CN119624617B - RPA-based automated bad account management and control method, system, device, and readable storage medium - Google Patents
RPA-based automated bad account management and control method, system, device, and readable storage mediumInfo
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Abstract
The invention discloses an automatic bad account management and control method, system, equipment and readable storage medium based on RPA, wherein the method comprises the steps of S1, inputting transaction information and account information generated by general business into a data lake warehouse, creating a personal customer temporary table and a public customer temporary table according to customer numbers, preprocessing data of the two temporary tables to obtain a preliminary screening account, S2, designing a bad account management and control flow rule to be automated according to business requirements, extracting account attribute data from the data lake warehouse by using the RPA according to the preliminary screening account, executing the bad account management and control flow rule to obtain a decision result, and executing management and control measures by using the RPA according to the decision result. The method solves the problem that the existing bad customer account management and control method lacks effective integration of multi-source data and lacks decision support of an automation rule, so that bad accounts cannot be quickly identified and managed.
Description
Technical Field
The invention belongs to the technical field of financial services, and particularly relates to an automatic bad account management and control method, system, equipment and readable storage medium based on RPA.
Background
At present, the poor account management and control flow is mostly carried out after credit intensive operation, is initiated manually, and is required to inquire and operate the poor loan debt and the account related to the poor loan debt by stroke, so that the workload is high. After the list is processed by the individual bad clients, accounts are still newly opened, the management and control actions are easy to miss, and the time efficiency and coverage of the bad client account management and control work cannot be achieved.
Meanwhile, the conventional general loan service is used for customer access through various channels, including WeChat applets of a mobile terminal, mobile banking, various flat plates of a PC terminal, online banking and the like, and various running water and account information exist in the original system components. For the management and control of bad accounts, the management and control are carried out by means of reports and manual modes, and the situations of misjudgment, inaccurate analysis and the like exist, so that customer complaints are easily caused. Meanwhile, the manual management and control mode faces the problem of data island and has a certain time difference, so that risk identification is not comprehensive and timely enough, bank funds are extremely easy to lose, and a certain risk is caused.
The chinese patent with publication number CN110060149a discloses a post-loan risk management and control method and system, which are used for obtaining account information, funds flow data and management data of a post-loan monitored object, and preprocessing the data to generate a time funds image, so as to monitor the funds use and funds effect of the post-loan monitored object in real time, thereby reducing the funds risk of the post-loan monitored object. The method comprises the steps of obtaining account information and fund flow direction data of a post-credit monitoring object, obtaining operation data of the post-credit monitoring object, preprocessing the account information, the fund flow direction data and the operation data to remove repeated data, obtaining operation information and account entry and exit data of the post-credit monitoring object, and generating time fund images according to time periods by the operation information and the account entry and exit data to be used for evaluating fund purposes and fund results of the post-credit monitoring object. The method mainly focuses on generating time fund images for monitoring, and lacks of deep analysis and intelligent judgment on data.
Disclosure of Invention
The invention provides an RPA-based bad account automatic management and control method, an RPA-based bad account automatic management and control system, RPA-based bad account automatic management and control equipment and a readable storage medium, and aims to solve the problem that an existing bad customer account management and control method lacks effective integration of multi-source data and lacks decision support of an automatic rule, so that bad accounts cannot be quickly identified and managed.
In order to solve the technical problems, the invention provides an automatic bad account management and control method based on RPA, which comprises the following steps:
S1, transaction information and account information generated by general business are put into a data lake warehouse, a personal customer temporary table and a public customer temporary table are created according to customer numbers, and data preprocessing is carried out on the two types of temporary tables to obtain a preliminary screening account.
S2, designing a bad account management and control flow rule to be automated according to service requirements, extracting account attribute data from a data lake warehouse according to a primary screening account by using an RPA, executing the bad account management and control flow rule to obtain a decision result, and executing management and control measures by using the RPA according to the decision result.
Preferably, in the step S1, the data preprocessing for the two types of temporary tables specifically includes:
And creating a personal client temporary table and a public client temporary table according to the client numbers, wherein fields in the personal client temporary table and the public client temporary table at least comprise the client numbers, the client names, bad loan balances and loan balances.
The queried personal new generation client number and related information field are inserted into a personal temporary table, the queried public new generation client number and related information field are inserted into a public temporary table, and the personal client temporary table and the public client temporary table are combined according to the personal new generation client number and the public new generation client number to obtain a combined table.
And inserting a management right second-level component number, a personal receipt and payment state and a public receipt and payment state field into the merging table, removing records with the same client number and abnormal personal receipt and payment state, merging records with bad loans on both the public and the personal, merging records with client names modified by the public or the personal, removing records with bad loans on both the public and the personal, and removing records with empty client numbers on the public to obtain the result table.
And comparing the result list with the client white list, and removing clients contained in the white list to obtain a preliminary screening account.
Preferably, the step S2 specifically includes:
And designing a bad account management and control flow rule to be automated according to business requirements, extracting account attribute data from the data lake warehouse by using the RPA according to the primary screening account, and carrying out standardized processing on the account attribute data, wherein the account attribute data at least comprises a product code, risk classification, loan debt balance, account state and customer grade.
The bad account management and control flow rule is specifically to set threshold indexes of bad account parameters of different grades and types, define processing logic of the bad accounts of different grades and types through conditional sentences, adjust the threshold indexes regularly according to market or policy changes at the same time, and the RPA automatically calls the bad account management and control flow processing script according to the bad account management and control flow rule and dynamically scores according to the weight of each bad parameter to obtain a comprehensive scoring result, and determine the risk grade of the bad account according to the comprehensive scoring result to obtain a decision result.
And executing the control measures by using the RPA according to the decision result, wherein the steps comprise updating and marking the state of the bad account, recording the control operation log and automatically generating a notification multi-channel pushing corresponding control user.
Preferably, the method further comprises the steps of:
and S3, defining roles of different grades, distributing specific authorities required by each class of roles according to service requirements, establishing a authority list, associating the roles with the matched grade roles according to basic information of specific management and control users, establishing a user role matching rule to ensure that each management and control user can only execute operations allowed by the roles, and automatically managing and controlling the role distribution and authority updating flow of the users according to the user role matching rule by using an RPA tool.
The permission list comprises a checking permission, a modification permission, an auditing permission, a configuration permission and an operation permission, wherein the checking permission comprises a log checking permission, a flow monitoring permission and a data access permission, the modification permission comprises a flow configuration permission, a data input permission and a variable management permission, the auditing permission comprises a flow auditing permission and an exception handling permission, the configuration permission comprises a user management permission, an RPA (remote procedure A) setting permission and a permission management permission, and the operation permission comprises a starting/stopping flow permission and a scheduling permission.
On the other hand, the invention provides an RPA-based bad account automatic management and control system, which comprises a data lake entering and preprocessing module and a bad account management and control module.
The data lake entering and preprocessing module is used for entering transaction information and account information generated by general business into a data lake warehouse, creating a personal customer temporary table and a public customer temporary table according to customer numbers, and preprocessing data of the two temporary tables to obtain a primary screening account.
And the bad account management and control module is used for designing a bad account management and control flow rule to be automated according to service requirements, extracting account attribute data from the data lake warehouse according to the primary screening account by using the RPA, executing the bad account management and control flow rule to obtain a decision result, and executing management and control measures by using the RPA according to the decision result.
Preferably, the data preprocessing of the two types of temporary tables in the data lake entering and preprocessing module specifically comprises the following steps:
And creating a personal client temporary table and a public client temporary table according to the client numbers, wherein fields in the personal client temporary table and the public client temporary table at least comprise the client numbers, the client names, bad loan balances and loan balances.
The queried personal new generation client number and related information field are inserted into a personal temporary table, the queried public new generation client number and related information field are inserted into a public temporary table, and the personal client temporary table and the public client temporary table are combined according to the personal new generation client number and the public new generation client number to obtain a combined table.
And inserting a management right second-level component number, a personal receipt and payment state and a public receipt and payment state field into the merging table, removing records with the same client number and abnormal personal receipt and payment state, merging records with bad loans on both the public and the personal, merging records with client names modified by the public or the personal, removing records with bad loans on both the public and the personal, and removing records with empty client numbers on the public to obtain the result table.
And comparing the result list with the client white list, and removing clients contained in the white list to obtain a preliminary screening account.
Preferably, the bad account management module is specifically configured to:
And designing a bad account management and control flow rule to be automated according to business requirements, extracting account attribute data from the data lake warehouse by using the RPA according to the primary screening account, and carrying out standardized processing on the account attribute data, wherein the account attribute data at least comprises a product code, risk classification, loan debt balance, account state and customer grade.
The bad account management and control flow rule is specifically to set threshold indexes of bad account parameters of different grades and types, define processing logic of the bad accounts of different grades and types through conditional sentences, adjust the threshold indexes regularly according to market or policy changes at the same time, and the RPA automatically calls the bad account management and control flow processing script according to the bad account management and control flow rule and dynamically scores according to the weight of each bad parameter to obtain a comprehensive scoring result, and determine the risk grade of the bad account according to the comprehensive scoring result to obtain a decision result.
And executing the control measures by using the RPA according to the decision result, wherein the steps comprise updating and marking the state of the bad account, recording the control operation log and automatically generating a notification multi-channel pushing corresponding control user.
Preferably, the system further comprises a user role and rights management module, and the user role and rights management module is used for:
Defining different grade roles, distributing specific authorities required by each grade role according to service requirements, establishing an authority list, associating the specific authorities with the matched grade roles according to basic information of specific management and control users, establishing user role matching rules to ensure that each management and control user can only execute operations allowed by the roles, and automatically managing and controlling the role distribution and authority updating flows of the users according to the user role matching rules by using an RPA tool.
The permission list comprises a checking permission, a modification permission, an auditing permission, a configuration permission and an operation permission, wherein the checking permission comprises a log checking permission, a flow monitoring permission and a data access permission, the modification permission comprises a flow configuration permission, a data input permission and a variable management permission, the auditing permission comprises a flow auditing permission and an exception handling permission, the configuration permission comprises a user management permission, an RPA (remote procedure A) setting permission and a permission management permission, and the operation permission comprises a starting/stopping flow permission and a scheduling permission.
In still another aspect, the invention further provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the RPA-based bad account automation management method according to any embodiment of the invention when executing the computer program.
In yet another aspect, the present invention further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements an RPA-based bad account automation management method according to any embodiment of the present invention.
Compared with the prior art, the invention has the following technical effects:
The automatic management and control method for the general business bad account based on the RPA technology provided by the invention has the advantages that the transaction information and the account information are automatically put into a lake and preprocessed, the manual intervention time is obviously reduced, the speed and accuracy of data processing are improved, the decision can be quickly made according to the set rule through the RPA automatic call of the bad account management and control flow processing script, the identification and processing speed of the bad account is improved, meanwhile, the security and management efficiency are improved, the systematicness and automation level of the whole bad account management and control flow are improved, and the complexity and the labor cost of risk management are reduced by defining different grades of roles and distributing corresponding rights, ensuring that a user can only execute the operations allowed by the roles.
Drawings
Fig. 1 is an overall flowchart of the RPA-based bad account automation management and control method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in conjunction with specific embodiments of the present application.
Example 1
The embodiment provides an automatic bad account management and control method based on RPA, referring to FIG. 1, comprising the following steps:
S1, establishing connection with a general service data source, inputting transaction information and account information generated by general service into a data lake warehouse, creating a personal customer temporary table and a public customer temporary table according to customer numbers, and performing data preprocessing on the two temporary tables to obtain a preliminary screening account.
As a preferred implementation manner of this embodiment, the data preprocessing of the two types of temporary tables in step S1 specifically includes:
And creating a personal client temporary table and a public client temporary table according to the client numbers, wherein fields in the personal client temporary table and the public client temporary table at least comprise the client numbers, the client names, bad loan balances and loan balances.
The queried personal new generation client number and related information field are inserted into a personal temporary table, the queried public new generation client number and related information field are inserted into a public temporary table, and the personal client temporary table and the public client temporary table are combined according to the personal new generation client number and the public new generation client number to obtain a combined table.
The method comprises the steps of inserting a second-class component number of a management right into a merging table, a personal receipt and payment state and a public receipt and payment state field, removing records with the same client number but abnormal personal receipt and payment state (including receipt only, payment only, closure (no payment) and closure), forced receipt only, forced closure, closure stop, forced closure, state uncertainty and forced closure (can be resolved)), merging records with bad loans for the public and the personal, merging records with modified client names for the public or the personal, removing records with bad loans for the public and the personal, and removing records with empty client numbers for the public to obtain a result table.
And comparing the result list with the client white list, and removing clients contained in the white list to obtain a preliminary screening account.
S2, designing a bad account management and control flow rule to be automated according to service requirements, extracting account attribute data from a data lake warehouse according to a primary screening account by using an RPA, executing the bad account management and control flow rule to obtain a decision result, and executing management and control measures by using the RPA according to the decision result.
As a preferred implementation manner of this embodiment, the step S2 specifically includes:
And designing a poor account management and control flow rule to be automated according to business requirements, extracting account attribute data from a data lake warehouse by using RPA according to the primary screening account, storing the data into a format (such as CSV, excel and the like) for subsequent processing, and carrying out standardized processing on the account attribute data, wherein the account attribute data at least comprises a product code, risk classification, loan debt balance, account state and customer grade.
The bad account management and control flow rule is specifically to set threshold indexes of bad account parameters of different grades and types, define processing logic of the bad accounts of different grades and types through conditional sentences, adjust the threshold indexes regularly according to market or policy changes at the same time, and the RPA automatically calls the bad account management and control flow processing script according to the bad account management and control flow rule and dynamically scores according to the weight of each bad parameter to obtain a comprehensive scoring result, and determine the risk grade of the bad account according to the comprehensive scoring result to obtain a decision result.
And executing control measures by using the RPA according to the decision result, wherein the control measures comprise updating and marking bad account states, recording control operation logs and automatically generating control users corresponding to the notification multi-channel pushing, so as to ensure traceability of audit.
Further, the effect of the management and control measures can be monitored regularly, the follow-up performance of the bad account can be analyzed, the feedback of the user can be collected and controlled, and the effectiveness and the defects of the automatic flow are known. And meanwhile, according to feedback and effect analysis, the bad account management and control flow rule and dynamic scoring weight are adjusted, and the flow is optimized.
As a preferred implementation of this embodiment, the method further comprises:
and S3, defining roles of different grades, distributing specific authorities required by each class of roles according to service requirements, establishing a authority list, associating the roles with the matched grade roles according to basic information of specific management and control users, establishing a user role matching rule to ensure that each management and control user can only execute operations allowed by the roles, and automatically managing and controlling the role distribution and authority updating flow of the users according to the user role matching rule by using an RPA tool.
The permission list comprises a viewing permission, a modification permission, an auditing permission, a configuration permission and an operation permission, wherein the viewing permission comprises a log viewing permission, a flow monitoring permission and a data access permission. The log viewing authority is used for allowing a user to view the RPA operation log and the running state, the process monitoring authority is used for allowing the user to monitor the running condition of an automatic process, including successful and failed records, and the data access authority is used for supporting the user to perform multidimensional batch or single-stroke query but not allowing modification.
The modification authority comprises a flow configuration authority, a data input authority and a variable management authority. The process configuration permission is used for allowing a user to modify configuration parameters of an existing automatic process, the data entry permission is used for allowing the user to add or update account attribute data records in a system, and the variable management permission is used for allowing the user to modify parameter indexes used in an automatic process.
The auditing authority comprises a flow auditing authority and an exception handling authority. The process auditing authority is used for allowing a user to audit and approve a new automatic process or a modification request, and the exception handling authority is used for allowing the user to check and handle the exception condition in the operation of the automatic process and carrying out corresponding adjustment.
The configuration rights comprise user management rights, RPA setting rights and rights management rights. The user management authority is used for allowing a user to create, modify and delete other users, the RPA setting authority is used for allowing the user to modify global settings of the RPA platform, such as security, data storage and the like, and the authority management authority is used for allowing the user to allocate or modify the authority for other roles and managing the association of the roles and the authorities.
The operation authority comprises a start/stop flow authority and a scheduling authority. The starting/stopping process authority is used for allowing a user to manually start or stop a specific automatic process, and the scheduling authority is used for allowing the user to set the running time and the running frequency of the automatic process.
Example two
Correspondingly, the embodiment provides an RPA-based bad account automatic management and control system, which comprises a data lake entering and preprocessing module, a bad account management and control module and a user role and authority management module.
The data entering and preprocessing module is used for entering transaction information and account information generated by general business into the data lake warehouse, creating a personal customer temporary table and a public customer temporary table according to customer numbers, preprocessing data of the two temporary tables to obtain a preliminary screening account, and the module is used for realizing the function of the step S1 in the first embodiment and is not repeated here.
The poor account management and control module is used for designing a poor account management and control flow rule to be automated according to service requirements, extracting account attribute data from a data lake warehouse according to a primary screening account by using an RPA, executing the poor account management and control flow rule to obtain a decision result, and executing management and control measures by using the RPA according to the decision result, wherein the module is used for realizing the function of the step S2 in the first embodiment and is not described herein again.
Defining different grade roles, distributing specific authorities required by each grade role according to service requirements, establishing a authority list, associating the specific authorities with the matched grade roles according to basic information of specific management and control users, establishing a user role matching rule to ensure that each management and control user can only execute operations allowed by the roles, and automatically managing and controlling the role distribution and authority updating flow of the users according to the user role matching rule by using an RPA tool;
The authority list comprises a checking authority, a modifying authority, an auditing authority, a configuration authority and an operation authority, wherein the checking authority comprises a log checking authority, a flow monitoring authority and a data access authority, the modifying authority comprises a flow configuration authority, a data input authority and a variable management authority, the auditing authority comprises a flow auditing authority and an exception handling authority, the configuration authority comprises a user management authority, an RPA (remote procedure A) setting authority and an authority management authority, the operation authority comprises a starting/stopping flow authority and a scheduling authority, and the module is used for realizing the function of the step S3 in the first embodiment and is not repeated herein.
Example III
The embodiment provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the RPA-based bad account automatic management and control method according to any embodiment of the invention when executing the computer program.
Example IV
The present embodiment provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements an RPA-based bad account automation management method according to any of the embodiments of the present invention.
In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relation of association objects, and indicates that there may be three kinds of relations, for example, a and/or B, and may indicate that a alone exists, a and B together, and B alone exists. Wherein A, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of the following" and the like means any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c may represent a, b, c, a and b, a and c, b and c, or a and b and c, wherein a, b, c may be single or plural.
Those of ordinary skill in the art will appreciate that the various elements and algorithm steps described in the embodiments disclosed herein can be implemented as a combination of electronic hardware, computer software, and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In several embodiments provided by the present application, any of the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes various media capable of storing program codes, such as a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present invention.
Claims (8)
1. The method for automatically controlling the bad account based on the RPA is characterized by comprising the following steps of:
S1, transaction information and account information generated by general business are put into a data lake warehouse, a personal customer temporary table and a public customer temporary table are created according to customer numbers, and data preprocessing is carried out on the two types of temporary tables to obtain a preliminary screening account;
s2, designing a bad account management and control flow rule to be automated according to service requirements, extracting account attribute data from a data lake warehouse by using an RPA according to a primary screening account, executing the bad account management and control flow rule to obtain a decision result, and executing management and control measures by using the RPA according to the decision result, wherein the step S2 specifically comprises the following steps:
Designing a bad account management and control flow rule to be automated according to business requirements, extracting account attribute data from a data lake warehouse by using RPA according to a preliminary screening account, and carrying out standardized processing on the account attribute data, wherein the account attribute data at least comprises a product code, risk classification, loan debt balance, account state and customer grade;
The bad account management and control flow rule is specifically to set threshold indexes of bad account parameters of different grades and types, define processing logic of the bad accounts of different grades and types through conditional sentences, adjust the threshold indexes regularly according to market or policy changes at the same time, and the RPA automatically calls the bad account management and control flow processing script according to the bad account management and control flow rule and dynamically scores according to the weight of each bad parameter to obtain a comprehensive scoring result, and determine the risk grade of the bad account according to the comprehensive scoring result to obtain a decision result;
According to the decision result, using RPA to execute control measures, including updating and marking bad account state, recording control operation log and automatically generating notification multi-channel pushing corresponding control users;
S3, defining roles of different grades, distributing specific authorities required by each grade of roles according to service requirements, establishing a authority list, associating the specific authorities with the matched grade roles according to basic information of specific management and control users, establishing a user role matching rule to ensure that each management and control user can only execute operations allowed by the roles, and automatically managing and controlling the role distribution and authority updating flow of the users according to the user role matching rule by using an RPA tool;
the permission list comprises a checking permission, a modification permission, an auditing permission, a configuration permission and an operation permission, wherein the checking permission comprises a log checking permission, a flow monitoring permission and a data access permission, the modification permission comprises a flow configuration permission, a data input permission and a variable management permission, the auditing permission comprises a flow auditing permission and an exception handling permission, the configuration permission comprises a user management permission, an RPA (remote procedure A) setting permission and a permission management permission, and the operation permission comprises a starting/stopping flow permission and a scheduling permission.
2. The method for automatically managing and controlling the bad account based on the RPA according to claim 1, wherein the data preprocessing of the two types of temporary tables in the step S1 is specifically:
Creating a personal client temporary table and a public client temporary table according to client numbers, wherein fields in the personal client temporary table and the public client temporary table at least comprise client numbers, client names, bad loan balances and loan balances;
Inserting the queried personal new generation client number and related information field into a personal temporary table, inserting the queried public new generation client number and related information field into a public temporary table, and merging the personal client temporary table and the public client temporary table according to the personal new generation client number and the public new generation client number to obtain a merged table;
Inserting a management right second-level component number, a personal receipt and payment state and a public receipt and payment state field into the merging table, removing records with the same client number but abnormal personal receipt and payment state, merging records with bad loans for both the public and the personal, merging records with client names modified for both the public and the personal, removing records with bad loans for both the public and the personal, and removing records with empty client numbers for the public to obtain a result table;
and comparing the result list with the client white list, and removing clients contained in the white list to obtain a preliminary screening account.
3. An RPA-based bad account automatic management and control system, wherein the system is used for realizing the RPA-based bad account automatic management and control method according to any one of claims 1 to 2, and comprises a data lake entering and preprocessing module and a bad account management and control module;
The data lake entering and preprocessing module is used for entering transaction information and account information generated by general business into a data lake bin, creating a personal customer temporary table and a public customer temporary table according to customer numbers, and preprocessing data of the two temporary tables to obtain a primary screening account;
And the bad account management and control module is used for designing a bad account management and control flow rule to be automated according to service requirements, extracting account attribute data from the data lake warehouse according to the primary screening account by using the RPA, executing the bad account management and control flow rule to obtain a decision result, and executing management and control measures by using the RPA according to the decision result.
4. The RPA-based bad account automation management and control system according to claim 3, wherein the data preprocessing of the two types of temporary tables in the data lake entering and preprocessing module is specifically:
Creating a personal client temporary table and a public client temporary table according to client numbers, wherein fields in the personal client temporary table and the public client temporary table at least comprise client numbers, client names, bad loan balances and loan balances;
Inserting the queried personal new generation client number and related information field into a personal temporary table, inserting the queried public new generation client number and related information field into a public temporary table, and merging the personal client temporary table and the public client temporary table according to the personal new generation client number and the public new generation client number to obtain a merged table;
Inserting a management right second-level component number, a personal receipt and payment state and a public receipt and payment state field into the merging table, removing records with the same client number but abnormal personal receipt and payment state, merging records with bad loans for both the public and the personal, merging records with client names modified for both the public and the personal, removing records with bad loans for both the public and the personal, and removing records with empty client numbers for the public to obtain a result table;
and comparing the result list with the client white list, and removing clients contained in the white list to obtain a preliminary screening account.
5. The RPA-based bad account automation management system of claim 3, wherein the bad account management module is specifically configured to:
Designing a bad account management and control flow rule to be automated according to business requirements, extracting account attribute data from a data lake warehouse by using RPA according to a preliminary screening account, and carrying out standardized processing on the account attribute data, wherein the account attribute data at least comprises a product code, risk classification, loan debt balance, account state and customer grade;
The bad account management and control flow rule is specifically to set threshold indexes of bad account parameters of different grades and types, define processing logic of the bad accounts of different grades and types through conditional sentences, adjust the threshold indexes regularly according to market or policy changes at the same time, and the RPA automatically calls the bad account management and control flow processing script according to the bad account management and control flow rule and dynamically scores according to the weight of each bad parameter to obtain a comprehensive scoring result, and determine the risk grade of the bad account according to the comprehensive scoring result to obtain a decision result;
And executing the control measures by using the RPA according to the decision result, wherein the steps comprise updating and marking the state of the bad account, recording the control operation log and automatically generating a notification multi-channel pushing corresponding control user.
6. The RPA-based bad account automation management system of claim 3, further comprising a user role and rights management module to:
Defining roles of different grades, distributing specific authorities required by each grade of roles according to service requirements, establishing an authority list, associating the specific authorities with the matched grade roles according to basic information of specific management and control users, establishing user role matching rules to ensure that each management and control user can only execute operations allowed by the roles, and automatically managing and controlling the role distribution and authority updating flow of the users according to the user role matching rules by using an RPA tool;
the permission list comprises a checking permission, a modification permission, an auditing permission, a configuration permission and an operation permission, wherein the checking permission comprises a log checking permission, a flow monitoring permission and a data access permission, the modification permission comprises a flow configuration permission, a data input permission and a variable management permission, the auditing permission comprises a flow auditing permission and an exception handling permission, the configuration permission comprises a user management permission, an RPA (remote procedure A) setting permission and a permission management permission, and the operation permission comprises a starting/stopping flow permission and a scheduling permission.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the RPA-based bad account automation management method according to any one of claims 1 to 2 when executing the computer program.
8. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the RPA-based bad account automation management method according to any one of claims 1 to 2.
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