on this customer list usually have good credit, excellent historical transaction records, and are entitled to fast approval and loan disbursement services.

[0046] Specifically, based on the parsed identity information, check whether the applicant is in the fast-charge quota whitelist, that is, check whether the applicant has submitted a fast-charge quota application to a financial institution and has the authority to enjoy fast approval and loan services. Based on the parsed identity information, check whether the applicant is in the fast-charge quota application list, that is, check whether the applicant is a customer who has submitted a fast-charge quota application but has not yet completed the approval.

[0047] If the applicant does not exist in the fast charge quota whitelist and the fast charge quota application list, determining whether the applicant exists in the project whitelist of the target quota adjustment project according to the identity information;

[0048] Specifically, if it is found that the applicant does not exist in the fast charge quota whitelist or the fast charge quota application list, then based on the parsed identity information, it is queried whether the applicant exists in the project whitelist of the target quota adjustment project. The project whitelist refers to the list of customers who meet the relevant project service conditions pre-screened by financial institutions for specific quota adjustment projects (such as target quota adjustment projects).

[0049] If the applicant is in the project whitelist, the credit limit qualification information of the applicant within a preset period is obtained based on the identity information.

[0050] Specifically, if it is found that the applicant customer is in the project whitelist of the target credit limit adjustment project, it means that the applicant customer meets the project service conditions of the target credit limit adjustment project. Then, based on the parsed identity information, the database storing the credit limit qualification information of each customer is accessed, and the credit limit qualification information of the identity information within a preset period is filtered out from the database.

[0051] By pre-screening and maintaining a whitelist of fast-charge credits, a list of fast-charge credit applications, and a whitelist of projects, the embodiment of the present application can quickly identify eligible customers after receiving their credit adjustment applications without the need for real-time credit report inquiries, greatly shortening the processing time for credit adjustment and improving overall business processing efficiency. By classifying and maintaining customers in different whitelists or lists, customers who have real needs for specific credit adjustment projects and have a strong willingness to adjust can be more accurately identified, helping to better meet the personalized needs of customers.

[0052] Step S202, obtaining the historical behavior data of the applicant, and analyzing and obtaining the credit limit adjustment willingness value of the applicant based on the historical behavior data and the credit limit qualification information;

[0053] In this embodiment, historical behavior data refers to data generated when the applicant interacts with financial institutions or other related services in the past period of time. The data may include but is not limited to consumption records, repayment records, overdue situations, frequency and results of applying for other financial products, browsing behavior of financial products (such as clicks, browsing time, etc.), and records of interaction with customers (such as customer service consultations, complaints, etc.). The willingness value for credit limit adjustment is a quantitative indicator used to measure the willingness or desire of applicants to adjust their existing credit limits. The willingness value for credit limit adjustment can be a probability (indicating the possibility that the customer is willing to adjust the credit limit) or a score (indicating the customer's desire for credit limit adjustment) obtained based on the analysis of historical behavior data and credit limit qualification information.

[0054] Specifically, the historical behavior data of the applicant in the past period of time is retrieved from the database or obtained from a third-party data source. Analysis is performed based on the historical behavior data and the credit limit qualification information. For example, a prediction model is constructed using machine learning or statistical methods. The prediction model is used to predict the credit limit adjustment willingness value based on the historical behavior data and credit limit qualification information. The historical behavior data and credit limit qualification information of the applicant are input into the trained prediction model to calculate the credit limit adjustment willingness value of the applicant.

[0055] In some optional implementations, the above-mentioned analyzing and obtaining the credit limit adjustment willingness value of the applicant customer based on the historical behavior data and the credit limit qualification information may further include the following steps:

[0056] Analyze the consumption demand information of the applicant customer according to the historical behavior data;

[0057] The credit limit adjustment willingness value of the applicant customer is determined according to the consumption demand information and the credit limit qualification information.

[0058] Specifically, consumer demand information is extracted from historical behavior data, such as consumption frequency, consumption amount, consumption type, consumption cycle, consumption trend, etc.; payment ability information is extracted from credit limit qualification information, such as current credit limit data, credit limit usage history, repayment record, etc. A prediction model is pre-trained using machine learning, and the consumer demand information extracted from the applicant's historical behavior data and the payment ability information extracted from the applicant's credit limit qualification information are input into the trained prediction model to calculate the applicant's credit limit adjustment willingness value.

[0059] The embodiment of the present application can accurately capture the customer's consumption demand information by analyzing the applicant's historical behavior data, and can accurately capture the customer's payment ability information by analyzing the credit limit qualification information. By using machine learning technology to build a prediction model, the prediction model can quickly and accurately predict the applicant's credit limit adjustment willingness value.

[0060] In some optional implementations, the step of determining the credit limit adjustment willingness value of the applicant customer according to the consumption demand information and the credit limit qualification information may further include the following steps:

[0061] Acquire a first weight value of each first component element in the consumer demand information, and quantify the consumer demand information into a consumer demand value according to the first weight value;

[0062] Specifically, determine each first component element contained in the consumption demand information of the applicant, which may include consumption frequency, consumption amount, consumption type, consumption cycle, consumption trend, etc., and assign a corresponding first weight value to each first component element, wherein the sum of the first weight values ​​of each first component element is 1 (or 100%). Perform data standardization on each first component element contained in the consumption demand information, and perform weighted sum calculation based on the value of each first component element after standardization and the first weight value assigned to each first component element to obtain the consumption demand value.

[0063] Acquire a second weight value of each second component element in the credit limit qualification information, and quantify the credit limit qualification information into a payment ability value according to the second weight value;

[0064] Specifically, the second components included in the credit qualification information of the applicant are determined, and the second components may include the credit limit data currently held, the credit limit usage history, the repayment record, etc., and a corresponding second weight value is assigned to each second component, wherein the sum of the second weight values ​​of each second component is 1 (or 100%). Data standardization is performed on each second component included in the credit qualification information, and a weighted sum calculation is performed based on the standardized value of each second component and the second weight value assigned to each second component to obtain the payment capacity value.

[0065] The credit limit adjustment willingness value of the applicant customer is calculated based on the consumption demand value and the payment ability value.

[0066] Specifically, the calculation is performed based on the consumption demand value and the payment ability value. For example, the consumption demand value and the payment ability value are compared to calculate the difference or ratio. The difference between the consumption demand value and the payment ability value reflects the degree of mismatch between the two; the ratio between the consumption demand value and the payment ability value reflects the relative relationship between the two. The credit limit adjustment willingness value of the applicant customer is determined based on the calculated difference or ratio.

[0067] The embodiment of the present application achieves the transition from qualitative description to quantitative analysis by obtaining the first weight value of each first component element in the consumer demand information and quantifying the consumer demand information into a consumer demand value based on the first weight value; and by obtaining the second weight value of each second component element in the credit qualification information and quantifying the credit qualification information into a payment ability value based on the second weight value. By combining the consumer demand value and the payment ability value, the credit adjustment willingness value of the applicant is calculated, and the statistical method is used for quantitative analysis and comprehensive evaluation, which can greatly improve the efficiency of decision-making.

[0068] Step S203, matching a credit limit adjustment policy according to the credit limit adjustment willingness value, generating authorization confirmation information according to the credit limit adjustment policy and sending it to the client;

[0069] In this embodiment, the credit limit adjustment strategy refers to a credit limit adjustment plan formulated based on the customer's credit limit adjustment willingness value, consumption demand, payment ability and other relevant factors. The plan may include, but is not limited to, the size of the adjusted credit limit, the time when the adjustment takes effect, and other contents. Authorization confirmation information refers to the confirmation information sent to the applicant customer after the credit limit adjustment is determined. The authorization confirmation information may include, but is not limited to, the details of the adjusted credit limit, the time when the adjustment takes effect, the content that needs to be confirmed by the customer (such as accepting or rejecting the adjustment), the deadline for confirmation, and the method of confirmation (such as clicking a link, replying to a text message, etc.).

[0070] Specifically, the corresponding credit limit adjustment strategy is set in advance according to different ranges of credit limit adjustment willingness values, and the credit limit adjustment strategy may include but is not limited to large-amount long-term adjustment strategy, small-amount short-term adjustment strategy, etc. The credit limit adjustment strategy is obtained according to the credit limit adjustment willingness value of the applicant, and authorization confirmation information is generated according to the matched credit limit adjustment strategy. The communication method of the client is determined, and the authorization confirmation information is sent to the client. After the applicant receives the authorization confirmation information on the client, he or she chooses to accept or reject the credit limit adjustment strategy according to his or her own wishes.

[0071] In some optional implementations, the matching of the credit limit adjustment strategy according to the credit limit adjustment willingness value may further include the following steps:

[0072] Compare the credit limit adjustment willingness value with a preset threshold value to obtain a comparison result;

[0073] If the comparison result is that the credit limit adjustment willingness value is greater than or equal to the preset threshold, the credit limit adjustment strategy is determined as a large-amount long-term adjustment strategy;

[0074] If the comparison result is that the credit limit adjustment willingness value is less than the preset threshold, the credit limit adjustment strategy is determined as a small-amount short-term adjustment strategy.

[0075] Specifically, the collected credit limit adjustment willingness values ​​are compared with a preset threshold to obtain a comparison result, and the preset threshold is used to classify the credit limit adjustment willingness values ​​in order to decide which adjustment strategy to adopt. When the comparison result is that the credit limit adjustment willingness value is greater than or equal to the preset threshold, it indicates that the applicant wants to make a larger credit limit adjustment, or the adjustment willingness is very strong. At this time, the credit limit adjustment strategy is determined as a large-amount long-term adjustment strategy. When the comparison result is that the credit limit adjustment willingness value is less than the preset threshold, it indicates that the applicant wants to make a smaller credit limit adjustment, or the adjustment willingness is relatively mild. At this time, the credit limit adjustment strategy is determined as a small-amount short-term adjustment strategy.

[0076] The embodiment of the present application can more accurately match a credit limit adjustment strategy suitable for the customer by comparing the credit limit adjustment willingness value with a preset threshold.

[0077] Step S204, when receiving the acceptance signal for the authorization confirmation information returned by the client, the specific consumption item to be detected is obtained according to the target amount adjustment item analysis;

[0078] In this embodiment, the receiving signal for the authorization confirmation information from the client is monitored. When the receiving signal for the authorization confirmation information returned by the client is received, the target credit adjustment item applied by the applicant is analyzed to obtain the relevant consumption items included in the target credit adjustment item, and the relevant consumption items are determined as specific consumption items that need to be detected. Among them, the specific consumption items may include but are not limited to subscription for financial management, large-amount cross-border transfer consumption, or consumption in specific industries.

[0079] Step S205, when the consumption behavior of the applicant customer for the specific consumption item is detected, the target credit limit data of the applicant customer is adjusted according to the credit limit adjustment strategy.

[0080] In this embodiment, a monitoring mechanism is set for the applicant customer to track and record the consumption behavior of the applicant customer. When the consumption behavior of the applicant customer is detected, the consumption behavior is matched with a preset specific consumption item. When the consumption behavior is successfully matched with the specific consumption item, the target credit limit data of the applicant customer is adjusted according to the credit limit adjustment strategy confirmed by the applicant customer.

[0081] In some optional implementations, when the consumption behavior of the applicant customer for the specific consumption item is detected, adjusting the target credit limit data of the applicant customer according to the credit limit adjustment strategy may further include the following steps:

[0082] When the payment request information of the applicant customer for the specific consumption item is detected, determining the credit limit adjustment range of the applicant customer according to the payment request information and the credit limit adjustment strategy;

[0083] The credit limit data of the applicant customer is adjusted to the target credit limit data according to the credit limit adjustment range.

[0084] Specifically, a monitoring mechanism is set up for the applicant customer to track and record the applicant customer's request payment information for specific consumption items. The request payment information may include the payment item type, payment amount, payment time, etc. When the applicant customer's request payment information is detected, the request payment information is matched with the preset specific consumption item. When the consumption behavior is successfully matched with the specific consumption item, the applicant customer's credit limit adjustment range is determined based on the credit limit adjustment strategy confirmed by the applicant customer and the request payment information. The credit limit adjustment range can be a fixed value or a preset ratio based on the request payment amount. According to the credit limit adjustment range, the credit limit data of the applicant customer is adjusted from the current credit limit value to the target credit limit value.

[0085] The embodiment of the present application can quickly respond to the consumption needs of customers by real-time or periodic detection of the requested payment information of the applicant customer, and automatically adjust the credit limit data according to the credit limit adjustment strategy. This dynamic adjustment mechanism ensures that customers can obtain sufficient payment credit when needed, and improves the flexibility and response speed of the service.

[0086] The embodiment of the present application adopts the above scheme, specifically by obtaining the credit limit qualification information and historical behavior data of the applicant customer when detecting the application signal for the target credit limit adjustment project sent by the client, and analyzing the customer's credit limit adjustment willingness value based on the credit limit qualification information and historical behavior data, so as to clarify the customer's real demand for credit limit adjustment, and at the same time avoid frequent credit inquiries caused by the customer's unconscious operation, thereby reducing the bank's operating costs; by matching the corresponding credit limit adjustment strategy according to the credit limit adjustment willingness value, and generating authorization confirmation information according to the credit limit adjustment strategy for the customer to make a decision, it is ensured that the customer agrees and accepts the credit limit adjustment strategy, and improves the accuracy and rationality of the credit limit adjustment; when the customer's consumption behavior for a specific consumption project is detected, the customer's target credit limit data is adjusted according to the credit limit adjustment strategy, and the credit limit adjustment can be automatically reached based on the customer's actual consumption behavior, thereby enhancing the customer's consumption experience. The present application scheme meets the customer's consumption needs through a flexible credit limit adjustment strategy, promotes the development of banking business and revenue growth, and at the same time, reduces the risk of potential customer loss due to credit limit restrictions.

[0087] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned credit limit qualification information, historical behavior data, consumption behavior information and other relevant data, the above-mentioned credit limit qualification information, historical behavior data, consumption behavior information and other relevant data can also be stored in a blockchain node.

[0088] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0089] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0090] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0091] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a quota data adjustment device, which is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0092] like Figure 3 As shown, the credit limit data adjustment device 400 described in this embodiment includes: an acquisition module 401, an analysis module 402, a matching module 403, a parsing module 404 and an adjustment module 405.

[0093] in:

[0094] The acquisition module 401 is used to acquire the credit limit qualification information of the applicant within a preset period according to the application signal when detecting the application signal for the target credit limit adjustment project sent by the client;

[0095] The analysis module 402 is used to obtain the historical behavior data of the applicant, and analyze and obtain the credit limit adjustment willingness value of the applicant according to the historical behavior data and the credit limit qualification information;

[0096] A matching module 403 is used to match the credit limit adjustment policy according to the credit limit adjustment willingness value, generate authorization confirmation information according to the credit limit adjustment policy, and send it to the client;

[0097] The parsing module 404 is configured to parse the target amount adjustment item to obtain the specific consumption item that needs to be detected when receiving the acceptance signal for the authorization confirmation information returned by the client;

[0098] The adjustment module 405 is used to adjust the target credit limit data of the applicant customer according to the credit limit adjustment strategy when the consumption behavior of the applicant customer for the specific consumption item is detected.

[0099] In this embodiment, a monitoring mechanism is pre-set to capture the application signal for the target credit limit adjustment project sent by the client. Among them, the target credit limit adjustment project refers to the specific request made by the customer to the bank or financial institution according to his own needs to adjust his existing credit limit data in the credit card or loan service. For example, the target credit limit adjustment project can be a credit limit increase project (such as the customer needs a higher credit limit to support his consumption or business needs due to increased consumption, increased income, etc.). The application signal refers to the electronic information or instruction sent by the client (such as mobile banking APP, online banking, telephone banking, etc.) when the customer submits a credit limit adjustment project request to the bank or financial institution. The application signal contains specific information of the customer's request for credit limit adjustment, such as the customer's identity information, adjustment type (increase / decrease), adjustment amount, adjustment reason, etc.

[0100] Specifically, the acquisition module 401 is used to extract relevant information from the application signal, such as the project information of the target credit adjustment project and the identity information of the applicant, when detecting the application signal sent by the client for the target credit adjustment project. The credit qualification information of the applicant within the preset period is obtained according to the application signal. For example, it can be that according to the identity information extracted from the application signal, a database storing the credit qualification information of each customer is accessed, and the credit qualification information of the identity information within the preset period is screened from the database. Among them, the credit qualification information refers to a series of key data sets used to evaluate whether the customer is qualified to adjust the credit limit. The data set may include but is not limited to the credit record of the customer within the preset period (the record reflects the customer's economic ability and credit status), the current credit limit data, the credit limit usage history, the repayment record, and the comprehensive credit score.

[0101] Specifically, historical behavior data refers to data generated when applicants interact with financial institutions or other related services in the past period of time. The data may include but are not limited to consumption records, repayment records, overdue situations, frequency and results of applying for other financial products, browsing behavior of financial products (such as clicks, browsing time, etc.), and records of interaction with customers (such as customer service consultations, complaints, etc.). The willingness to adjust the credit limit value is a quantitative indicator used to measure the willingness or desire of applicants to adjust their existing credit limits. The willingness to adjust the credit limit value can be a probability (indicating the possibility that the customer is willing to adjust the credit limit) or a score (indicating the customer's desire for credit limit adjustment) obtained based on the analysis of historical behavior data and credit limit qualification information.

[0102] Specifically, the analysis module 402 is used to retrieve the historical behavior data of the applicant in the past period of time from the database or from a third-party data source. The analysis is performed based on the historical behavior data and the credit limit qualification information. For example, a prediction model is constructed using machine learning or statistical methods. The prediction model is used to predict the credit limit adjustment willingness value based on the historical behavior data and credit limit qualification information. The historical behavior data and credit limit qualification information of the applicant are input into the trained prediction model to calculate the credit limit adjustment willingness value of the applicant.

[0103] Specifically, the credit limit adjustment strategy refers to a credit limit adjustment plan formulated based on the customer's credit limit adjustment willingness value, consumption demand, payment ability and other relevant factors. The plan may include but is not limited to the size of the adjusted credit limit, the time when the adjustment takes effect, etc. Authorization confirmation information refers to the confirmation information sent to the applicant customer after the credit limit adjustment is determined. The authorization confirmation information may include but is not limited to the details of the adjusted credit limit, the time when the adjustment takes effect, the content that requires customer confirmation (such as acceptance or rejection of the adjustment), the deadline for confirmation, and the method of confirmation (such as clicking a link, replying to a text message, etc.).

[0104] Specifically, the corresponding credit limit adjustment strategy is set in advance according to different ranges of credit limit adjustment willingness values, and the credit limit adjustment strategy may include but is not limited to large-amount long-term adjustment strategy, small-amount short-term adjustment strategy, etc. Matching module 403 is used to match the credit limit adjustment willingness value of the applicant client to obtain the credit limit adjustment strategy, and generate authorization confirmation information according to the matched credit limit adjustment strategy. Determine the communication mode of the client, and send the authorization confirmation information to the client. After receiving the authorization confirmation information on the client, the applicant client chooses to accept or reject the credit limit adjustment strategy according to his own wishes.

[0105] Specifically, the parsing module 404 is used to monitor the receiving signal for the authorization confirmation information from the client, and when receiving the receiving signal for the authorization confirmation information returned by the client, parse the target amount adjustment item applied by the applicant, obtain the relevant consumption items included in the target amount adjustment item, and determine the relevant consumption items as specific consumption items that need to be detected. Among them, the specific consumption items may include but are not limited to subscription for financial management, large-amount cross-border transfer consumption, or consumption in a specific industry.

[0106] Specifically, the adjustment module 405 is used to set a monitoring mechanism for the applicant customer, and is used to track and record the consumption behavior of the applicant customer. When the consumption behavior of the applicant customer is detected, the consumption behavior is matched with a preset specific consumption item. When the consumption behavior is successfully matched with the specific consumption item, the target credit limit data of the applicant customer is adjusted according to the credit limit adjustment strategy confirmed by the applicant customer.

[0107] The credit limit data adjustment device 400 of the embodiment of the present application, by acquiring the credit limit qualification information and historical behavior data of the applicant customer when detecting the application signal for the target credit limit adjustment project sent by the client, and analyzing the credit limit adjustment willingness value of the applicant customer according to the credit limit qualification information and historical behavior data, can clarify the customer's real demand for credit limit adjustment, and at the same time avoid frequent credit inquiries caused by the customer's unconscious operation, thereby reducing the bank's operating costs; by matching the corresponding credit limit adjustment strategy according to the credit limit adjustment willingness value, and generating authorization confirmation information according to the credit limit adjustment strategy for the customer to make a decision, it ensures that the customer agrees and accepts the credit limit adjustment strategy, and improves the accuracy and rationality of the credit limit adjustment; when the customer's consumption behavior for a specific consumption project is detected, the customer's target credit limit data is adjusted according to the credit limit adjustment strategy, and the credit limit adjustment can be automatically reached based on the customer's actual consumption behavior, thereby enhancing the customer's consumption experience. The present application scheme meets the customer's consumption needs through a flexible credit limit adjustment strategy, promotes the development of banking business and revenue growth, and at the same time, reduces the risk of potential customer loss due to credit limit restrictions.

[0108] In some optional implementations of this embodiment, the acquisition module 401 may include a parsing submodule, a first determining submodule, a second determining submodule, and an acquisition submodule.

[0109] A parsing submodule, used for parsing the application signal to obtain the identity information of the applicant;

[0110] A first determination submodule is used to determine whether the applicant is in the fast charging quota whitelist or the fast charging quota application list according to the identity information;

[0111] A second determination submodule is used to determine whether the applicant exists in the project whitelist of the target quota adjustment project according to the identity information if the applicant does not exist in the fast charging quota whitelist and the fast charging quota application list;

[0112] The acquisition submodule is used to obtain the credit limit qualification information of the applicant within a preset period according to the identity information if the applicant exists in the project whitelist.

[0113] Specifically, the parsing submodule is used to parse the detected application signal to obtain the identity information of the applicant, and the identity information may include but is not limited to the applicant's name, ID number, mobile phone number, bank account number and identity identifier.

[0114] Specifically, fast charging is a financial service that provides fast approval and loan disbursement, aiming to improve customer experience and shorten the waiting time for credit limit adjustment. A fast charging credit application refers to a credit limit adjustment application submitted by a customer to a financial institution for fast approval and loan disbursement services. The fast charging credit whitelist refers to a list of customers who have submitted fast charging credit applications to financial institutions and meet the fast charging service conditions and have passed the approval. Customers on this customer list usually have good credit, excellent historical transaction records, and are entitled to fast approval and loan disbursement services.

[0115] Specifically, the first determination submodule is used to query whether the applicant exists in the fast charge quota whitelist based on the parsed identity information, that is, to query whether the applicant has submitted a fast charge quota application to a financial institution and has the authority to enjoy fast approval and loan services. Based on the parsed identity information, query whether the applicant exists in the fast charge quota application list, that is, to query whether the applicant is a customer who has submitted a fast charge quota application but has not yet completed the approval.

[0116] Specifically, the second determination submodule is used to query whether the applicant exists in the project whitelist of the target credit adjustment project based on the parsed identity information if it is found that the applicant does not exist in the fast charge credit whitelist or the fast charge credit application list. The project whitelist refers to a list of customers who meet the relevant project service conditions pre-screened by financial institutions for specific credit adjustment projects (such as target credit adjustment projects).

[0117] Specifically, the acquisition sub-module is used to access the database storing the credit limit qualification information of each customer based on the parsed identity information if it is found that the applicant customer exists in the project whitelist of the target credit limit adjustment project, indicating that the applicant customer meets the project service conditions of the target credit limit adjustment project, and filter out the credit limit qualification information of the identity information within a preset period from the database.

[0118] The acquisition module 401 of the embodiment of the present application can quickly identify eligible customers after receiving the customer's credit adjustment application by pre-screening and maintaining the fast charging credit whitelist, fast charging credit application list and project whitelist, without the need for real-time credit report query, which greatly shortens the processing time of credit adjustment and improves the overall business processing efficiency. By classifying and maintaining customers in different whitelists or lists, customers who have real needs for specific credit adjustment projects and have a strong willingness to adjust can be more accurately identified, which helps to better meet the personalized needs of customers.

[0119] In some optional implementations of this embodiment, the analysis module 402 may include an analysis submodule and a third determination submodule.

[0120] An information analysis submodule, used to analyze the consumption demand information of the applicant customer based on the historical behavior data;

[0121] The value determination submodule is used to determine the credit limit adjustment willingness value of the applicant customer based on the consumption demand information and the credit limit qualification information.

[0122] Specifically, the information analysis submodule is used to extract consumption demand information from historical behavior data, such as consumption frequency, consumption amount, consumption type, consumption cycle, consumption trend, etc.

[0123] Specifically, the value determination submodule is used to extract payment ability information from the credit limit qualification information, such as the current credit limit data, credit limit usage history, repayment record, etc. The prediction model is pre-trained by machine learning, and the consumption demand information extracted from the historical behavior data of the applicant customer and the payment ability information extracted from the credit limit qualification information of the applicant customer are input into the trained prediction model to calculate the credit limit adjustment willingness value of the applicant customer.

[0124] The analysis module 402 of the embodiment of the present application can accurately capture the customer's consumption demand information by analyzing the applicant's historical behavior data, and can accurately capture the customer's payment ability information by analyzing the credit limit qualification information. By using machine learning technology to build a prediction model, the prediction model can quickly and accurately predict the applicant's credit limit adjustment willingness value.

[0125] In some optional implementations of this embodiment, the above-mentioned value determination submodule may include a first quantization unit, a second quantization unit and a calculation unit.

[0126] A first quantization unit, used to obtain a first weight value of each first component element in the consumer demand information, and quantify the consumer demand information into a consumer demand value according to the first weight value;

[0127] A second quantization unit is used to obtain a second weight value of each second component element in the credit limit qualification information, and quantify the credit limit qualification information into a payment ability value according to the second weight value;

[0128] A calculation unit is used to calculate the credit limit adjustment willingness value of the applicant customer according to the consumption demand value and the payment ability value.

[0129] Specifically, the first quantification unit is used to determine each first component element contained in the consumption demand information of the applicant, which may include consumption frequency, consumption amount, consumption type, consumption cycle, consumption trend, etc., and assign a corresponding first weight value to each first component element, wherein the sum of the first weight values ​​of each first component element is 1 (or 100%). Perform data standardization on each first component element contained in the consumption demand information, and perform weighted sum calculation based on the value of each first component element after standardization and the first weight value assigned to each first component element to obtain the consumption demand value.

[0130] Specifically, the second quantification unit is used to determine each second component element included in the credit qualification information of the applicant, which second component element may include the current credit line data, credit line usage history, repayment record, etc., and assign a corresponding second weight value to each second component element, wherein the sum of the second weight values ​​of each second component element is 1 (or 100%). Perform data standardization processing on each second component element included in the credit qualification information, and perform weighted sum calculation based on the standardized value of each second component element and the second weight value assigned to each second component element to obtain the payment ability value.

[0131] Specifically, the calculation unit is used to perform calculations based on the consumption demand value and the payment ability value, for example, by comparing the consumption demand value with the payment ability value to calculate a difference or ratio. The difference between the consumption demand value and the payment ability value reflects the degree of mismatch between the two; the ratio between the consumption demand value and the payment ability value reflects the relative relationship between the two. The credit limit adjustment willingness value of the applicant customer is determined based on the calculated difference or ratio.

[0132] The analysis module 402 of the embodiment of the present application achieves the transition from qualitative description to quantitative analysis by obtaining the first weight value of each first component element in the consumer demand information and quantifying the consumer demand information into a consumer demand value accordingly; and by obtaining the second weight value of each second component element in the credit qualification information and quantifying the credit qualification information into a payment ability value accordingly. By combining the consumer demand value and the payment ability value, the credit adjustment willingness value of the applicant is calculated, and the quantitative analysis and comprehensive evaluation are performed using statistical methods, which can greatly improve the efficiency of decision-making.

[0133] In some optional implementations of this embodiment, the matching module 403 may include a comparison submodule, a third determination submodule, and a fourth determination submodule.

[0134] A comparison submodule, used to compare the credit limit adjustment willingness value with a preset threshold value to obtain a comparison result;

[0135] A third determination submodule is configured to determine the credit limit adjustment strategy as a large-amount long-term adjustment strategy if the comparison result is that the credit limit adjustment willingness value is greater than or equal to a preset threshold;

[0136] The fourth determination submodule is used to determine the credit limit adjustment strategy as a small-amount short-term adjustment strategy if the comparison result is that the credit limit adjustment willingness value is less than a preset threshold.

[0137] Specifically, the comparison submodule is used to compare the collected credit limit adjustment willingness values ​​with a preset threshold value to obtain a comparison result. The preset threshold value is used to classify the credit limit adjustment willingness values ​​so as to decide which adjustment strategy to adopt.

[0138] Specifically, the third determination submodule is used to determine the credit limit adjustment strategy as a large-scale long-term adjustment strategy when the comparison result is that the credit limit adjustment willingness value is greater than or equal to the preset threshold, indicating that the applicant customer hopes to make a larger credit limit adjustment or has a very strong adjustment willingness.

[0139] Specifically, the fourth determination submodule is used to determine the credit limit adjustment strategy as a small-amount short-term adjustment strategy when the comparison result is that the credit limit adjustment willingness value is less than the preset threshold, indicating that the applicant customer hopes to make a smaller credit limit adjustment, or the adjustment willingness is relatively mild.

[0140] The matching module 403 of the embodiment of the present application can more accurately match a credit limit adjustment strategy suitable for the customer by comparing the credit limit adjustment willingness value with a preset threshold.

[0141] In some optional implementations of this embodiment, the adjustment module 405 may include a fifth determination submodule and an adjustment submodule.

[0142] A fifth determination submodule, configured to, when detecting the payment request information of the applicant customer for the specific consumption item, determine the credit limit adjustment range of the applicant customer according to the payment request information and the credit limit adjustment strategy;

[0143] The adjustment submodule is used to adjust the credit limit data of the applicant customer to the target credit limit data according to the credit limit adjustment range.

[0144] Specifically, the fifth determination submodule is used to set a monitoring mechanism for the applicant customer, and is used to track and record the applicant customer's request payment information for a specific consumption item, and the request payment information may include the payment item type, payment amount, payment time, etc. When the applicant customer's request payment information is detected, the request payment information is matched with the preset specific consumption item. When the consumption behavior is successfully matched with the specific consumption item, the applicant customer's credit limit adjustment range is determined according to the credit limit adjustment strategy confirmed by the applicant customer and the request payment information. The credit limit adjustment range may be a fixed value or a preset ratio based on the request payment amount.

[0145] Specifically, the adjustment submodule is used to adjust the credit limit data of the applicant customer from the current credit limit value to the target credit limit value according to the credit limit adjustment range.

[0146] The adjustment module 405 of the embodiment of the present application can quickly respond to the consumption needs of the customer by detecting the payment request information of the applicant in real time or regularly, and automatically adjust the credit limit data according to the credit limit adjustment strategy. This dynamic adjustment mechanism ensures that the customer can obtain sufficient payment credit when needed, and improves the flexibility and response speed of the service.

[0147] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0148] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 6 with a memory 61, a processor 62, and a network interface 63, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0149] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0150] The memory 61 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 61 can be an internal storage unit of the computer device 6, such as a hard disk or memory of the computer device 6. In other embodiments, the memory 61 can also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 61 can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions of the credit limit data adjustment method, etc. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or are to be output.

[0151] The processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip in some embodiments. The processor 62 is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to run computer-readable instructions or process data stored in the memory 61, such as computer-readable instructions for running the credit limit data adjustment method.

[0152] The network interface 63 may include a wireless network interface or a wired network interface. The network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0153] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned quota data adjustment method.

[0154] The computer device, computer-readable storage medium and computer-readable instructions provided by the embodiment of the present application, when detecting the application signal for the target credit adjustment project sent by the client, obtain the credit qualification information and historical behavior data of the applicant customer through the processor execution, analyze the credit adjustment willingness value of the applicant customer according to the credit qualification information and historical behavior data, and can clarify the customer's real demand for credit adjustment, while avoiding frequent credit inquiries caused by the customer's unconscious operation, reducing the bank's operating costs; by matching the corresponding credit adjustment strategy according to the credit adjustment willingness value, and generating authorization confirmation information according to the credit adjustment strategy for the customer to make a decision, it is ensured that the customer agrees and accepts the credit adjustment strategy, and improves the accuracy and rationality of the credit adjustment; when detecting the customer's consumption behavior for a specific consumption project, the customer's target credit data is adjusted according to the credit adjustment strategy, and the credit adjustment can be automatically reached based on the customer's actual consumption behavior, enhancing the customer's consumption experience. The present application scheme meets the customer's consumption needs through a flexible credit adjustment strategy, promotes the development of banking business and revenue growth, and at the same time, reduces the risk of potential customer loss due to credit restrictions.

[0155] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0156] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to replace some of the technical features therein with equivalents. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

[0157] The non-Company software tools or components appearing in the embodiments of this application are merely examples and do not represent actual use.

Claims

1. A method for adjusting quota data, characterized in that: The steps include: When an application signal for a target credit limit adjustment item sent by a client is detected, the credit limit qualification information of the applicant within a preset period is obtained according to the application signal; Acquire the historical behavior data of the applicant, and analyze and obtain the credit limit adjustment willingness value of the applicant based on the historical behavior data and the credit limit qualification information; Matching a credit limit adjustment policy according to the credit limit adjustment willingness value, generating authorization confirmation information according to the credit limit adjustment policy and sending it to the client; When receiving an acceptance signal for the authorization confirmation information returned by the client, the specific consumption item to be detected is obtained by parsing the target quota adjustment item; When the consumption behavior of the applicant customer for the specific consumption item is detected, the target credit limit data of the applicant customer is adjusted according to the credit limit adjustment strategy.

2. The method for adjusting the quota data according to claim 1, characterized in that: The step of obtaining the credit limit qualification information of the applicant within a preset period according to the application signal includes: Obtaining identity information of the applicant according to the application signal analysis; Determine whether the applicant is in the fast charge quota whitelist or the fast charge quota application list according to the identity information; If the applicant does not exist in the fast charge quota whitelist and the fast charge quota application list, determining whether the applicant exists in the project whitelist of the target quota adjustment project according to the identity information; If the applicant is in the project whitelist, the credit limit qualification information of the applicant within a preset period is obtained based on the identity information.

3. The method for adjusting the quota data according to claim 1, characterized in that: The step of analyzing and obtaining the credit limit adjustment willingness value of the applicant customer according to the historical behavior data and the credit limit qualification information comprises: Analyze the consumption demand information of the applicant customer according to the historical behavior data; The credit limit adjustment willingness value of the applicant customer is determined according to the consumption demand information and the credit limit qualification information.

4. The method for adjusting the quota data according to claim 3, characterized in that: The step of determining the credit limit adjustment willingness value of the applicant customer according to the consumption demand information and the credit limit qualification information includes: Acquire a first weight value of each first component element in the consumer demand information, and quantify the consumer demand information into a consumer demand value according to the first weight value; Acquire a second weight value of each second component element in the credit limit qualification information, and quantify the credit limit qualification information into a payment ability value according to the second weight value; The credit limit adjustment willingness value of the applicant customer is calculated based on the consumption demand value and the payment ability value.

5. The method for adjusting the quota data according to claim 1, characterized in that: The step of matching the credit limit adjustment strategy according to the credit limit adjustment willingness value includes: Compare the credit limit adjustment willingness value with a preset threshold value to obtain a comparison result; If the comparison result is that the credit limit adjustment willingness value is greater than or equal to the preset threshold, the credit limit adjustment strategy is determined as a large-amount long-term adjustment strategy; If the comparison result is that the credit limit adjustment willingness value is less than the preset threshold, the credit limit adjustment strategy is determined as a small-amount short-term adjustment strategy.

6. The method for adjusting the quota data according to claim 1, characterized in that: The step of adjusting the target credit limit data of the applicant customer according to the credit limit adjustment strategy when the consumption behavior of the applicant customer for the specific consumption item is detected includes: When the payment request information of the applicant customer for the specific consumption item is detected, determining the credit limit adjustment range of the applicant customer according to the payment request information and the credit limit adjustment strategy; The credit limit data of the applicant customer is adjusted to the target credit limit data according to the credit limit adjustment range.

7. A quota data adjustment device, characterized in that: The device comprises: An acquisition module, configured to acquire the credit limit qualification information of the applicant within a preset period according to the application signal when an application signal for a target credit limit adjustment item sent by the client is detected; An analysis module, used to obtain the historical behavior data of the applicant, and analyze and obtain the credit limit adjustment willingness value of the applicant based on the historical behavior data and the credit limit qualification information; A matching module, used for matching a credit limit adjustment policy according to the credit limit adjustment willingness value, generating authorization confirmation information according to the credit limit adjustment policy and sending it to the client; A parsing module, configured to, when receiving an acceptance signal for the authorization confirmation information returned by the client, parse the target quota adjustment item to obtain a specific consumption item that needs to be detected; The adjustment module is used to adjust the target credit limit data of the applicant customer according to the credit limit adjustment strategy when the consumption behavior of the applicant customer for the specific consumption item is detected.

8. The quota data adjustment device according to claim 7, characterized in that: The analysis module comprises: An information analysis submodule, used to analyze the consumption demand information of the applicant customer based on the historical behavior data; The value determination submodule is used to determine the credit limit adjustment willingness value of the applicant customer based on the consumption demand information and the credit limit qualification information.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the credit limit data adjustment method as claimed in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the credit limit data adjustment method according to any one of claims 1 to 6 are implemented.

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