Method and device for generating approval adjustment information, storage medium, and electronic device

Abstract

The present application provides a method and apparatus, storage medium, and electronic device for generating approval adjustment information. The method comprises: obtaining N sets of credit approval data; obtaining a target approval log for a target approval node from a credit approval system based on the N sets of credit approval data; determining approval efficiency data for the target approval node from the target approval log; and generating approval adjustment information for the target approval node using the approval efficiency data. This application solves the problem of the inability to timely optimize the approval process in related technologies, thereby improving approval efficiency.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computers, and specifically, to a method and device for generating approval adjustment information, a storage medium, and an electronic device. Background Art

[0002] Credit approval is a core business for banks and other financial institutions, involving multiple steps, including risk assessment, credit limit allocation, and approval decisions. Traditional credit approval relies on manual review and decision-making, which is inefficient and susceptible to subjective factors, resulting in long approval times, low accuracy, and a poor customer experience.

[0003] In addition, the approval process in relevant technologies lacks effective monitoring methods, making it impossible to improve approval efficiency in a timely manner. Summary of the Invention

[0004] The embodiments of the present application provide a method and device for generating approval adjustment information, a storage medium, and an electronic device, so as to at least solve the problem in the related art that the approval process cannot be optimized in a timely manner.

[0005] According to one embodiment of the present application, a method for generating approval adjustment information is provided, comprising: obtaining N groups of credit approval data, wherein the N groups of credit approval data include process data for approving credit applications of N users, and the N groups of credit approval data are all data for performing approval operations through M approval nodes in a credit approval system, and the above-mentioned N and the above-mentioned M are both natural numbers greater than 1; obtaining a target approval log of a target approval node from the above-mentioned credit approval system based on the N groups of credit approval data, wherein the above-mentioned target approval node is any node among the above-mentioned M approval nodes; determining approval efficiency data of the above-mentioned target approval node from the above-mentioned target approval log, wherein the above-mentioned approval efficiency data includes at least one of the following: approval time, approval pass rate, credit risk assessment data, evaluation data of the above-mentioned user, and feedback data of other approval nodes; generating approval adjustment information of the above-mentioned target approval node using the above-mentioned approval efficiency data, wherein the above-mentioned approval adjustment information is used to instruct the above-mentioned target approval node to adjust the process of performing the above-mentioned approval operation.

[0006] In an exemplary embodiment, before obtaining N groups of credit approval data, the method further includes: receiving credit requests sent by N users; parsing the user information and credit fund information included in the N credit requests to obtain N groups of initial credit request data; performing data processing operations on the N groups of initial credit request data to obtain N groups of credit request data, wherein the data processing operations include data verification operations and data format conversion operations, and the data verification operations include verification of data integrity and verification of data logic; inputting the N groups of credit request data into the credit approval system respectively, so as to perform the approval operations on the N groups of credit request data respectively through the M approval nodes; collecting process data when the M approval nodes perform the approval operations on the N groups of credit request data respectively through the data collection service provided in the approval system to obtain the N groups of credit approval data; converting the N groups of credit approval data into data in a target format, and storing them in a target database.

[0007] In an exemplary embodiment, obtaining N sets of credit approval data includes: receiving a node approval request, wherein the node approval request is used to request approval of the target approval node; responding to the node approval request, obtaining from a target database credit approval data that matches the target node information of the target approval node, the target approval process of the target node, and the approval time period included in the node approval request, to obtain N sets of the credit approval data.

[0008] In an exemplary embodiment, determining the approval efficiency data of the target approval node from the target approval log includes: converting the target approval log into a structured data format to obtain target approval data; extracting the approval process of the target approval node from the target approval data, and calculating the approval time from the approval process, wherein the approval time includes: approval start time, approval end time, and pause or waiting time during the approval process; calculating the average approval time using the approval start time, approval end time, and pause or waiting time during the approval process; collecting statistics on the approval result data of the target approval node from the target approval data, and calculating the ratio between the number of approved credit approval data in N groups and N from the approval result data to obtain the approval pass rate; extracting the risk score of the target approval node based on the user information and historical credit data of the user, and the historical repayment data of the user from the target approval data; and calculating the approval result data of the target approval node based on the user information and historical credit data of the user from the target approval data. The above-mentioned risk score and the above-mentioned historical repayment data calculate the proportion of credit overdue and / or credit default of the above-mentioned users, and determine the frequency of the credit risk warning mechanism triggered by the above-mentioned target approval node and the corresponding processing results after the above-mentioned credit risk warning mechanism is triggered; determine the credit risk assessment data based on the above-mentioned proportion and the above-mentioned processing results; extract the satisfaction score data and approval feedback data of the above-mentioned users from the above-mentioned target approval data, wherein the above-mentioned satisfaction score data is data collected from the approval questionnaire or user feedback system; quantify the above-mentioned satisfaction score data and approval feedback data to obtain the evaluation data of the above-mentioned users; obtain the feedback data of the upstream approval node and the feedback data of the downstream approval node of the above-mentioned target approval node from the above-mentioned target approval data to obtain the feedback data of other approval nodes, wherein the feedback data of the above-mentioned upstream approval node includes the preparation data transferred to the above-mentioned target approval node, and the feedback data of the above-mentioned downstream approval node includes the evaluation data of the approval decision made for the above-mentioned target approval node.

[0009] In an exemplary embodiment, after calculating the average approval time using the approval start time, the approval end time, and the pause or waiting time during the approval process, the method further includes: generating a distribution trend graph of the approval time; identifying the time delay in the approval process of the target approval node from the distribution trend graph, and marking the time delay.

[0010] In an exemplary embodiment, the approval adjustment information of the target approval node is generated using the approval efficiency data, including: determining the abnormality type of the target approval node according to the approval efficiency data; and generating the approval adjustment information of the target approval node according to the abnormality type.

[0011] In an exemplary embodiment, the abnormality type of the target approval node is determined based on the approval efficiency data, including: when the approval efficiency data includes approval time, and the average approval time in the approval time is greater than the preset approval time, and the fluctuation range of the approval time is greater than the preset fluctuation range, determining that the abnormality of the target approval node is a first abnormality type; when the approval efficiency data includes approval pass rate and credit risk assessment data, and there is an abnormality in the data correlation relationship between the approval pass rate and the credit risk assessment data, determining that the abnormality of the target approval node is a second abnormality type; when the approval efficiency data includes the user's evaluation data, and the probability that the user satisfaction in the evaluation data is greater than the preset satisfaction is less than the preset probability, determining that the abnormality of the target approval node is a third abnormality type; when the approval efficiency data includes feedback data from other approval nodes, and there is no correlation between the feedback data from the other approval nodes and the approval result of the target approval node, determining that the abnormality of the target approval node is a fourth abnormality type.

[0012] According to another embodiment of the present application, a device for generating approval adjustment information is provided, comprising: a first acquisition module for acquiring N credit approval data, wherein the N credit approval data include process data for approving credit applications of N users, and the N credit approval data are all data for performing approval operations through M approval nodes in a credit approval system, and the N and M are both natural numbers greater than 1; a second acquisition module for acquiring a target approval log of a target approval node from the credit approval system based on the N credit approval data, wherein the target approval node is any one of the M approval nodes; a determination module for determining approval efficiency data of the target approval node from the target approval log, wherein the approval efficiency data includes at least one of the following: approval time, approval pass rate, credit risk assessment data, evaluation data of the user, and feedback data of other approval nodes; a generation module for generating approval adjustment information of the target approval node using the approval efficiency data, wherein the approval adjustment information is used to instruct the target approval node to adjust the process of performing the approval operation.

[0013] According to another embodiment of the present application, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0014] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0015] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0016] Through this application, N sets of credit approval data matching the approval processing of N users' credit requests are obtained. Based on this data, the target approval log of the target approval node is obtained from the credit approval system to determine the average approval time, approval pass rate, the proportion of users with credit overdue payments and / or credit defaults, etc. The abnormality type of the target approval node is then determined based on this data, and approval adjustment information for the target approval node is generated according to the abnormality type. This solves the problem of the related art in which the approval process cannot be optimized in a timely manner, thereby achieving the effect of improving approval efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a hardware environment diagram of a method for generating approval adjustment information according to an embodiment of the present application;

[0018] Figure 2 is a flow chart of a method for generating approval adjustment information according to an embodiment of the present application;

[0019] Figure 3 This is a flow chart of a method for generating approval adjustment information of a credit approval system according to an embodiment of the present application;

[0020] Figure 4 This is a structural block diagram of a device for generating approval adjustment information according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] It should be noted that the terms "first", "second", etc. in the description and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0023] The method embodiments provided in the embodiments of the present application can be executed in a server device or a similar computing device. Taking running on a server device as an example, Figure 1 This is a hardware environment diagram of a method for generating approval adjustment information according to an embodiment of the present application. Figure 1 As shown, the server device may include one or more ( Figure 1Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the above-mentioned server device may also include a transmission device 106 for communication functions and an input and output device 108. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above server device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0024] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a method for generating approval adjustment information in an embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to a server device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0025] The transmission device 106 is used to receive or send data via a network. A specific example of the aforementioned network may include a wireless network provided by a communication provider of the server device. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0026] In this embodiment, a method for generating approval adjustment information is provided. Figure 2 This is a flow chart of a method for generating approval adjustment information according to an embodiment of the present application. Figure 2 As shown, the process includes the following steps:

[0027] Step S202: Acquire N sets of credit approval data, wherein the N sets of credit approval data include process data of approving credit applications of N users, and the N sets of credit approval data are data of approval operations performed by M approval nodes in the credit approval system, where N and M are both natural numbers greater than 1.

[0028] Optionally, this embodiment can be applied in a credit approval system, including but not limited to a blockchain-based credit approval system, a cloud-native architecture credit approval platform, and an edge computing-based instant approval system.

[0029] Optionally, in this embodiment, the credit approval data is process data of approving the user's credit application, including but not limited to: information of the approval node that performs the approval operation on the credit approval data, and the credit request corresponding to the credit approval data.

[0030] Optionally, the approval operations in this embodiment include, but are not limited to: data verification, credit assessment, decision on whether to approve the loan, determination of repayment conditions, risk detection, and early warning.

[0031] Step S204: obtaining a target approval log of a target approval node from the credit approval system based on the N sets of credit approval data, wherein the target approval node is any one of the M approval nodes;

[0032] Optionally, the target approval log in this embodiment is used to record the data generated by the target approval node when performing the approval operation, including but not limited to: approval start time, approval end time, pause or waiting time during the approval process, approval result data, user satisfaction score data and approval feedback data.

[0033] Step S206: Determine the approval efficiency data of the target approval node from the target approval log, wherein the approval efficiency data includes at least one of the following: approval time, approval pass rate, credit risk assessment data, user evaluation data, and feedback data from other approval nodes;

[0034] Optionally, the calculation method of the approval pass rate in this embodiment includes dividing the number of approved credits in N groups of credit approval data by N.

[0035] Step S208: generating approval adjustment information of the target approval node using the approval efficiency data, wherein the approval adjustment information is used to instruct the target approval node to adjust the process of executing the approval operation.

[0036] Optionally, the approval adjustment information in this implementation is used to adjust the process of approval operations, including but not limited to: process optimization suggestions, such as shortening the processing time of specific approval nodes and simplifying redundant steps in the approval process; decision rule adjustment, adjusting the parameters of the credit scoring model based on the approval pass rate and risk assessment data, and optimizing the approval decision rules; training and coaching needs, based on the performance of the approval personnel, making personalized training suggestions to improve the quality and efficiency of approval; improving the risk warning mechanism, optimizing the risk identification algorithm, reducing false alarms, and improving the accuracy and timeliness of warnings; improving the user feedback mechanism, improving the user feedback collection and processing process, and improving customer satisfaction and service levels.

[0037] Through the above steps, based on the N sets of credit approval data obtained, the approval log of the target approval node is obtained, and approval efficiency data is extracted from the approval log. Then, approval adjustment information is generated using the approval efficiency data, instructing the target approval node to optimize the approval process. This solves the problem of the related art of being unable to adjust the approval process in a timely manner, and achieves the effect of improving approval efficiency and reducing approval time.

[0038] In an exemplary embodiment, before obtaining N groups of credit approval data, the method further includes: receiving credit requests sent by N users; parsing the user information and credit fund information included in the N credit requests to obtain N groups of initial credit request data; performing data processing operations on the N groups of initial credit request data to obtain N groups of credit request data, wherein the data processing operations include data verification operations and data format conversion operations, and the data verification operations include verification of data integrity and verification of data logic; inputting the N groups of credit request data into the credit approval system respectively, so as to perform the approval operations on the N groups of credit request data respectively through the M approval nodes; collecting process data when the M approval nodes perform the approval operations on the N groups of credit request data respectively through the data collection service provided in the approval system to obtain the N groups of credit approval data; converting the N groups of credit approval data into data in a target format, and storing them in a target database.

[0039] Optionally, in this embodiment, user information includes but is not limited to basic personal information, such as name, ID number, contact information, residential address, occupation information, etc.; credit history, including the user's past credit record, repayment history, overdue or default information; financial information, including proof of income, asset list, bank statements, debt records, etc.; and some optional information, such as educational background, marital status, living conditions, etc.

[0040] Optionally, in this embodiment, the credit fund information includes but is not limited to loan type, such as personal loan, business loan, home loan, car loan, etc.; loan amount, the specific loan amount applied for by the user; loan purpose, the purpose of using the funds, such as for education, entrepreneurship, house purchase, car purchase, etc.; repayment method, including loan terms such as repayment period, repayment frequency, interest rate, etc.; and some additional conditions, such as whether there is insurance, whether there is a co-borrower, whether a mortgage is required, etc.

[0041] Optionally, in this embodiment, the verification of data integrity can be specifically: checking whether all required fields in each credit request are filled in, such as the borrower's name, ID number, contact information, income status, loan purpose, etc.; verifying whether the data conforms to the expected format, such as whether the ID number is 18 digits, whether the mobile phone number format is correct, whether the date format is unified, etc.; checking whether the data in different parts are consistent with each other, for example, whether the income shown in the income certificate matches the income stated by the borrower, and whether the total asset value in the financial data is consistent with the asset list; the system may cross-verify with external databases (such as credit bureaus, public records, etc.) to ensure that the information provided is consistent with data from third-party sources.

[0042] Optionally, in this embodiment, the data logic may be verified to include that the loan amount cannot exceed a certain multiple of income, the loan term should be appropriate to the loan type, and the user's declared income is much higher than the industry average. The system may mark it as an anomaly for further review.

[0043] Optionally, in this embodiment, the target format of the data includes but is not limited to: Extensible Markup Language (XML) files, JavaScript Object Notation (JSON), and table formats in a database.

[0044] Through the above steps, the user's credit request is received, the request data is parsed, data verification and format conversion are performed, the processed credit request data is input into the credit approval system, the approval operation is executed, and the approval process data is collected, converted into a unified format and stored, ensuring the accuracy and consistency of the credit approval data, and facilitating centralized management and analysis of credit approval data.

[0045] In an exemplary embodiment, obtaining N sets of credit approval data includes: receiving a node approval request, wherein the node approval request is used to request approval of the target approval node; responding to the node approval request, obtaining from a target database credit approval data that matches the target node information of the target approval node, the target approval process of the target node, and the approval time period included in the node approval request, to obtain N sets of the credit approval data.

[0046] Optionally, in this embodiment, the node approval request is used to request approval of the target approval node, specifically to check whether the review time of the target approval node is reasonable, whether the review conditions are compliant, etc.

[0047] Through the above steps, upon receiving an approval request for a specific approval node (corresponding to the above target approval node), the credit approval data matching the request information is retrieved from the database, and the approval information of the specific approval node can be accurately located and obtained.

[0048] In an exemplary embodiment, determining the approval efficiency data of the target approval node from the target approval log includes: converting the target approval log into a structured data format to obtain target approval data; extracting the approval process of the target approval node from the target approval data, and calculating the approval time from the approval process, wherein the approval time includes: approval start time, approval end time, and pause or waiting time during the approval process; calculating the average approval time using the approval start time, approval end time, and pause or waiting time during the approval process; collecting statistics on the approval result data of the target approval node from the target approval data, and calculating the ratio between the number of approved credit approval data in N groups and N from the approval result data to obtain the approval pass rate; extracting the risk score of the target approval node based on the user information and historical credit data of the user, and the historical repayment data of the user from the target approval data; and calculating the approval result data of the target approval node based on the user information and historical credit data of the user from the target approval data. The above-mentioned risk score and the above-mentioned historical repayment data calculate the proportion of credit overdue and / or credit default of the above-mentioned users, and determine the frequency of the credit risk warning mechanism triggered by the above-mentioned target approval node and the corresponding processing results after the above-mentioned credit risk warning mechanism is triggered; determine the credit risk assessment data based on the above-mentioned proportion and the above-mentioned processing results; extract the above-mentioned user's satisfaction score data and approval feedback data from the above-mentioned target approval data, wherein the above-mentioned satisfaction score data is data collected from the approval questionnaire or user feedback system; quantify the above-mentioned satisfaction score data and approval feedback data to obtain the evaluation data of the above-mentioned user; obtain the feedback data of the upstream approval node and the feedback data of the downstream approval node of the above-mentioned target approval node from the above-mentioned target approval data to obtain the feedback data of other approval nodes, wherein the feedback data of the above-mentioned upstream approval node includes the preparation data transferred to the above-mentioned target approval node, and the feedback data of the above-mentioned downstream approval node includes the evaluation data of the approval decision made for the above-mentioned target approval node.

[0049] Optionally, the approval efficiency data in this embodiment can be intuitively displayed in the approval interface in the form of a cockpit dashboard.

[0050] Optionally, in this embodiment, the average approval time is the ratio of the time consumed for performing approval operations on M credit requests at the target approval node to M, where M is a natural number greater than or equal to 1.

[0051] Optionally, the time consumed for the target approval node to perform the approval operation on the credit request in this embodiment is calculated using the approval start time and approval end time of the target approval node performing the approval operation on the credit request.

[0052] Optionally, the risk score in this embodiment can be obtained through a machine learning model or a risk scoring algorithm. User information and user historical credit data are input, and the user's risk score is obtained using a machine learning model or a risk scoring algorithm.

[0053] Optionally, in this embodiment, the step of calculating the user's credit overdue and / or credit default rate using the risk score and historical repayment data may include: analyzing historical repayment records to calculate the number of times the user failed to repay on time (overdue) or failed to repay the loan (default) within a certain period of time (e.g., the past year), as well as the corresponding total loan amount; then, based on the number of overdue or default times and the total loan amount, calculating each user's initial overdue and / or default rate. For example, if a user has three loans, one of which is overdue, the total loan amount is RMB 1 million, and the overdue amount is RMB 300,000, then the initial overdue rate is 30%. This rate is then combined with the user's risk score to assess the user's credit overdue and default risk level, and the final credit overdue and / or credit default rate is obtained. For example, for users with higher risk scores, the final credit overdue and / or credit default rate is increased accordingly based on the initial overdue rate obtained after analyzing the historical repayment records.

[0054] Optionally, in this embodiment, the frequency of the target approval node triggering the credit risk warning mechanism is the ratio of the number of credit requests among M credit requests that trigger the credit risk warning mechanism when the target approval node performs the approval operation to M, where M is a natural number greater than or equal to 1.

[0055] Optionally, in this embodiment, the corresponding processing results after triggering the credit risk warning mechanism include but are not limited to approval decision adjustment, warning notification, and secondary approval. For example: if the risk warning mechanism is triggered, the system may require the approval personnel to re-examine the application, or automatically adjust the approval conditions, such as reducing the loan amount, increasing the interest rate, requiring additional guarantees, etc.

[0056] Optionally, in this embodiment, the feedback data from the upstream approval node includes preparation data to be transferred to the target approval node, including but not limited to user fund verification operations, user loan limit confirmation, etc.

[0057] Optionally, in this embodiment, the feedback data from the downstream approval node includes evaluation data on the approval decision made by the target approval node, including but not limited to decision confirmation or adjustment (the downstream approval node may confirm the decision of the target approval node or propose decision adjustment suggestions based on subsequent review findings), risk review results (the downstream approval node may conduct a secondary risk assessment to ensure that the risk of the loan issuance decision is controllable), loan conditions and terms (based on the decision of the target approval node, the downstream approval node may set specific loan conditions, such as loan issuance time, loan amount, loan interest rate, etc.), and subsequent process guidance (the downstream approval node may provide guidance on subsequent credit processes, such as instructing the borrower on how to repay the loan after the loan is issued, providing loan management services, etc.). For example, if the target approval node (e.g., the loan approval node) decides to approve a user's loan application, the downstream approval node (e.g., the loan management node) may assess the risk of the decision and set loan issuance conditions, such as requiring the user to complete all loan issuance procedures by a specific date, otherwise a reassessment will be required.

[0058] Through the above steps, the target approval log is converted into structured data, and approval efficiency data such as approval time, approval rate, risk assessment, user evaluation and other node feedback are extracted. Then, various indicators such as average approval time, approval rate, credit risk ratio, user satisfaction, etc. are calculated, providing a data basis for quantitative evaluation of approval efficiency and providing a basis for subsequent identification of potential problems and improvement points in the approval process.

[0059] In an exemplary embodiment, after calculating the average approval time using the approval start time, the approval end time, and the pause or waiting time during the approval process, the method further includes: generating a distribution trend graph of the approval time; identifying the time delay in the approval process of the target approval node from the distribution trend graph, and marking the time delay.

[0060] Optionally, in this embodiment, the distribution trend graph of the approval time may be displayed in the form of a line graph, a scatter plot, and a box plot. For example, the approval time is arranged in chronological order, and the approval time data corresponding to each time point is connected with a broken line; the approval time data is distributed in the form of points in a coordinate system; and a five-number summary of the approval time data (minimum value, lower quartile, median, upper quartile, maximum value) can be displayed through a box plot.

[0061] Through the above steps, we can analyze the distribution trend of approval time, identify time delays in the approval process, and mark the identified time delays. This will enable us to accurately locate the links where approval time delays occur, facilitate subsequent targeted adjustments, and optimize the approval process.

[0062] In an exemplary embodiment, the approval adjustment information of the target approval node is generated using the approval efficiency data, including: determining the abnormality type of the target approval node according to the approval efficiency data; and generating the approval adjustment information of the target approval node according to the abnormality type.

[0063] Optionally, in this embodiment, the exception types include but are not limited to approval time exceptions, decision risk exceptions, approval node allocation exceptions, rule execution exceptions, and user experience exceptions.

[0064] Optionally, in this embodiment, targeted training can be provided to relevant approvers based on the type of anomaly. For example, those with slow approval speeds may require process familiarity training, while those with high error rates may require compliance and risk awareness training. At the same time, a system can be built that can recommend appropriate training resources based on the specific needs of approvers. The recommendation strategy can be adaptively adjusted based on the approver's historical participation and personal interests, generating a personalized training plan for each approver, including course selection, learning paths, and expected goals. This training plan can be adjusted based on changes in the approver's performance after completing the training. After the training is implemented, the effectiveness of the training can be further determined by determining whether there are anomalies at the target approval node, allowing for further real-time adjustments to the training plan.

[0065] Through the above steps, the types of exceptions in the approval process are determined based on the approval efficiency data, and then approval adjustment information is generated for different exception types, which enables precise adjustment of the approval process and targeted solution of problems. By adjusting the approval process, the approval efficiency and quality are further improved.

[0066] In an exemplary embodiment, the abnormality type of the target approval node is determined based on the approval efficiency data, including: when the approval efficiency data includes approval time, and the average approval time in the approval time is greater than the preset approval time, and the fluctuation range of the approval time is greater than the preset fluctuation range, determining that the abnormality of the target approval node is a first abnormality type; when the approval efficiency data includes approval pass rate and credit risk assessment data, and there is an abnormality in the data correlation relationship between the approval pass rate and the credit risk assessment data, determining that the abnormality of the target approval node is a second abnormality type; when the approval efficiency data includes the user's evaluation data, and the probability that the user satisfaction in the evaluation data is greater than the preset satisfaction is less than the preset probability, determining that the abnormality of the target approval node is a third abnormality type; when the approval efficiency data includes feedback data from other approval nodes, and there is no correlation between the feedback data from the other approval nodes and the approval result of the target approval node, determining that the abnormality of the target approval node is a fourth abnormality type.

[0067] Optionally, in this embodiment, the first abnormality type of the target approval node may be an approval timeout abnormality, which indicates that the approval node may have an efficiency bottleneck, such as uneven task allocation among approval personnel, unreasonable approval process design, or system performance issues. For this first abnormality type, the approval adjustment information generated by the system may include: optimizing the approval node task allocation strategy, namely, by reallocating approval tasks to ensure balanced workload among approval personnel and avoid approval delays caused by task overload for a particular person; streamlining the approval process, namely, analyzing redundant steps in the approval process and simplifying or deleting unnecessary processes; and improving system performance, such as appropriately increasing server processing capacity, optimizing database query efficiency, or increasing bandwidth. For example, if the system monitors that the average approval time of credit assessment nodes is greater than a preset approval time, or that the fluctuation range of the approval time is greater than a preset fluctuation range, the system determines the amount of credit request data required to be processed by each approval node. If the amount of credit request data required to be processed by the approval nodes is unevenly distributed, the generated approval adjustment information may recommend distributing the workload, namely, when allocating approval nodes, prioritizing credit request data to those approval nodes that require less credit request data to be processed; or, based on the type of credit request data, allocating it to an approval node that requires less time to approve that type of credit request data.

[0068] Optionally, in this embodiment, the second anomaly type for the target approval node may be a mismatch between the approval decision and the risk assessment. That is, when the approval approval rate of the target approval node is abnormally high or low, while the risk assessment data is relatively stable, the system will determine this as the second anomaly type. This indicates that the approval decision is too loose or too strict, inconsistent with the risk assessment results, and may result in loan losses or the loss of high-quality customers. For the second anomaly type, the approval adjustment information that the system can generate includes but is not limited to adjusting the approval standards, that is, recommending adjustments to the approval standards to ensure that the decision matches the risk profile; optimizing the decision support model; and checking and adjusting the machine learning model or risk scoring algorithm to ensure that it accurately reflects credit risk. For example, if the approval approval rate of the target approval node is much higher than that of other nodes, but the risk score has not significantly improved, the relevant approval personnel can receive compliance and risk awareness training.

[0069] Optionally, in this embodiment, the third exception type for the target approval node could be a poor customer experience, which may be caused by factors such as lengthy approval times, poor communication, or a complex approval process, impacting the customer experience. For this third exception type, the generated approval adjustment information includes, but is not limited to, optimizing the user communication process, i.e., improving the communication channels between users and approval personnel, or increasing the frequency of communication with users; and improving the user interface and optimizing its design to streamline the loan application process. For example, if the target approval node exception is determined to be the third exception type, further analysis of user feedback data reveals that if user feedback repeatedly mentions cumbersome application procedures and the interface is prone to accidental touches, optimization of the user interface design may be considered.

[0070] Optionally, in this embodiment, the fourth abnormality type of the target approval node may be the failure of the information transmission and feedback mechanism between approval nodes, indicating the existence of information islands in the approval process, affecting the overall approval efficiency and quality. For the fourth abnormality type, the approval adjustment information that the system can generate includes but is not limited to data flow optimization, reviewing and optimizing data flow to ensure timely and accurate data transmission between approval nodes to avoid information delays or errors; approval process coordination, redesigning or adjusting the approval process to ensure that the decision-making basis of upstream and downstream approval nodes is interconnected, thereby improving the coordination and efficiency of the process. For example, in the case where it is determined that the abnormality of the target approval node is the fourth abnormality type, further detection determines that the actual problem is that the loan issuance node fails to adjust the loan conditions according to the risk score of the credit assessment node. The generated approval adjustment information is to recommend the addition of an automatic adjustment mechanism to the process design to dynamically adjust the loan interest rate, repayment method, etc. according to the credit score.

[0071] Through the above steps, abnormal judgments are made on approval time, pass rate and risk assessment, user evaluation, feedback from other approval nodes, etc., and the corresponding abnormality types are determined according to the abnormal situations of different data. This can more specifically identify problems in the approval process. By classifying abnormality types, more accurate and effective process adjustment strategies can be implemented.

[0072] The above method is described below with a specific example. Figure 3 This is a flow chart of a method for generating approval adjustment information of a credit approval system implemented according to this application, such as Figure 3 As shown in the figure, a commercial bank is using a credit approval system based on big data and artificial intelligence. The system is designed with multiple approval nodes, including customer information verification, credit scoring, loan amount assessment, compliance check and final approval.

[0073] Step S302: Receive a node approval request, wherein the node approval request is used to request approval of the "credit score" node (i.e., the target approval node mentioned above);

[0074] Step S304: Obtain credit approval data from the target database, and obtain the target approval log of the "Credit Score" node from the credit approval system based on the credit approval data. The target approval log includes N approval records that passed through the "Credit Score" node in the past 30 days, where N = 1200 (i.e., 1200 approval cases are collected);

[0075] Step S306: The target approval log is structured and converted into a database format suitable for analysis. During this process, the system checks the data integrity to confirm that there are no missing fields and standardizes all data to ensure a consistent format for subsequent analysis.

[0076] In step S308, the approval efficiency data of the "Credit Score" node is extracted from the structured data, including: approval time and approval pass rate. Analysis shows that the average approval time of the "Credit Score" node reaches 70 minutes, which is much higher than the preset standard of 45 minutes, and the fluctuation range is also large; the approval pass rate is 75%, and the difference from the approval rate predicted by the risk assessment model (72%) meets the threshold (within 5% error), indicating that the decision matches the risk assessment.

[0077] Step S310: Use the approval efficiency data to generate approval adjustment information for the "Credit Score" node. Based on the above analysis results, determine that the "Credit Score" node is the first abnormal type, and generate approval adjustment information: increase the number of approvers for the "Credit Score" node, optimize the approval algorithm to increase the processing speed, and at the same time, train the approvers of this node so that they can perform the review operations proficiently.

[0078] Through the above steps, banks can promptly identify efficiency bottlenecks and decision-making deviations in the credit approval process. By generating and implementing approval adjustment information, they can effectively improve the quality and efficiency of approvals and enhance the user experience.

[0079] It should be noted that, through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this 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 enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0080] This embodiment also provides a device for generating approval adjustment information, which is used to implement the above-mentioned embodiments and preferred implementations. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0081] Figure 4 This is a structural block diagram of a device for generating approval adjustment information according to an embodiment of the present application. Figure 4 As shown, the device includes:

[0082] A first acquisition module 402 is configured to acquire N credit approval data, wherein the N credit approval data include process data of approving credit applications of N users, and the N credit approval data are data of approval operations performed by M approval nodes in a credit approval system, and both N and M are natural numbers greater than 1;

[0083] A second acquisition module 404 is configured to acquire a target approval log of a target approval node from the credit approval system based on the N credit approval data, wherein the target approval node is any one of the M approval nodes;

[0084] Determination module 406 is configured to determine approval efficiency data of the target approval node from the target approval log, wherein the approval efficiency data includes at least one of the following: approval time, approval pass rate, credit risk assessment data, user evaluation data, and feedback data from other approval nodes;

[0085] The generating module 408 is configured to generate approval adjustment information of the target approval node using the approval efficiency data, wherein the approval adjustment information is configured to instruct the target approval node to adjust the process of executing the approval operation.

[0086] In an exemplary embodiment, the first acquisition module 402 further includes: receiving credit requests sent by N users; parsing the user information and credit fund information included in the N credit requests to obtain N groups of initial credit request data; performing data processing operations on the N groups of initial credit request data to obtain N groups of credit request data, wherein the data processing operations include data verification operations and data format conversion operations, and the data verification operations include verification of data integrity and verification of data logic; inputting the N groups of credit request data into the credit approval system respectively, so as to perform the approval operations on the N groups of credit request data respectively through the M approval nodes; collecting process data when the M approval nodes perform the approval operations on the N groups of credit request data respectively through the data collection service set in the approval system to obtain the N groups of credit approval data; converting the N groups of credit approval data into data in a target format, and storing them in a target database.

[0087] In an exemplary embodiment, the first acquisition module 402 further includes: a first receiving unit for receiving a node approval request, wherein the node approval request is used to request approval of the target approval node; a first responding unit for responding to the node approval request, obtaining from the target database credit approval data that matches the target node information of the target approval node, the target approval process of the target node, and the approval time period included in the node approval request, to obtain N groups of the credit approval data.

[0088] In an exemplary embodiment, the determination module 406 further includes: a first conversion unit for converting the target approval log into a structured data format to obtain target approval data; a first extraction unit for extracting the approval process of the target approval node from the target approval data, and calculating the approval time from the approval process, wherein the approval time includes: approval start time, approval end time, and pause or waiting time during the approval process; a first calculation unit for calculating the average approval time using the approval start time, approval end time, and pause or waiting time during the approval process; a second calculation unit for counting the approval result data of the target approval node from the target approval data, and calculating the ratio between the number of approved credit approval data in N groups and N from the approval result data to obtain the approval pass rate; a second extraction unit for extracting the risk score of the target approval node based on the user information and historical credit data of the user, and the historical repayment data of the user from the target approval data; a third calculation unit for calculating the approval result data of the target approval node using the target approval data; The above-mentioned risk score and the above-mentioned historical repayment data calculate the proportion of credit overdue and / or credit default of the above-mentioned users, and determine the frequency of the above-mentioned target approval node triggering the credit risk early warning mechanism and the corresponding processing result after the above-mentioned credit risk early warning mechanism is triggered; the first determination unit is used to determine the credit risk assessment data based on the above-mentioned proportion and the above-mentioned processing result; the third extraction unit is used to extract the above-mentioned user's satisfaction score data and approval feedback data from the above-mentioned target approval data, wherein the above-mentioned satisfaction score data is data collected from the approval questionnaire or user feedback system; the first quantification unit is used to quantify the above-mentioned satisfaction score data and approval feedback data to obtain the evaluation data of the above-mentioned user; the first acquisition unit is used to obtain the feedback data of the upstream approval node and the feedback data of the downstream approval node of the above-mentioned target approval node from the above-mentioned target approval data to obtain the feedback data of other approval nodes, wherein the feedback data of the above-mentioned upstream approval node includes the preparation data transferred to the above-mentioned target approval node, and the feedback data of the above-mentioned downstream approval node includes the evaluation data of the approval decision made for the above-mentioned target approval node.

[0089] In an exemplary embodiment, the above-mentioned determination module 406 also includes: a first generation unit, used to generate a distribution trend graph of approval time; a first identification unit, used to identify the time delay in the approval process of the above-mentioned target approval node from the above-mentioned distribution trend graph, and mark the above-mentioned time delay.

[0090] In an exemplary embodiment, the generation module 408 further includes: a second determination unit for determining the exception type of the target approval node according to the approval efficiency data; and a second generation unit for generating approval adjustment information of the target approval node according to the exception type.

[0091] In an exemplary embodiment, the generation module 408 further includes: a third determination unit for determining that the abnormality of the target approval node is a first abnormality type when the approval efficiency data includes approval time, and the average approval time in the approval time is greater than a preset approval time, and the fluctuation range of the approval time is greater than a preset fluctuation range; a fourth determination unit for determining that the abnormality of the target approval node is a second abnormality type when the approval efficiency data includes approval pass rate and credit risk assessment data, and there is an abnormality in the data correlation relationship between the approval pass rate and the credit risk assessment data; a fifth determination unit for determining that the abnormality of the target approval node is a third abnormality type when the approval efficiency data includes user evaluation data, and the probability that the user satisfaction in the evaluation data is greater than a preset satisfaction is less than a preset probability; and a sixth determination unit for determining that the abnormality of the target approval node is a fourth abnormality type when the approval efficiency data includes feedback data from other approval nodes, and there is no correlation between the feedback data from the other approval nodes and the approval result of the target approval node.

[0092] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0093] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any of the above method embodiments when run.

[0094] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0095] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0096] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0097] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0098] An embodiment of the present application further provides another computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above method embodiments are implemented.

[0099] An embodiment of the present application also provides a computer program, which includes computer instructions, which are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of any of the above method embodiments.

[0100] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0101] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0102] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for generating approval adjustment information, characterized in that: The method comprises: Obtaining N sets of credit approval data, wherein the N sets of credit approval data include process data of approving credit applications of N users, and the N sets of credit approval data are data of approval operations performed by M approval nodes in a credit approval system, where N and M are both natural numbers greater than 1; Obtaining a target approval log of a target approval node from the credit approval system according to the N groups of credit approval data, wherein the target approval node is any one of the M approval nodes; Determining approval efficiency data of the target approval node from the target approval log, wherein the approval efficiency data includes at least one of the following: approval time, approval pass rate, credit risk assessment data, user evaluation data, and feedback data from other approval nodes; Generating approval adjustment information of the target approval node using the approval efficiency data, wherein the approval adjustment information is used to instruct the target approval node to adjust the process of executing the approval operation; Determining the approval efficiency data of the target approval node from the target approval log includes: converting the target approval log into a structured data format to obtain target approval data; extracting the approval process of the target approval node from the target approval data, and calculating the approval time from the approval process, wherein the approval time includes: approval start time, approval end time, and pause or waiting time during the approval process; calculating the average approval time using the approval start time, the approval end time, and the pause or waiting time during the approval process; collecting statistics on the approval result data of the target approval node from the target approval data, and calculating the ratio between the number of approved credit approval data in N groups and N from the approval result data to obtain the approval pass rate; extracting the risk score made by the target approval node based on the user information and historical credit data of the user, and the historical repayment data of the user from the target approval data; using the risk score and the historical repayment data to calculate the proportion of credit overdue and / or credit default of the user, and determine the frequency of the target approval node triggering the credit risk warning mechanism and the corresponding processing result after the credit risk warning mechanism is triggered; determine the credit risk assessment data based on the proportion and the processing result; extract the user's satisfaction score data and approval feedback data from the target approval data, wherein the satisfaction score data is data collected from the approval questionnaire or user feedback system; quantify the satisfaction score data and approval feedback data to obtain the user's evaluation data; obtain the feedback data of the upstream approval node and the feedback data of the downstream approval node of the target approval node from the target approval data to obtain the feedback data of other approval nodes, wherein the feedback data of the upstream approval node includes the preparation data transferred to the target approval node, and the feedback data of the downstream approval node includes the evaluation data of the approval decision made for the target approval node.

2. The method according to claim 1, characterized in that Before obtaining N sets of credit approval data, the method further includes: Receive credit requests from N users; Parsing the user information and credit fund information included in the N credit requests to obtain N sets of credit request initial data; Performing data processing operations on the N sets of initial credit request data to obtain N sets of credit request data, wherein the data processing operations include data verification operations and data format conversion operations, and the data verification operations include verification of data integrity and verification of data logic; Inputting N groups of credit request data into the credit approval system respectively, so as to perform the approval operation on the N groups of credit request data respectively through the M approval nodes; collecting, by means of a data collection service provided in the approval system, process data when the M approval nodes respectively perform the approval operations on the N groups of credit request data, to obtain N groups of credit approval data; The N groups of credit approval data are converted into data in a target format and stored in a target database.

3. The method according to claim 1, characterized in that Obtain N sets of credit approval data, including: receiving a node approval request, wherein the node approval request is used to request approval of the target approval node; In response to the node approval request, credit approval data that matches the target node information of the target approval node, the target approval process of the target node, and the approval time period included in the node approval request is obtained from the target database to obtain N groups of credit approval data.

4. The method according to claim 1, wherein After calculating the average approval time using the approval start time, the approval end time, and the pause or waiting time during the approval process, the method further includes: Generate a distribution trend chart of approval time; A time delay in the approval process of the target approval node is identified from the distribution trend graph, and the time delay is marked.

5. The method according to claim 1, wherein Generating approval adjustment information of the target approval node using the approval efficiency data includes: Determining the abnormality type of the target approval node according to the approval efficiency data; Approval adjustment information of the target approval node is generated according to the exception type.

6. The method according to claim 5, characterized in that Determining the abnormality type of the target approval node according to the approval efficiency data includes: When the approval efficiency data includes approval time, and the average approval time in the approval time is greater than the preset approval time, and the fluctuation range of the approval time is greater than the preset fluctuation range, determining that the abnormality of the target approval node is a first abnormality type; When the approval efficiency data includes approval pass rate and credit risk assessment data, and there is an abnormality in the data association relationship between the approval pass rate and the credit risk assessment data, determining that the abnormality of the target approval node is a second abnormality type; When the approval efficiency data includes the user's evaluation data, and the probability that the user satisfaction in the evaluation data is greater than a preset satisfaction is less than a preset probability, determining that the abnormality of the target approval node is a third abnormality type; When the approval efficiency data includes feedback data from other approval nodes, and there is no correlation between the feedback data from other approval nodes and the approval result of the target approval node, the abnormality of the target approval node is determined to be a fourth abnormality type.

7. A device for generating approval adjustment information, characterized in that: include: A first acquisition module is configured to acquire N credit approval data, wherein the N credit approval data include process data of approving credit applications of N users, and the N credit approval data are data of approval operations performed by M approval nodes in a credit approval system, and both N and M are natural numbers greater than 1; a second acquisition module, configured to acquire a target approval log of a target approval node from the credit approval system according to the N credit approval data, wherein the target approval node is any one of the M approval nodes; a determination module, configured to determine approval efficiency data of the target approval node from the target approval log, wherein the approval efficiency data includes at least one of the following: approval time, approval pass rate, credit risk assessment data, user evaluation data, and feedback data from other approval nodes; a generating module, configured to generate approval adjustment information of the target approval node using the approval efficiency data, wherein the approval adjustment information is used to instruct the target approval node to adjust a process for executing the approval operation; The determination module also includes: a first conversion unit for converting the target approval log into a structured data format to obtain target approval data; a first extraction unit for extracting the approval process of the target approval node from the target approval data, and calculating the approval time from the approval process, wherein the approval time includes: approval start time, approval end time, pause or waiting time during the approval process; a first calculation unit for calculating the average approval time using the approval start time, the approval end time, and the pause or waiting time during the approval process; a second calculation unit for counting the approval result data of the target approval node from the target approval data, and calculating the ratio between the number of approved credit approval data in N groups and N from the approval result data to obtain the approval pass rate; a second extraction unit for extracting the risk score of the target approval node based on the user information and historical credit data of the user, and the historical repayment data of the user from the target approval data; a third calculation unit for calculating the average approval time using the risk score and The historical repayment data is used to calculate the proportion of credit overdue and / or credit default of the user, and to determine the frequency of the target approval node triggering the credit risk early warning mechanism and the corresponding processing result after the credit risk early warning mechanism is triggered; a first determination unit is used to determine credit risk assessment data based on the proportion and the processing result; a third extraction unit is used to extract the user's satisfaction score data and approval feedback data from the target approval data, wherein the satisfaction score data is data collected from an approval questionnaire or a user feedback system; a first quantification unit is used to quantify the satisfaction score data and approval feedback data to obtain the user's evaluation data; a first acquisition unit is used to acquire feedback data of the upstream approval node and the downstream approval node of the target approval node from the target approval data to obtain feedback data of other approval nodes, wherein the feedback data of the upstream approval node includes preparation data transferred to the target approval node, and the feedback data of the downstream approval node includes evaluation data of the approval decision made for the target approval node.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 6 when executed by a processor.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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