RPA-based automated bad account management and control method, system, device, and readable storage medium

Abstract

The present invention discloses an automated bad account management method, system, device and readable storage medium based on RPA, wherein the method comprises: S1: entering transaction information and account information generated by inclusive business into a data lake warehouse, creating a personal customer temporary table and a corporate customer temporary table according to the customer number, performing data preprocessing on the two types of temporary tables, and obtaining preliminary screening accounts; S2: designing bad account management process rules that need to be automated according to business needs, using RPA to extract account attribute data from the data lake warehouse according to the preliminary screening accounts, executing the bad account management process rules to obtain decision results, and using RPA to execute management measures based on the decision results. The present invention solves the problem that existing bad customer account management methods lack effective integration of multi-source data and lack decision support for automated rules, resulting in the inability to quickly identify and control bad accounts.

Description

Technical Field

[0001] The present invention belongs to the field of financial services technology, and in particular relates to an RPA-based automated bad account management method, system, device, and readable storage medium. Background Art

[0002] Currently, the NPL management process primarily operates through centralized, post-loan operations, initiated manually and requiring detailed inquiries and operations on each NPL and its associated accounts. This creates a significant workload. Some NPL customers continue to open new accounts after being removed from the balance sheet, making it easy to overlook control actions. Consequently, NPL account management efforts lack both timeliness and comprehensiveness.

[0003] Existing inclusive loan businesses reach customers through a variety of channels, including WeChat mini-programs, mobile banking, various tablets, and online banking. Transaction and account information is stored in legacy system components. The management of nonperforming accounts relies on reports and manual methods, which can lead to misjudgments and inaccurate analysis, easily leading to customer complaints. Furthermore, manual management suffers from data silos and time lags, resulting in incomplete and in-time risk identification, which can easily cause bank losses and create certain risks.

[0004] A Chinese patent with publication number CN110060149A discloses a post-loan risk management method and system for obtaining account information, capital flow data, and operating data of a post-loan monitoring target, and pre-processing the above data to generate a time-capital image, so as to monitor the capital usage and capital effectiveness of the post-loan monitoring target in real time, thereby reducing the capital risk of the post-loan monitoring target. The invention embodiment method includes: obtaining the account information and capital flow data of the post-loan monitoring target; obtaining the operating data of the post-loan monitoring target; pre-processing the account information and capital flow data and the operating data to remove duplicate data and obtain the operating information and inflow and outflow data of the post-loan monitoring target; generating a time-capital image based on the operating information and inflow and outflow data according to a time period, so as to evaluate the capital usage and capital effectiveness of the post-loan monitoring target. The method described in the invention mainly focuses on generating a time-capital image for monitoring, and lacks in-depth analysis and intelligent judgment of the data. Summary of the Invention

[0005] The present invention provides an RPA-based automated bad account management method, system, device and readable storage medium, aiming to solve the problem that existing bad customer account management methods lack effective integration of multi-source data and lack decision support for automated rules, resulting in the inability to quickly identify and manage bad accounts.

[0006] To solve the above technical problems, the present invention provides an automated management and control method for bad accounts based on RPA, comprising the following steps:

[0007] S1: Store the transaction information and account information generated by inclusive business into the data lake warehouse, create temporary tables for personal customers and corporate customers based on customer numbers, pre-process the data in the two types of temporary tables, and obtain preliminary screening accounts.

[0008] S2: Design automated bad account control process rules based on business needs. Use RPA to extract account attribute data from the data lake warehouse based on preliminary account screening. Execute the bad account control process rules to obtain decision results. Based on the decision results, use RPA to implement control measures.

[0009] Preferably, the data preprocessing of the two types of temporary tables in step S1 is specifically as follows:

[0010] A personal customer temporary table and a corporate customer temporary table are created according to the customer number. The fields in the personal customer temporary table and the corporate customer temporary table include at least the customer number, customer name, non-performing loan balance, and loan balance.

[0011] Insert the retrieved new generation personal customer number and related information fields into the personal temporary table, insert the retrieved new generation corporate customer number and related information fields into the corporate temporary table, and merge the personal customer temporary table and the corporate customer temporary table based on the new generation personal customer number and the new generation corporate customer number to obtain a merged table.

[0012] Insert the account management secondary branch number, personal collection and payment status, and corporate collection and payment status fields into the merged table, remove records with identical customer numbers but abnormal personal collection and payment status, merge records with both corporate and personal non-performing loans, merge records with corporate or personal customer name modifications, remove records with both corporate and personal non-performing loans, and remove records with empty corporate customer numbers to obtain the result table.

[0013] Compare the result table with the customer whitelist, and eliminate the customers included in the whitelist to obtain the preliminary screening accounts.

[0014] Preferably, the step S2 is specifically as follows:

[0015] Design automated bad account management process rules based on business needs, use RPA to extract account attribute data from the data lake warehouse based on preliminary account screening, and standardize the account attribute data. The account attribute data includes at least: product code, risk classification, loan debt balance, account status, and customer level.

[0016] The specific rules of the bad account management process are to set threshold indicators for bad account parameters of different levels and types, define the processing logic of bad accounts of different levels and types through conditional statements, and regularly adjust the threshold indicators according to market or policy changes. RPA automatically calls the bad account management process processing script according to the bad account management process rules, and dynamically scores each bad parameter based on the weight to obtain a comprehensive scoring result. The comprehensive scoring result is used to determine the risk level of the bad account and obtain the decision result.

[0017] Based on the decision results, RPA is used to execute control measures, including updating and marking bad account status, recording control operation logs, and automatically generating notifications to push corresponding control users through multiple channels.

[0018] Preferably, the method further comprises:

[0019] S3: Define roles of different levels, assign specific permissions required for each level role based on business needs and establish a permission list. Associate specific managed users with matching level roles based on their basic information. Establish user role matching rules to ensure that each managed user can only perform operations allowed by their role. Use RPA tools to automate the role assignment and permission update process for managed users based on user role matching rules.

[0020] The permission list includes: viewing permission, modification permission, audit permission, configuration permission and operation permission; the viewing permission includes log viewing permission, process monitoring permission and data access permission; the modification permission includes process configuration permission, data entry permission and variable management permission; the audit permission includes process audit permission and exception handling permission; the configuration permission includes user management permission, RPA setting permission and permission management permission; the operation permission includes start / stop process permission and scheduling permission.

[0021] On the other hand, the present invention provides an RPA-based automated bad account management system, including a data entry and preprocessing module and a bad account management module.

[0022] The data lake entry and preprocessing module is used to enter the transaction information and account information generated by inclusive business into the data lake warehouse, create temporary tables for personal customers and corporate customers based on customer numbers, and preprocess the data in the two types of temporary tables to obtain preliminary screening accounts.

[0023] The bad account control module is used to design automated bad account control process rules based on business needs. It uses RPA to extract account attribute data from the data lake warehouse based on preliminary account screening, executes the bad account control process rules to obtain decision results, and uses RPA to implement control measures based on the decision results.

[0024] Preferably, the data preprocessing for the two types of temporary tables in the data lake entry and preprocessing module is specifically as follows:

[0025] A personal customer temporary table and a corporate customer temporary table are created according to the customer number. The fields in the personal customer temporary table and the corporate customer temporary table include at least the customer number, customer name, non-performing loan balance, and loan balance.

[0026] Insert the retrieved new generation personal customer number and related information fields into the personal temporary table, insert the retrieved new generation corporate customer number and related information fields into the corporate temporary table, and merge the personal customer temporary table and the corporate customer temporary table based on the new generation personal customer number and the new generation corporate customer number to obtain a merged table.

[0027] Insert the account management secondary branch number, personal collection and payment status, and corporate collection and payment status fields into the merged table, remove records with identical customer numbers but abnormal personal collection and payment status, merge records with both corporate and personal non-performing loans, merge records with corporate or personal customer name modifications, remove records with both corporate and personal non-performing loans, and remove records with empty corporate customer numbers to obtain the result table.

[0028] Compare the result table with the customer whitelist, and eliminate the customers included in the whitelist to obtain the preliminary screening accounts.

[0029] Preferably, the bad account management module is specifically used to:

[0030] Design automated bad account management process rules based on business needs, use RPA to extract account attribute data from the data lake warehouse based on preliminary account screening, and standardize the account attribute data. The account attribute data includes at least: product code, risk classification, loan debt balance, account status, and customer level.

[0031] The specific rules of the bad account management process are to set threshold indicators for bad account parameters of different levels and types, define the processing logic of bad accounts of different levels and types through conditional statements, and regularly adjust the threshold indicators according to market or policy changes. RPA automatically calls the bad account management process processing script according to the bad account management process rules, and dynamically scores each bad parameter based on the weight to obtain a comprehensive scoring result. The comprehensive scoring result is used to determine the risk level of the bad account and obtain the decision result.

[0032] Based on the decision results, RPA is used to execute control measures, including updating and marking bad account status, recording control operation logs, and automatically generating notifications to push corresponding control users through multiple channels.

[0033] Preferably, the system further includes a user role and authority management module, which is used to:

[0034] Define roles of different levels, assign specific permissions required for each level role based on business needs and establish a permission list. Associate specific managed users with matching level roles based on their basic information, establish user role matching rules to ensure that each managed user can only perform operations allowed by their role, and use RPA tools to automate the role assignment and permission update process for managed users based on user role matching rules.

[0035] The permission list includes: viewing permission, modification permission, audit permission, configuration permission and operation permission; the viewing permission includes log viewing permission, process monitoring permission and data access permission; the modification permission includes process configuration permission, data entry permission and variable management permission; the audit permission includes process audit permission and exception handling permission; the configuration permission includes user management permission, RPA setting permission and permission management permission; the operation permission includes start / stop process permission and scheduling permission.

[0036] On the other hand, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the RPA-based automated bad account management method as described in any embodiment of the present invention is implemented.

[0037] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for automated management and control of bad accounts based on RPA as described in any embodiment of the present invention is implemented.

[0038] Compared with the prior art, the present invention has the following technical effects:

[0039] The automated management and control method for inclusive business bad accounts based on RPA technology proposed in the present invention significantly reduces manual intervention time and improves the speed and accuracy of data processing by automatically entering transaction information and account information into the lake and pre-processing it; through RPA automatic calling of the bad account management process processing script, it can quickly make decisions based on the set rules, thereby improving the identification and processing speed of bad accounts; at the same time, by defining different levels of roles and assigning corresponding permissions, it ensures that the management users can only perform operations permitted by their roles, thereby improving security and management efficiency, and improving the systematic and automated level of the entire bad account management process, thereby reducing the complexity of risk management and labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is the overall flow chart of the RPA-based automated bad account management method described in the present invention. DETAILED DESCRIPTION

[0041] In order to make the objectives, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in combination with specific embodiments of the present application and with reference to the accompanying drawings.

[0042] Example 1

[0043] This embodiment provides an automated management method for bad accounts based on RPA. Figure 1 As shown, the following steps are included:

[0044] S1: Establish a connection with the inclusive business data source, store the transaction information and account information generated by the inclusive business into the data lake warehouse, create temporary tables for personal customers and corporate customers based on customer numbers, pre-process the data in the two types of temporary tables, and obtain preliminary screening accounts.

[0045] As a preferred implementation of this embodiment, the data preprocessing of the two types of temporary tables in step S1 is specifically as follows:

[0046] A personal customer temporary table and a corporate customer temporary table are created according to the customer number. The fields in the personal customer temporary table and the corporate customer temporary table include at least the customer number, customer name, non-performing loan balance, and loan balance.

[0047] Insert the retrieved new generation personal customer number and related information fields into the personal temporary table, insert the retrieved new generation corporate customer number and related information fields into the corporate temporary table, and merge the personal customer temporary table and the corporate customer temporary table based on the new generation personal customer number and the new generation corporate customer number to obtain a merged table.

[0048] Insert the second-level branch number of account management authority, personal collection and payment status, and corporate collection and payment status fields into the merged table; remove records with equal customer numbers but abnormal personal collection and payment status (including only collection but not payment, only payment but not collection, sealing (no payment and no collection), forced collection but not payment, forced payment but not collection, forced sealing, stop collection + forced non-payment, uncertain status, forced sealing (interest can be settled)), merge records with both corporate and personal non-performing loans, merge records with corporate or personal modified customer names, remove records with both corporate and personal non-performing loans, and remove records with empty corporate customer numbers to obtain the result table.

[0049] Compare the result table with the customer whitelist, and eliminate the customers included in the whitelist to obtain the preliminary screening accounts.

[0050] S2: Design automated bad account control process rules based on business needs. Use RPA to extract account attribute data from the data lake warehouse based on preliminary account screening. Execute the bad account control process rules to obtain decision results. Based on the decision results, use RPA to implement control measures.

[0051] As a preferred implementation of this embodiment, step S2 is specifically as follows:

[0052] Design automated bad account management process rules based on business needs, use RPA to extract account attribute data from the data lake warehouse based on preliminary account screening, save the data in a format suitable for subsequent processing (such as CSV, Excel, etc.), and standardize the account attribute data. The account attribute data includes at least: product code, risk classification, loan debt balance, account status, and customer level.

[0053] The specific rules of the bad account management process are to set threshold indicators for bad account parameters of different levels and types, define the processing logic of bad accounts of different levels and types through conditional statements, and regularly adjust the threshold indicators according to market or policy changes. RPA automatically calls the bad account management process processing script according to the bad account management process rules, and dynamically scores each bad parameter based on the weight to obtain a comprehensive scoring result. The comprehensive scoring result is used to determine the risk level of the bad account and obtain the decision result.

[0054] Based on the decision results, RPA is used to implement control measures, including updating and marking bad account status, recording control operation logs, and automatically generating notifications to push corresponding control users through multiple channels to ensure audit traceability.

[0055] Furthermore, the effectiveness of control measures can be regularly monitored, the subsequent performance of bad accounts can be analyzed, and feedback from control users can be collected to understand the effectiveness and shortcomings of the automated process. Based on this feedback and effectiveness analysis, the rules and dynamic scoring weights of the bad account control process can be adjusted to optimize the process.

[0056] As a preferred implementation of this embodiment, the method further includes:

[0057] S3: Define roles of different levels, assign specific permissions required for each level role based on business needs and establish a permission list. Associate specific managed users with matching level roles based on their basic information. Establish user role matching rules to ensure that each managed user can only perform operations allowed by their role. Use RPA tools to automate the role assignment and permission update process for managed users based on user role matching rules.

[0058] The permissions list includes: viewing permissions, modify permissions, audit permissions, configuration permissions, and operation permissions. View permissions include log viewing permissions, process monitoring permissions, and data access permissions. Log viewing permissions allow users to view RPA operation logs and operating status; process monitoring permissions allow users to monitor the operation of automated processes, including success and failure records; and data access permissions allow users to perform multi-dimensional batch or single query operations, but do not allow modification.

[0059] The modification permissions include process configuration permissions, data entry permissions, and variable management permissions. The process configuration permission allows users to modify the configuration parameters of existing automated processes; the data entry permission allows users to add or update account attribute data records in the system; and the variable management permission allows users to modify parameter indicators used in the automation process.

[0060] The review authority includes process review authority and exception handling authority. The process review authority allows users to review and approve new automated processes or modification requests; the exception handling authority allows users to view and handle exceptions in the operation of automated processes and make corresponding adjustments.

[0061] Configuration permissions include user management permissions, RPA settings permissions, and permissions management permissions. User management permissions allow users to create, modify, and delete other users; RPA settings permissions allow users to modify global settings of the RPA platform, such as security and data storage; and permissions management permissions allow users to assign or modify permissions to other roles and manage the association between roles and permissions.

[0062] The operation permissions include the start / stop process permission and the scheduling permission. The start / stop process permission allows users to manually start or stop a specific automated process; the scheduling permission allows users to set the running time and frequency of the automated process.

[0063] Example 2

[0064] Accordingly, this embodiment provides an RPA-based automated bad account management system, including a data lake entry and preprocessing module, a bad account management module, and a user role and permission management module.

[0065] The data entry and preprocessing module is used to enter the transaction information and account information generated by inclusive business into the data lake warehouse, create temporary tables for personal customers and corporate customers based on customer numbers, and preprocess the data in the two types of temporary tables to obtain preliminary screening accounts. This module is used to implement the functions of step S1 in Example 1 and will not be repeated here.

[0066] The bad account management module is used to design automated bad account management process rules based on business needs. It uses RPA to extract account attribute data from the data lake warehouse based on preliminary account screening, executes the bad account management process rules to obtain decision results, and uses RPA to implement management measures based on the decision results. This module is used to implement the functions of step S2 in Example 1 and will not be repeated here.

[0067] The user role and permission management module is used to: define roles of different levels, assign specific permissions to each level role based on business needs and establish a permission list; associate specific managed users with matching level roles based on their basic information; establish user role matching rules to ensure that each managed user can only perform operations permitted by their role; and use RPA tools to automate the role assignment and permission update process for managed users based on user role matching rules;

[0068] The permission list includes: viewing permission, modification permission, audit permission, configuration permission and operation permission; the viewing permission includes log viewing permission, process monitoring permission, and data access permission; the modification permission includes process configuration permission, data entry permission, and variable management permission; the audit permission includes process audit permission and exception handling permission; the configuration permission includes user management permission, RPA setting permission, and permission management permission; the operation permission includes start / stop process permission and scheduling permission. This module is used to implement the function of step S3 in Example 1 and will not be repeated here.

[0069] Example 3

[0070] This embodiment provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the RPA-based automated bad account management method as described in any embodiment of the present invention is implemented.

[0071] Example 4

[0072] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the RPA-based automated bad account management method as described in any embodiment of the present invention is implemented.

[0073] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c or a and b and c, where a, b, c can be single or multiple.

[0074] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0075] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0076] In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), magnetic disk or optical disk, and other media that can store program code.

[0077] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An automated management and control method for bad accounts based on RPA, characterized in that: The following steps are involved: S1: Store transaction and account information generated by inclusive finance services into the data lake warehouse. Create temporary tables for individual customers and corporate customers based on customer IDs. Preprocess the data in these two temporary tables to obtain preliminary screening accounts. S2: Design automated bad account control process rules based on business needs. Use RPA to extract account attribute data from the data lake warehouse based on preliminary account screening. Execute the bad account control process rules to obtain decision results. Based on the decision results, use RPA to implement control measures. Step S2 is specifically as follows: Design automated bad account management process rules based on business requirements. Use RPA to extract account attribute data from the data lake warehouse based on preliminary account screening and standardize the account attribute data. The account attribute data includes at least: product code, risk classification, loan balance, account status, and customer level. The specific rules for the bad account control process include setting threshold indicators for bad account parameters of different levels and types, defining the processing logic for different levels and types of bad accounts through conditional statements, and regularly adjusting the threshold indicators based on market or policy changes. RPA automatically calls the bad account control process processing script based on the bad account control process rules and dynamically scores each bad parameter based on its weight to obtain a comprehensive scoring result. The comprehensive scoring result is used to determine the risk level of the bad account and obtain the decision result. Based on the decision results, RPA is used to execute control measures, including updating and marking bad account status, recording control operation logs, and automatically generating notifications to push to corresponding control users through multiple channels. S3: Define different levels of roles, assign specific permissions to each level of role based on business needs, and create a permissions list. Associate specific managed users with matching level roles based on their basic information. Establish user role matching rules to ensure that each managed user can only perform operations permitted by their role. Use RPA tools to automate the role assignment and permission update process for managed users based on the user role matching rules. The permission list includes: viewing permission, modification permission, audit permission, configuration permission and operation permission; the viewing permission includes log viewing permission, process monitoring permission and data access permission; the modification permission includes process configuration permission, data entry permission and variable management permission; the audit permission includes process audit permission and exception handling permission; the configuration permission includes user management permission, RPA setting permission and permission management permission; the operation permission includes start / stop process permission and scheduling permission.

2. The RPA-based automated bad account management method according to claim 1 is characterized in that: The data preprocessing of the two types of temporary tables in step S1 is specifically as follows: Creating a temporary table for individual customers and a temporary table for corporate customers based on the customer number, wherein the fields in the temporary table for individual customers and the temporary table for corporate customers include at least the customer number, customer name, non-performing loan balance, and loan balance; Insert the retrieved new generation personal customer number and related information fields into the personal temporary table, insert the retrieved new generation corporate customer number and related information fields into the corporate temporary table, and merge the personal customer temporary table and the corporate customer temporary table based on the new generation personal customer number and the new generation corporate customer number to obtain a merged table; Insert the account management authority secondary branch number, personal payment status, and corporate payment status fields into the merged table. Remove records with identical customer numbers but abnormal personal payment status. Merge records with both corporate and personal non-performing loans. Merge records with modified customer names for either the corporate or personal side. Remove records with both corporate and personal non-performing loans. Remove records with blank corporate customer numbers to obtain the result table. Compare the result table with the customer whitelist, and eliminate the customers included in the whitelist to obtain the preliminary screening accounts.

3. An RPA-based automated bad account management and control system, characterized by: The system is used to implement the RPA-based automated bad account management method according to any one of claims 1 to 2, including a data lake entry and preprocessing module and a bad account management module; The data lake ingestion and preprocessing module is used to store transaction and account information generated by inclusive finance services into the data lake warehouse. It then creates temporary tables for individual customers and corporate customers based on customer IDs, preprocesses data in both temporary tables, and obtains preliminary screening accounts. The bad account control module is used to design automated bad account control process rules based on business needs. It uses RPA to extract account attribute data from the data lake warehouse based on preliminary account screening, executes the bad account control process rules to obtain decision results, and uses RPA to implement control measures based on the decision results.

4. The RPA-based automated bad account management and control system according to claim 3 is characterized in that: The data preprocessing module performs the following steps on the two types of temporary tables: Creating a temporary table for individual customers and a temporary table for corporate customers based on the customer number, wherein the fields in the temporary table for individual customers and the temporary table for corporate customers include at least the customer number, customer name, non-performing loan balance, and loan balance; Insert the retrieved new generation personal customer number and related information fields into the personal temporary table, insert the retrieved new generation corporate customer number and related information fields into the corporate temporary table, and merge the personal customer temporary table and the corporate customer temporary table based on the new generation personal customer number and the new generation corporate customer number to obtain a merged table; Insert the account management authority secondary branch number, personal payment status, and corporate payment status fields into the merged table. Remove records with identical customer numbers but abnormal personal payment status. Merge records with both corporate and personal non-performing loans. Merge records with modified customer names for either the corporate or personal side. Remove records with both corporate and personal non-performing loans. Remove records with blank corporate customer numbers to obtain the result table. Compare the result table with the customer whitelist, and eliminate the customers included in the whitelist to obtain the preliminary screening accounts.

5. The RPA-based automated bad account management and control system according to claim 3 is characterized in that: The bad account control module is specifically used to: Design automated bad account management process rules based on business requirements. Use RPA to extract account attribute data from the data lake warehouse based on preliminary account screening and standardize the account attribute data. The account attribute data includes at least: product code, risk classification, loan balance, account status, and customer level. The specific rules for the bad account control process include setting threshold indicators for bad account parameters of different levels and types, defining the processing logic for different levels and types of bad accounts through conditional statements, and regularly adjusting the threshold indicators based on market or policy changes. RPA automatically calls the bad account control process processing script based on the bad account control process rules and dynamically scores each bad parameter based on its weight to obtain a comprehensive scoring result. The comprehensive scoring result is used to determine the risk level of the bad account and obtain the decision result. Based on the decision results, RPA is used to execute control measures, including updating and marking bad account status, recording control operation logs, and automatically generating notifications to push corresponding control users through multiple channels.

6. The RPA-based automated bad account management and control system according to claim 3 is characterized in that: The system further includes a user role and authority management module, which is used to: Define different levels of roles, assign specific permissions to each level of role based on business needs, and create a permission list. Associate specific managed users with matching level roles based on their basic information. Establish user role matching rules to ensure that each managed user can only perform operations permitted by their role. Use RPA tools to automate the role assignment and permission update process for managed users based on user role matching rules. The permission list includes: viewing permission, modification permission, audit permission, configuration permission and operation permission; the viewing permission includes log viewing permission, process monitoring permission and data access permission; the modification permission includes process configuration permission, data entry permission and variable management permission; the audit permission includes process audit permission and exception handling permission; the configuration permission includes user management permission, RPA setting permission and permission management permission; the operation permission includes start / stop process permission and scheduling permission.

7. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the RPA-based automated bad account management method according to any one of claims 1 to 2 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the RPA-based automated bad account management method according to any one of claims 1 to 2 is implemented.

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