CN119762213B - Approval adjustment information generation method and device, storage medium and electronic equipment - Google Patents
Approval adjustment information generation method and device, storage medium and electronic equipmentInfo
- Publication number
- CN119762213B CN119762213B CN202411832495.4A CN202411832495A CN119762213B CN 119762213 B CN119762213 B CN 119762213B CN 202411832495 A CN202411832495 A CN 202411832495A CN 119762213 B CN119762213 B CN 119762213B
- Authority
- CN
- China
- Prior art keywords
- approval
- data
- target
- node
- credit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application provides a method and a device for generating approval adjustment information, a storage medium and electronic equipment, wherein the method comprises the steps of obtaining N groups of credit approval data, obtaining a target approval log of a target approval node from a credit approval system according to the N groups of credit approval data, determining approval efficiency data of the target approval node from the target approval log, and generating the approval adjustment information of the target approval node by utilizing the approval efficiency data. The application solves the problem that the approval process cannot be optimized in time in the related technology, and achieves the effect of improving the approval efficiency.
Description
Technical Field
The embodiment of the application relates to the field of computers, in particular to a method and a device for generating approval adjustment information, a storage medium and electronic equipment.
Background
The credit approval business is one of core businesses of financial institutions such as banks, and relates to a plurality of links such as risk assessment, credit allocation, approval decision-making and the like. Traditional credit approval relies on manual inspection and decision making, is low in efficiency and is easily influenced by subjective factors, so that the approval time is long, the accuracy is low, and the customer experience is poor.
In addition, the approval process in the related art lacks an effective monitoring means, and the approval efficiency cannot be improved in time.
Disclosure of Invention
The embodiment of the application provides a method and a device for generating approval adjustment information, a storage medium and electronic equipment, which at least solve the problem that the approval process cannot be optimized in time in the related technology.
According to one embodiment of the application, a method for generating approval adjustment information is provided, which comprises the steps of obtaining N groups of credit approval data, wherein the N groups of credit approval data comprise approval process data for approving credit applications of N users, the N groups of credit approval data are all data for executing approval operations through M approval nodes in a credit approval system, the N and the M are natural numbers larger 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, the target approval node is any node of the M approval nodes, determining approval efficiency data of the target approval node from the target approval log, wherein the approval efficiency data comprise at least one of approval time, approval passing rate, credit risk assessment data, evaluation data of the users and feedback data of other approval nodes, and generating the target approval log of the target approval node according to the N groups of credit approval data, wherein the target approval node is any node of the M approval efficiency data, and the target approval efficiency data are used for executing the adjustment information of the target approval node.
In an exemplary embodiment, before acquiring the N sets of credit approval data, the method further includes receiving a credit request sent by N users, analyzing user information and credit fund information included in the N sets of credit request to obtain N sets of credit request initial data, performing a data processing operation on the N sets of credit request initial data to obtain N sets of credit request data, where the data processing operation includes a data verification operation and a data format conversion operation, the data verification operation includes verification of data integrity and verification of data logic, inputting the N sets of credit request data into the credit approval system respectively, to perform the approval operation on the N sets of credit request data through M approval nodes respectively, acquiring process data when the M approval nodes perform the approval operation on the N sets of credit request data respectively, to obtain N sets of credit approval data, converting the N sets of credit approval data into data in a target format, and storing the data in a target database.
In an exemplary embodiment, acquiring N groups of credit approval data comprises receiving a node approval request, wherein the node approval request is used for requesting approval of the target approval node, and responding to the node approval request, acquiring the credit approval data matched with target node information of the target approval node, target approval flow of the target node and approval time period included in the node approval request from a target database to obtain N groups of the credit approval data.
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 comprises approval start time, approval end time, pause or waiting time in the approval process, calculating average approval time by using the approval start time, the approval end time and the pause or waiting time in the approval process, counting approval result data of the target approval node from the target approval data, calculating the ratio of the number of through approval to the number of N in the approval result data, obtaining approval passing rate, extracting risk scores made by the target approval node based on user information of the user and historical credit data of the user from the target approval data, calculating the proportion of the occurrence of credit and/or approval of the user by using the risk scores and the historical credit data, determining the proportion of the approval by using the risk scores and the historical credit data, and triggering the approval result, determining the risk score mechanism based on the corresponding to the target approval result, determining the risk score and the risk score mechanism based on the risk score and the risk score, the satisfaction degree grading data are data collected from an approval questionnaire or a user feedback system, the satisfaction degree grading data and approval feedback data are quantitatively processed to obtain the user evaluation data, feedback data of an upstream approval node and feedback data of a downstream approval node of the target approval node are obtained from the target approval data, and feedback data of other approval nodes are obtained, wherein the feedback data of the upstream approval node comprise preparation data which flows to the target approval node, and the feedback data of the downstream approval node comprise evaluation data of an approval decision made by the target approval node.
In an exemplary embodiment, after calculating the average approval time by using the approval start time, the approval end time, and the pause or wait time in the approval process, the method further includes generating a distribution trend chart of the approval time, identifying a time delay in the approval process of the target approval node from the distribution trend chart, and marking the time delay.
In an exemplary embodiment, generating the approval adjustment information of the target approval node by using the approval efficiency data includes determining an exception 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 exception type.
In one exemplary embodiment, determining the abnormal type of the target approval node according to the approval efficiency data includes determining that the abnormality of the target approval node is a first abnormal type when the approval efficiency data includes approval time and the average approval time in the approval time is greater than preset approval time and the fluctuation range of the approval time is greater than preset fluctuation range, determining that the abnormality of the target approval node is a second abnormal type when the approval efficiency data includes approval passing rate and credit risk assessment data and a data association relationship between the approval passing rate and the credit risk assessment data is abnormal, determining that the abnormality of the target approval node is a third abnormal type when the approval efficiency data includes evaluation data of the user and the probability of user satisfaction in the evaluation data is less than the preset probability and determining that the abnormality of the target approval node is a fourth abnormal type when the approval efficiency data includes feedback data of other approval nodes and a data association relationship between the target approval node and the target approval node is abnormal.
According to another embodiment of the application, a device for generating approval adjustment information is provided, which comprises a first acquisition module, a second acquisition module and a determining module, wherein the first acquisition module is used for acquiring N pieces of credit approval data, N pieces of credit approval data comprise process data for approving credit applications of N users, the N pieces of credit approval data are all data for executing approval operations through M pieces of approval nodes in a credit approval system, the N pieces of credit approval data and the M pieces of credit approval data are natural numbers larger than 1, the second acquisition module is used for acquiring a target approval log of a target approval node from the credit approval system according to the N pieces of credit approval data, the target approval node is any node of the M pieces of approval nodes, the determining module is used for determining the approval efficiency data of the target approval node from the target approval log, the approval efficiency data comprise at least one of approval time, approval rate, credit risk assessment data and approval efficiency data of the users, and feedback data of other approval nodes are used for generating the target approval nodes, and the target approval adjustment information is used for executing the target adjustment information.
According to a further embodiment of the application, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of any of the method embodiments described above.
According to a further embodiment of the present application, there is also provided a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
According to a further embodiment of the application, there is also provided an electronic device comprising a memory having stored therein a computer program, and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
According to the application, N groups of credit approval data matched with the approval processing of the credit requests of N users are obtained, the target approval log of the target approval node is obtained from a credit approval system according to the N groups of credit approval data, average approval time, approval passing rate, proportion of the users with overdue credit and/or credit violations and the like are determined, then the abnormal type of the target approval node is determined according to the data, and approval adjustment information of the target approval node is generated according to the abnormal type. Therefore, the problem that the approval process cannot be optimized in time in the related technology can be solved, and the effect of improving the approval efficiency is further achieved.
Drawings
FIG. 1 is a schematic diagram of a hardware environment of a method for generating approval adjustment information according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of generating approval adjustment information according to an embodiment of the present application;
FIG. 3 is a flow chart of a method for generating approval adjustment information for a credit approval system in accordance with an embodiment of the application;
fig. 4 is a block diagram showing a construction of an approval adjustment information generating apparatus according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in detail below with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a server apparatus or similar computing device. Taking the operation on a server device as an example, fig. 1 is a schematic diagram of a hardware environment of a method for generating approval adjustment information according to an embodiment of the present application. As shown in fig. 1, the server device may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU, a programmable logic device FPGA, or the like processing means) and a memory 104 for storing data, wherein the server device may further include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those of ordinary skill in the art that the architecture shown in fig. 1 is merely illustrative and is not intended to limit the architecture of the server apparatus described above. For example, the server device may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store a computer program, for example, a software program of an application software and a module, such as a computer program corresponding to a method for generating approval adjustment information in an embodiment of the present application, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, implement the above method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located with respect to the processor 102, which may be connected to the server device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of a server device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as a NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.
In this embodiment, a method for generating approval adjustment information is provided, and fig. 2 is a flowchart of a method for generating approval adjustment information according to an embodiment of the present application, as shown in fig. 2, where the flowchart includes the following steps:
Step S202, N groups of credit approval data are obtained, wherein the N groups of credit approval data comprise process data for approving credit applications of N users, the N groups of credit approval data are all data for executing approval operations through M approval nodes in a credit approval system, and the N and the M are natural numbers larger than 1;
optionally, the present embodiment may be applied in credit approval systems, including but not limited to, a blockchain-based credit approval system, a cloud-proto-architecture credit approval platform, an edge-computing-based instant approval system.
Optionally, in the embodiment, the credit approval data is process data for approving a credit application of the user, including but not limited to information of an approval node performing an approval operation on the credit approval data, and a credit request corresponding to the credit approval data.
Optionally, the approval operations in this embodiment include, but are not limited to, data verification, credit assessment, deciding whether to approve a loan, determining repayment conditions, risk detection, and pre-warning.
Step S204, obtaining a target approval log of a target approval node from the credit approval system according to N groups of the credit approval data, wherein the target approval node is any one of M approval nodes;
Optionally, the target approval log in this embodiment is used to record data generated by the target approval node when performing an approval operation, including, but not limited to, approval start time, approval end time, pause or wait time in the approval process, approval result data, satisfaction score data of the user, and approval feedback data.
Step S206, determining the approval efficiency data of the target approval node from the target approval log, wherein the approval efficiency data comprises at least one of approval time, approval passing rate, credit risk assessment data, the evaluation data of the user and feedback data of other approval nodes;
Optionally, the method of calculating the approval passing rate in this embodiment includes dividing the number of passing approval in the N groups of credit approval data by N.
Step S208, generating approval adjustment information of the target approval node by using the approval efficiency data, wherein the approval adjustment information is used for indicating the target approval node to adjust the flow of executing the approval operation.
Optionally, the approval adjustment information in the implementation is used for adjusting the approval operation process, including but not limited to, process optimization suggestions, such as shortening the processing time of specific approval nodes, simplifying redundant steps in the approval process, decision rule adjustment, adjusting parameters of a credit scoring model according to approval passing rate and risk assessment data, optimizing approval decision rules, training and coaching requirements, and providing personalized training suggestions according to the performance of approval personnel to improve approval quality and efficiency, improvement of a risk early warning mechanism, optimization of a risk recognition algorithm, reduction of false alarm, improvement of early warning accuracy and timeliness, improvement of user feedback collection and processing processes, and improvement of customer satisfaction and service level.
Through the steps, according to the N groups of credit approval data, an approval log of a target approval node is obtained, approval efficiency data is extracted from the approval log, and approval adjustment information is generated by utilizing the approval efficiency data, so that the target approval node is indicated to optimize an approval process. The method solves the problem that the approval process cannot be timely adjusted in the related art, and achieves the effects of improving the approval efficiency and reducing the approval time.
In an exemplary embodiment, before acquiring the N sets of credit approval data, the method further includes receiving a credit request sent by N users, analyzing user information and credit fund information included in the N sets of credit request to obtain N sets of credit request initial data, performing a data processing operation on the N sets of credit request initial data to obtain N sets of credit request data, where the data processing operation includes a data verification operation and a data format conversion operation, the data verification operation includes verification of data integrity and verification of data logic, inputting the N sets of credit request data into the credit approval system respectively, to perform the approval operation on the N sets of credit request data through M approval nodes respectively, acquiring process data when the M approval nodes perform the approval operation on the N sets of credit request data respectively, to obtain N sets of credit approval data, converting the N sets of credit approval data into data in a target format, and storing the data in a target database.
Optionally, in this embodiment, the user information includes, but is not limited to, personal basic information such as name, identification number, contact, residence address, occupation information, etc., credit history including past credit records, repayment history, overdue or default information of the user, financial information including income statement, asset list, bank running water, liability record, etc., and some optional information such as education background, marital status, residence status, etc.
Optionally, in this embodiment, the credit fund information includes, but is not limited to, loan types, such as a personal loan, an enterprise loan, a house loan, an automobile loan, etc., a loan amount, a specific loan amount applied by a user, a loan purpose, a fund use purpose, such as for education, entrepreneur, house purchase, car purchase, etc., a repayment manner, such as repayment terms, repayment frequency, interest rate, etc., and some additional conditions, such as whether there is an insurance, whether there is a common borrower, whether mortgage is needed, etc.
Optionally, in this embodiment, the verification of the integrity of the data may be specifically performed by checking whether all the necessary fields in each credit request are filled in, such as name, id number, contact, income condition, loan usage, etc. of the borrower, 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 uniform, etc., checking whether the data of different parts are consistent with each other, such as whether the income shown in the income certificate matches the income stated by the borrower, whether the total value of the assets in the financial data conforms to the asset list, and cross-verifying the system may conform to the data provided by the third party source.
Alternatively, in this embodiment, the verification of the data logic may be that the loan amount cannot exceed a certain multiple of the income, that the loan period should be compatible with the loan type, that the income declared by the user is much higher than the industry average, and that the system may flag as abnormal for further auditing.
Optionally, in the present embodiment, the data in the target format includes, but is not limited to, extensible markup language (Extensible Markup Language, abbreviated as XML) files, javaScript object notation (JavaScript Object Notation, abbreviated as JSON), and table formats in the database.
Through the steps, the credit request of the user is received, the request data is analyzed, data verification and format conversion are carried out, the processed credit request data is input into a credit approval system, approval operation is carried out, meanwhile, approval process data are collected and converted into a unified format and stored, the accuracy and consistency of the credit approval data are ensured, and centralized management and analysis of the credit approval data are facilitated.
In an exemplary embodiment, acquiring N groups of credit approval data comprises receiving a node approval request, wherein the node approval request is used for requesting approval of the target approval node, and responding to the node approval request, acquiring the credit approval data matched with target node information of the target approval node, target approval flow of the target node and approval time period included in the node approval request from a target database to obtain N groups of the credit approval data.
Optionally, in this embodiment, the node approval request is used to request approval of the target approval node, and specifically may be whether the approval time of the approval target node is reasonable, whether the approval condition is compliant, and the like.
Through the steps, when the approval request aiming at the specific approval node (corresponding to the target approval node) is received, credit approval data matched with the request information is retrieved from the database, and the approval information of the specific approval node can be accurately positioned and acquired.
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 comprises approval start time, approval end time, pause or waiting time in the approval process, calculating average approval time by using the approval start time, the approval end time and the pause or waiting time in the approval process, counting approval result data of the target approval node from the target approval data, calculating the ratio of the number of through approval to the number of N in the approval result data, obtaining approval passing rate, extracting risk scores made by the target approval node based on user information of the user and historical credit data of the user from the target approval data, calculating the proportion of the occurrence of credit and/or approval of the user by using the risk scores and the historical credit data, determining the proportion of the approval by using the risk scores and the historical credit data, and triggering the approval result, determining the risk score mechanism based on the corresponding to the target approval result, determining the risk score and the risk score mechanism based on the risk score and the risk score, the satisfaction degree grading data are data collected from an approval questionnaire or a user feedback system, the satisfaction degree grading data and approval feedback data are quantitatively processed to obtain the user evaluation data, feedback data of an upstream approval node and feedback data of a downstream approval node of the target approval node are obtained from the target approval data to obtain feedback data of other approval nodes, wherein the feedback data of the upstream approval node comprise preparation data which are circulated to the target approval node, and the feedback data of the downstream approval node comprise evaluation data of approval decisions made by the target approval node.
Optionally, the approval performance data in this embodiment may be intuitively displayed in the approval interface by means of a dashboard of the cockpit.
Optionally, in this embodiment, the average approval time is a ratio of time consumed by M credit requests to M for performing approval operations at the target approval node, where M is a natural number greater than or equal to 1.
Optionally, the time consumed by the credit request in this embodiment for performing the approval operation at the target approval node is calculated by using the approval start time and the approval end time of the approval operation performed by the target approval node on the credit request.
Alternatively, the risk score in this embodiment may be obtained by a machine learning model or a risk scoring algorithm, where the user information and the user history credit data are input, and the risk score of the user is obtained by using the machine learning model or the risk scoring algorithm.
Optionally, the step of calculating the proportion of the user's overdue credit and/or default credit using the risk score and the historical repayment data may be to analyze the historical repayment records to calculate the number of times the user fails to repay (overdue) or repay (default) the loan (default) over a period of time (e.g., the past year), and the corresponding total amount of the loan, and then calculate the initial proportion of overdue and/or default for each user based on the number of overdue or default times and the total amount of the loan. For example, if a user has 3 loans, 1 of which is overdue, the total loan amount is 100 ten thousand yuan, and the overdue amount is 30 ten thousand yuan, then the initial overdue proportion is 30%. And then combining the proportion with a risk score of the user, and evaluating the risk level of the overdue credit and the default of the user to obtain the proportion of the overdue credit and/or default of the credit of the final user. For example, for users with higher risk scores, based on the initial overdue proportion obtained after analysis of the historical payment records, the resulting proportion of generated credit overdue and/or credit violations is correspondingly increased.
Optionally, in this embodiment, the frequency of triggering the credit risk early warning mechanism by the target approval node is a ratio of the number of credit requests triggering the credit risk early warning mechanism to M, where M is a natural number greater than or equal to 1, in M credit requests when the target approval node performs an approval operation.
Optionally, in this embodiment, the processing results corresponding to the triggered credit risk early warning mechanism include, but are not limited to, approval decision adjustment, early warning notification, and secondary approval, for example, if the risk early warning mechanism is triggered, the system may require the approver to review the application again, or automatically adjust approval conditions, such as reducing the loan amount, improving the interest rate, requiring additional guarantee, etc.
Optionally, in this embodiment, the feedback data of the upstream approval node includes preparation data that flows to the target approval node, including but not limited to user funds verification operations, user loan amount confirmation, and the like.
Optionally, in this embodiment, the feedback data of the downstream approval node includes evaluation data of approval decisions made on 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 make decision adjustment advice based on subsequent examination findings), risk review results (the downstream approval node may make secondary risk assessment to ensure that the risk of the loan issuing decision is controllable), loan conditions and terms (the downstream approval node may set specific loan conditions, such as a loan time, a loan amount, a loan interest rate, etc.), subsequent course guidance (the downstream approval node may provide guidance of subsequent credit courses, such as guiding borrowers how to make repayment operations after loan, providing loan management services, etc.). For example, the target approval node (e.g., loan approval node) decides to approve a user's loan application, the downstream approval node (e.g., loan administration node) may evaluate the decision risk and set the loan conditions, e.g., ask the user to complete all the loan procedures before a certain date, otherwise, it needs to be reevaluated.
Through the steps, the target approval log is converted into the structured data, approval efficiency data such as approval time, passing rate, risk assessment, user assessment and other node feedback are extracted, and then various indexes such as average approval time, approval passing rate, credit risk ratio, user satisfaction and the like are calculated, so that a data basis for quantitatively assessing the approval efficiency is provided, and a basis is provided for subsequently determining potential problems and improvement points in the approval process.
In an exemplary embodiment, after calculating the average approval time by using the approval start time, the approval end time, and the pause or wait time in the approval process, the method further includes generating a distribution trend chart of the approval time, identifying a time delay in the approval process of the target approval node from the distribution trend chart, and marking the time delay.
Optionally, in this embodiment, the form of displaying the distribution trend graph of the approval time includes, but is not limited to, a line graph, a scatter graph, and a box graph, for example, the approval time is arranged in time sequence, the approval time data corresponding to each time point is connected by a broken line, the approval time data is distributed in a coordinate system in the form of a point, and the five-digit summary (minimum, bottom quartile, median, top quartile, maximum) of the approval time data can be displayed through the box graph.
Through the steps, the distribution trend of the approval time is analyzed, the time delay in the approval process is identified, the identified time delay is marked, the link of the approval time delay can be accurately positioned, the follow-up targeted adjustment is convenient, and the approval process is optimized.
In an exemplary embodiment, generating the approval adjustment information of the target approval node by using the approval efficiency data includes determining an exception 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 exception type.
Optionally, in this embodiment, the exception types include, but are not limited to, approval aging exception, decision risk exception, approval node allocation exception, rule execution exception, and user experience exception.
Alternatively, in this embodiment, the relevant approvers may be trained specifically according to the anomaly type, for example, a person with a slow approval may need training of process familiarity, and a person with a high error rate may need training in compliance and risk awareness. Meanwhile, a system capable of recommending proper training resources according to the specific requirements of the approvers can be constructed, recommendation strategies can be adaptively adjusted according to the historical participation conditions and personal interests of the approvers, and a personalized training plan comprising course selection, learning paths and expected targets is generated for each approver. The training plan can be adjusted according to the performance change of the approver after training, and after training is implemented, the training effect can be further determined by determining whether the target approval node is abnormal, so that the training plan can be further adjusted in real time.
Through the steps, the abnormal types in the approval process are judged according to the approval efficiency data, and then approval adjustment information is generated for different abnormal types, so that the accurate adjustment of the approval process is realized, the problem is solved in a targeted manner, and the approval efficiency and quality are further improved through adjusting the approval process.
In one exemplary embodiment, determining the abnormal type of the target approval node according to the approval efficiency data includes determining that the abnormality of the target approval node is a first abnormal type when the approval efficiency data includes approval time and the average approval time in the approval time is greater than preset approval time and the fluctuation range of the approval time is greater than preset fluctuation range, determining that the abnormality of the target approval node is a second abnormal type when the approval efficiency data includes approval passing rate and credit risk assessment data and a data association relationship between the approval passing rate and the credit risk assessment data is abnormal, determining that the abnormality of the target approval node is a third abnormal type when the approval efficiency data includes evaluation data of the user and the probability of user satisfaction in the evaluation data is less than the preset probability and determining that the abnormality of the target approval node is a fourth abnormal type when the approval efficiency data includes feedback data of other approval nodes and a data association relationship between the target approval node and the target approval node is abnormal.
Optionally, in this embodiment, the first exception type of the target approval node may be approval aging exception, which indicates that the approval node may have efficiency bottlenecks, such as uneven task allocation of approval personnel, unreasonable approval flow design, system performance problems, and the like. Aiming at the first abnormal type, the approval adjustment information which can be generated by the system comprises optimizing an approval node task allocation strategy, namely, by reallocating approval tasks, ensuring the workload balance of approval personnel, avoiding approval delay caused by overload of the tasks by a certain person, simplifying the approval process, namely, analyzing redundant steps in the approval process, simplifying or deleting unnecessary processes, improving the system performance, and being capable of properly improving the processing capacity of a server, optimizing the query efficiency of a database, increasing the bandwidth and the like. For example, if the system monitors that the average approval time of the credit evaluation node is greater than the preset approval time and the fluctuation range of the approval time is greater than the preset fluctuation range, the amount of credit request data required to be processed by each approval node is determined, if the amount of credit request data required to be processed by the approval node is unevenly distributed, the generated approval adjustment information may suggest a decentralized workload, that is, when the approval nodes are distributed, the credit request data is preferentially distributed to the approval nodes with smaller amount of credit request data required to be processed, or the approval nodes with shorter time required to approve the credit request data of the type are distributed according to the type of the credit request data.
Optionally, in this embodiment, the second exception type of the target approval node may be that the approval decision does not match the risk assessment, that is, when the approval passing rate of the target approval node is abnormally high or low, but the risk assessment data is relatively stable, the system will determine the second exception type. This means that approval decisions are too loose or too strict, not in line with the risk assessment results, possibly resulting in a loss of loan or loss of good customers. For the second anomaly type, the approval adjustment information that the system can generate includes, but is not limited to, an adjustment approval standard, i.e., a recommended adjustment approval standard, ensuring that the decision matches the risk condition, an optimized decision support model, and a machine learning model or risk scoring algorithm that is checked and adjusted to ensure that it accurately reflects the credit risk. For example, where the approval pass rate of a target approval node is much higher than other nodes without significant improvement in risk scores, the relevant approval personnel may be trained in terms of compliance and risk awareness.
Optionally, in this embodiment, the third exception type of the target approval node may be that the customer experience is poor, which may be caused by reasons of excessively long approval time, unsmooth communication, complex approval process, and the like, which affects the customer experience. For the third anomaly type, the generated approval adjustment information comprises, but is not limited to, optimizing a user communication process, namely improving a communication channel between a user and an approver or increasing the frequency of communication with the user, improving a user interface, optimizing a user interface design and simplifying a loan application process. For example, under the condition that the abnormality of the target approval node is determined to be of the third abnormality type, feedback data of the user is further analyzed, and if the user feedback is repeatedly referred to that the application operation is complicated and the interface is easy to touch by mistake, the interface design of the user can be optimized.
Optionally, in this embodiment, the fourth exception type of the target approval node may be that an information transmission and feedback mechanism between the approval nodes fails, which indicates that an information island exists in the approval process, and affects the overall approval efficiency and quality. For the fourth exception type, the approval adjustment information which can be generated by the system comprises, but is not limited to, data flow optimization, data flow examination and optimization, timely and accurate data transmission among approval nodes is ensured, information delay or error is avoided, the approval process is coordinated, the approval process is redesigned or adjusted, the decision basis of the upstream and downstream approval nodes is ensured to be related, and the coordination and efficiency of the process are improved. For example, in the case that the abnormality of the target approval node is determined to be of the fourth abnormality type, it is further detected that the actual problem is that the loan issuing node fails to adjust the loan condition according to the risk score of the credit evaluation node, and the generated approval adjustment information is to suggest to add an automatic adjustment mechanism in the flow design, dynamically adjust the loan interest rate, the repayment mode, and the like according to the credit score.
Through the steps, the approval time, the pass rate and risk assessment, the user evaluation, feedback of other approval nodes and the like are subjected to abnormality judgment, corresponding abnormality types are determined according to the abnormality conditions of different data, the problems in the approval process can be more specifically identified, and a more accurate and effective process adjustment strategy is realized by classifying the abnormality types.
In the following, the method is described with a specific example, and fig. 3 is a flowchart illustrating a method for generating approval adjustment information of a credit approval system implemented according to the present application, and as shown in fig. 3, a commercial bank is using a credit approval system based on big data and artificial intelligence, where the system is designed with a plurality of approval nodes, including links of customer information verification, credit scoring, loan amount evaluation, compliance check, and final approval.
Step S302, a node approval request is received, wherein the node approval request is used for requesting approval of a credit scoring node (namely the target approval node);
Step S304, obtaining credit approval data from a target database, and obtaining a target approval log of a credit score node from a credit approval system according to the credit approval data, wherein the target approval log comprises N approval records passing through the credit score node within the past 30 days, and N=1200 (namely 1200 approval cases are collected);
Step S306, carrying out structural processing on the target approval log, and converting the target approval log into a database format suitable for analysis, wherein in the process, the system checks the data integrity, confirms that no field is missing, standardizes all data, ensures that the formats are consistent, and is convenient for subsequent analysis;
Step S308, the approval efficiency data of the credit score node is extracted from the structured data, wherein the approval time and the approval passing rate are analyzed to obtain that the average approval time of the credit score node reaches 70 minutes, which is far higher than a preset 45-minute standard, the fluctuation range is large, the approval passing rate is 75%, and the difference between the approval passing rate and the passing rate (72%) predicted by the risk assessment model meets a threshold (within 5% of error), so that the decision is matched with the risk assessment.
Step S310, approval adjustment information of a node with credit score is generated by using the approval efficiency data, the node with credit score is determined to be a first abnormal type based on the analysis result, the approval adjustment information is generated by adding approval personnel of the node with credit score, and an approval algorithm is optimized to improve the processing speed, and meanwhile, the approval personnel of the node are trained to be proficient in executing the auditing operation.
Through the steps, the bank can timely identify the efficiency bottleneck and decision deviation in the credit approval process, and approval adjustment information is generated and implemented, so that the approval quality and efficiency are effectively improved, and the user experience is enhanced.
It should be noted that, from the description of the above embodiments, those skilled in the art will clearly understand that the method according to the above embodiments may be implemented by software plus a necessary general hardware platform, and of course may also be implemented by hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present application.
The embodiment also provides a device for generating approval adjustment information, which is used for implementing the above embodiment and the preferred implementation manner, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
Fig. 4 is a block diagram of an apparatus for generating approval adjustment information according to an embodiment of the present application, as shown in fig. 4, the apparatus including:
A first obtaining module 402, configured to obtain N credit approval data, where the N credit approval data includes process data for approving credit applications of N users, and the N credit approval data are all data for performing approval operations by M approval nodes in a credit approval system, and N and M are natural numbers greater than 1;
A second obtaining module 404, configured to obtain, from the credit approval system, a target approval log of a target approval node according to N pieces of the credit approval data, where the target approval node is any one of M pieces of the approval nodes;
A determining module 406, configured to determine approval performance data of the target approval node from the target approval log, where the approval performance data includes at least one of approval time, approval passing rate, credit risk assessment data, evaluation data of the user, and feedback data of other approval nodes;
the generating module 408 is configured to generate approval adjustment information of the target approval node by using the approval performance data, where the approval adjustment information is used to instruct the target approval node to adjust a flow of executing the approval operation.
In an exemplary embodiment, the first obtaining module 402 further includes receiving a credit request sent by N users, analyzing user information and credit fund information included in the N credit requests to obtain N groups of credit request initial data, performing a data processing operation on the N groups of credit request initial data to obtain N groups of credit request data, where the data processing operation includes a data verification operation and a data format conversion operation, the data verification operation includes verification of data integrity and verification of data logic, inputting the N groups of credit request data into the credit approval system respectively to perform the approval operation on the N groups of credit request data through M approval nodes respectively, collecting process data when the M approval nodes perform the approval operation on the N groups of credit request data respectively through a data collection service set in the approval system to obtain N groups of credit approval data, and converting the N groups of credit approval data into target format data and storing the target data in a target database.
In an exemplary embodiment, the first obtaining module 402 further includes a first receiving unit, configured to receive a node approval request, where the node approval request is used to request approval of the target approval node, and a first response unit, configured to obtain, in response to the node approval request, credit approval data that matches with target node information of the target approval node, a target approval process of the target node, and an approval time period included in the node approval request, from a target database, to obtain N groups of the credit approval data.
In an exemplary embodiment, the determining module 406 further includes a first converting unit configured to convert the target approval log into a structured data format to obtain target approval data, a first extracting unit configured to extract an approval process of the target approval node from the target approval data and calculate an approval time from the approval process, where the approval time includes an approval start time, an approval end time, a pause or wait time in the approval process, a first calculating unit configured to calculate an average approval time using the approval start time, the approval end time, a pause or wait time in the approval process, a second calculating unit configured to calculate approval result data of the target approval node from the target approval data and calculate a ratio between the number of N sets of the credit approval data and the N from the approval result data, a second extracting unit configured to extract the target approval data from the target approval data, and determine a risk-withdrawal result based on the first and second calculating unit and a first pre-alarm unit configured to determine a risk-approval result by a first pre-alarm unit and a second calculating unit configured to determine a risk-alarm based on the first and second pre-alarm unit configured to determine a risk-approval result by a user, a first pre-alarm unit configured to compare the risk-alarm unit and a second pre-alarm unit configured to determine a risk-alarm result by a second pre-alarm unit configured to compare the target approval result data to the target approval node to the target approval data, the system comprises a target approval data, a satisfaction degree scoring data and approval feedback data of the user, a first quantification unit and a first acquisition unit, wherein the satisfaction degree scoring data and the approval feedback data of the user are extracted from the target approval data, the satisfaction degree scoring data are acquired from an approval questionnaire or a user feedback system, the first quantification unit is used for quantitatively processing the satisfaction degree scoring data and the approval feedback data to obtain the evaluation data of the user, the first acquisition unit is used for acquiring the feedback data of an upstream approval node and the feedback data of a downstream approval node of the target approval node from the target approval data to obtain the feedback data of other approval nodes, the feedback data of the upstream approval node comprises preparation data which flows to the target approval node, and the feedback data of the downstream approval node comprises evaluation data of approval decisions made on the target approval node.
In an exemplary embodiment, the determining module 406 further includes a first generating unit configured to generate a distribution trend chart of approval times, and a first identifying unit configured to identify a time delay in an approval process of the target approval node from the distribution trend chart, and mark the time delay.
In an exemplary embodiment, the generating module 408 further includes a second determining unit configured to determine an exception type of the target approval node according to the approval efficiency data, and a second generating unit configured to generate approval adjustment information of the target approval node according to the exception type.
In an exemplary embodiment, the generating module 408 further includes a third determining unit configured to determine that the abnormality of the target approval node is the first abnormality type when the approval efficiency data includes approval time and the data association relationship between the approval efficiency data and the credit risk assessment data is abnormal, and a fifth determining unit configured to determine that the abnormality of the target approval node is the second abnormality type when the approval efficiency data includes approval data of the user and the probability of user satisfaction in the approval data being greater than the preset satisfaction is less than the preset probability, and determine that the abnormality of the target approval node is the second abnormality type when the approval efficiency data includes approval passing rate and credit risk assessment data and the data association relationship between the approval efficiency data and the credit risk assessment data is abnormal, and the fourth determining unit configured to determine that the abnormality of the target approval node is the feedback abnormality of the other target approval node and the feedback relationship between the other nodes is abnormal.
It should be noted that each of the above modules may be implemented by software or hardware, and the latter may be implemented by, but not limited to, the above modules all being located in the same processor, or each of the above modules being located in different processors in any combination.
Embodiments of the present application also provide a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
In an exemplary embodiment, the computer readable storage medium may include, but is not limited to, a U disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, etc. various media in which a computer program may be stored.
An embodiment of the application also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
In an exemplary embodiment, the electronic device may further include a transmission device connected to the processor, and an input/output device connected to the processor.
Embodiments of the application also provide a computer program product comprising a computer program which, when executed by a processor, implements the steps of any of the method embodiments described above.
Embodiments of the present application also provide another computer program product comprising a non-volatile computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of any of the method embodiments described above.
Embodiments of the present application also provide a computer program comprising computer instructions stored in a computer-readable storage medium, a processor of a computer device reading the computer instructions from the computer-readable storage medium, the computer instructions being executable by a burial device to cause the computer device to perform the steps of any of the method embodiments described above.
Specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the exemplary implementation, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the modules or steps of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application should be included in the protection scope of the present application.
Claims (9)
1. A method for generating approval adjustment information, the method comprising:
Acquiring N groups of credit approval data, wherein the N groups of credit approval data comprise process data for approving credit applications of N users, the N groups of credit approval data are all data for executing approval operations through M approval nodes in a credit approval system, and the N and the M are natural numbers larger than 1;
acquiring target approval logs of target approval nodes from the credit approval system according to N groups of credit approval data, wherein the target approval nodes are any one of M approval nodes;
Determining approval efficiency data of the target approval node from the target approval log, wherein the approval efficiency data comprises at least one of approval time, approval passing rate, credit risk assessment data, user evaluation data and feedback data of other approval nodes;
generating approval adjustment information of the target approval node by utilizing the approval efficiency data, wherein the approval adjustment information is used for indicating the target approval node to adjust the flow of executing the approval operation;
Determining approval efficiency data of the target approval node from the target approval log, wherein the method comprises the steps of converting the target approval log into a structured data format to obtain target approval data, extracting an approval process of the target approval node from the target approval data and calculating approval time from the approval process, wherein the approval time comprises approval start time, approval end time and pause or waiting time in the approval process, calculating average approval time by using the approval start time, the approval end time and pause or waiting time in the approval process, calculating approval result data of the target approval node from the target approval result data, calculating a ratio between the number of the approval N groups of the credit approval data and the N to obtain a passing rate, extracting the target approval node from the target approval data based on user information of the user and risk of the user, credit approval data, calculating a satisfaction score by using the feedback system and the feedback result of the user, and the feedback result of the approval system, and the satisfaction score is determined by using the feedback system, and the feedback result of the feedback system is the satisfaction score is triggered by the feedback system, and acquiring feedback data of an upstream approval node and feedback data of a downstream approval node of the target approval node from the target approval data to acquire feedback data of other approval nodes, wherein the feedback data of the upstream approval node comprises preparation data which flows to the target approval node, and the feedback data of the downstream approval node comprises evaluation data of approval decisions made by the target approval node.
2. The method of claim 1, wherein prior to acquiring the N sets of credit approval data, the method further comprises:
receiving credit requests sent by N users;
analyzing the user information and the credit fund information included in the N credit requests to obtain N groups of initial credit request data;
performing data processing operation on the N groups of initial credit request data to obtain N groups of credit request data, wherein the data processing operation comprises data verification operation and data format conversion operation, and the data verification operation comprises verification of data integrity and verification of data logic;
respectively inputting N groups of credit request data into the credit approval system so as to respectively execute the approval operation on the N groups of credit request data through M approval nodes;
Collecting process data when M approval nodes execute the approval operation on N groups of credit request data respectively through a data collection service arranged in the approval system to obtain N groups of credit approval data;
and converting the N groups of credit approval data into data in a target format, and storing the data in a target database.
3. The method of claim 1 wherein obtaining N sets of credit approval data comprises:
receiving a node approval request, wherein the node approval request is used for requesting approval of the target approval node;
And responding to the node approval request, acquiring credit approval data matched with target node information of the target approval node, target approval flow of the target node and approval time periods included in the node approval request from a target database, and obtaining N groups of credit approval data.
4. The method of claim 1, wherein after calculating an average approval time using the approval start time, the approval end time, a pause in the approval process, or a wait time, the method further comprises:
generating a distribution trend graph of approval time;
and identifying time delay in the approval process of the target approval node from the distribution trend graph, and marking the time delay.
5. The method of claim 1, wherein generating approval adjustment information for the target approval node using the approval performance data comprises:
determining the abnormal type of the target approval node according to the approval efficiency data;
And generating approval adjustment information of the target approval node according to the abnormal type.
6. The method of claim 5, wherein determining the exception type for the target approval node based on the approval performance data comprises:
determining that the abnormality of the target approval node is of a first abnormality type when the approval efficiency data comprises approval time, the average approval time in the approval time is larger than preset approval time, and the fluctuation range of the approval time is larger than preset fluctuation range;
determining that the abnormality of the target approval node is of a second abnormality type under the condition that the approval efficiency data comprises approval passing rate and credit risk assessment data and the data association relationship between the approval passing rate and the credit risk assessment data is abnormal;
Determining that the abnormality of the target approval node is of a third abnormality type when the approval efficiency data comprises evaluation data of the user and the probability of user satisfaction greater than preset satisfaction in the evaluation data is smaller than the preset probability;
And determining that the abnormality of the target approval node is of a fourth abnormality type under the condition that the approval efficiency data comprises feedback data of other approval nodes and no association exists between the feedback data of the other approval nodes and the approval result of the target approval node.
7. An approval adjustment information generation apparatus, comprising:
The first acquisition module is used for acquiring N pieces of credit approval data, wherein N pieces of credit approval data comprise process data for approving credit applications of N users, the N pieces of credit approval data are all data for executing approval operation through M approval nodes in a credit approval system, and the N and the M are natural numbers larger than 1;
The second acquisition module is used for acquiring target approval logs of target approval nodes from the credit approval system according to the N credit approval data, wherein the target approval nodes are any one of the M approval nodes;
The determining module is used for determining the approval efficiency data of the target approval node from the target approval log, wherein the approval efficiency data comprises at least one of approval time, approval passing rate, credit risk assessment data, user evaluation data and feedback data of other approval nodes;
The generating module is used for generating approval adjustment information of the target approval node by utilizing the approval efficiency data, wherein the approval adjustment information is used for indicating the target approval node to adjust the flow of executing the approval operation;
The determining module further comprises a first converting unit, a second converting unit and a third converting unit, wherein the first converting unit is used 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 comprises approval start time, approval end time and pause or waiting time in the approval process, a first calculation unit for calculating average approval time by using the approval start time, the approval end time and pause or waiting time in 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 approval passing rate from the approval result data by the ratio between the number of the approval data and the N, a second extraction unit for extracting the risk score of the target approval node based on the user information and the user history credit data and the user history repayment data, a third calculation unit for determining the risk score by using the risk feedback unit and the credit feedback score and the approval result and the pre-alarm rate, a first calculation unit for determining the risk score and a second calculation unit based on the first and the risk score and the first calculation unit, the satisfaction degree scoring data are data acquired from an approval questionnaire or a user feedback system, a first quantization unit is used for performing quantization processing on the satisfaction degree scoring data and the approval feedback data to obtain evaluation data of the user, and a first acquisition unit is used for acquiring feedback data of an upstream approval node and feedback data of a 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 comprise preparation data which are circulated to the target approval node, and the feedback data of the downstream approval node comprise evaluation data of approval decisions made by the target approval node.
8. A computer readable storage medium, characterized in that a computer program is stored in the computer readable storage medium, wherein the computer program, when being executed by a processor, implements the steps of the method according to any of the claims 1 to 6.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 6 when the computer program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411832495.4A CN119762213B (en) | 2024-12-12 | 2024-12-12 | Approval adjustment information generation method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411832495.4A CN119762213B (en) | 2024-12-12 | 2024-12-12 | Approval adjustment information generation method and device, storage medium and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN119762213A CN119762213A (en) | 2025-04-04 |
| CN119762213B true CN119762213B (en) | 2025-09-23 |
Family
ID=95174569
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411832495.4A Active CN119762213B (en) | 2024-12-12 | 2024-12-12 | Approval adjustment information generation method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119762213B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109886797A (en) * | 2018-12-31 | 2019-06-14 | 杭州恒生云融网络科技有限公司 | A kind of batch examination & approval study of credit and optimization method |
| CN115423437A (en) * | 2022-09-01 | 2022-12-02 | 中国工商银行股份有限公司 | Processing method and device for approval process |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107203847A (en) * | 2017-05-25 | 2017-09-26 | 大连云匠科技有限公司 | A process node efficiency statistical method for OA system client |
| CN113850527B (en) * | 2021-09-30 | 2024-03-12 | 明度智云(浙江)科技有限公司 | Custom approval method, system and storage medium for medical electronic experiment record |
| CN116911768A (en) * | 2023-06-07 | 2023-10-20 | 海信集团控股股份有限公司 | A method and device for determining the type of inefficient approval process |
| CN117764532A (en) * | 2024-01-08 | 2024-03-26 | 招银云创信息技术有限公司 | Project approval method and related device |
| CN118761745B (en) * | 2024-09-06 | 2024-12-03 | 合肥翰玖科技有限公司 | OA collaborative workflow optimization method applied to enterprise |
-
2024
- 2024-12-12 CN CN202411832495.4A patent/CN119762213B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109886797A (en) * | 2018-12-31 | 2019-06-14 | 杭州恒生云融网络科技有限公司 | A kind of batch examination & approval study of credit and optimization method |
| CN115423437A (en) * | 2022-09-01 | 2022-12-02 | 中国工商银行股份有限公司 | Processing method and device for approval process |
Also Published As
| Publication number | Publication date |
|---|---|
| CN119762213A (en) | 2025-04-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20110166987A1 (en) | Evaluating Loan Access Using Online Business Transaction Data | |
| EP1361526A1 (en) | Electronic data processing system and method of using an electronic processing system for automatically determining a risk indicator value | |
| CN110866822B (en) | Wind control management method and device for securitization of assets, electronic equipment and storage medium | |
| CN117455681A (en) | Business risk prediction methods and devices | |
| CN113643115A (en) | Method and system for scoring business acceptance draft credit based on option pricing model | |
| CN114118793B (en) | A local exchange risk warning method, device and equipment | |
| CN112598225A (en) | Evaluation index determination method and apparatus, storage medium, and electronic apparatus | |
| CN115082225A (en) | Enterprise financing risk assessment method and device | |
| CN112734566A (en) | Credit limit acquisition method and device and computer equipment | |
| CN117132383A (en) | Credit data processing method, device, equipment and readable storage medium | |
| CN116362895A (en) | Financial product recommendation method, device and storage medium | |
| KR20230094936A (en) | Activist alternative credit scoring system model using work behavior data and method for providing the same | |
| CN120807158A (en) | Risk assessment method and apparatus for financial business, electronic device and storage medium | |
| CN119762213B (en) | Approval adjustment information generation method and device, storage medium and electronic equipment | |
| CN111552814B (en) | Assessment scheme generation method and device based on assessment index map | |
| CN120373845A (en) | Risk identification method, medium and electronic equipment based on provider association relation | |
| CN116993480A (en) | Method and system for creating risk level model and evaluating risk level | |
| CN110570301B (en) | Risk identification method, device, equipment and medium | |
| CN114638439A (en) | Prediction method and device for large-amount exposure risk | |
| CN113822490A (en) | Asset clearing and accepting method and device based on artificial intelligence and electronic equipment | |
| CN119782151A (en) | Method and device for generating fund security configuration information, and storage medium | |
| Silva et al. | FK3BR Scale: Development of an Ultra-Short Measure | |
| CN114331734B (en) | Risk assessment method, apparatus, device and storage medium for product purchase | |
| CN120687957A (en) | Intelligent classification method and system for non-performing assets | |
| CN121501342A (en) | Task execution methods and devices, program products |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |