Digital employee intelligent system based on financial system

Abstract

The invention relates to the technical field of financial intelligent systems, and provides a digital employee intelligent system based on a financial system, and the system comprises a data collection and processing module which is used for enabling a client to act firstly when the client initiates a transaction request, consults a question or has other business demands; the natural voice processing module is used for converting the voice of the client into a text through voice interaction and a voice recognition function, then analyzing the text content through a text understanding function, and extracting the intention and key information of the client; and the intelligent decision-making module is used for performing judgment and processing by a rule engine according to a preset service rule when facing a simple service request of a client, and performing risk assessment and client classification by a machine learning algorithm according to historical data when facing a complex service request of the client. Through the intelligent decision-making module, various complex service requests can be quickly and accurately processed, manual intervention is not needed, and the decision-making efficiency and the decision-making accuracy are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of financial intelligent systems, and in particular to a digital employee intelligent system based on a financial system. Background Art

[0002] In the financial sector, with the continuous expansion of business and the increasing diversification of customer needs, traditional decision-making methods face many challenges. The emergence of intelligent decision-making modules has brought new changes to the financial system.

[0003] Traditional financial decision-making often relies on manual experience and fixed rules, which has limitations. On the one hand, manual decision-making is inefficient and difficult to cope with massive business requests and complex market changes. On the other hand, fixed rules are difficult to adapt to the dynamic business environment, which can easily lead to inaccurate and untimely decisions.

[0004] Therefore, we made improvements to this and proposed a digital employee intelligence system based on the financial system. Summary of the invention

[0005] (I) The technical problem to be solved by the present invention is to be able to quickly and accurately process various complex business requests without manual intervention, thereby greatly improving decision-making efficiency and accuracy.

[0006] (II) Technical solution

[0007] In order to achieve the above-mentioned invention object, the present invention provides a digital employee intelligence system based on a financial system, comprising:

[0008] The data collection and processing module is used when customers initiate transaction requests, ask questions or have other business needs. The data collection and processing module takes action first;

[0009] Natural speech processing module, used for voice interaction. The speech recognition function converts the customer's speech into text, and then the text understanding function analyzes the text content to extract the customer's intention and key information;

[0010] Intelligent decision-making module: When facing simple business requests from customers, the rule engine will judge and process according to the preset business rules. When facing complex business requests from customers, the machine learning algorithm can perform risk assessment and customer classification based on historical data, and then optimize the financial business process through the optimization algorithm;

[0011] The task execution module is used for automated processes and will automatically execute the corresponding business processes. During the execution process, it will be integrated with external systems. At the same time, the task monitoring and management functions will monitor the task execution status in real time to ensure the smooth completion of the task;

[0012] Human-computer interaction module, used for human-computer interaction and reflecting the business processing results;

[0013] The safety and risk management module is used to monitor the entire operation process and perform risk management.

[0014] Preferably, the data collection and processing module includes a data collection unit, a data cleaning unit, a data conversion unit and a data storage unit. The data collection unit is used to collect relevant data from various data sources within the financial system and external data channels. The data cleaning unit is used to clean the collected data, remove duplicate, erroneous and incomplete data, and ensure the accuracy and consistency of the data. The data conversion unit is used to convert data in different formats into a unified format for subsequent analysis and processing. The data storage unit is used to store the processed data in a database or data warehouse for easy access and analysis at any time.

[0015] Preferably, the natural speech processing module includes a speech recognition unit, a text understanding unit, a language generation unit and a sentiment analysis unit. The speech recognition unit is used to convert the customer's voice instructions or consultations into text form so that the digital employee can understand and process them. The text understanding unit is used to analyze and understand the customer's text input and extract key information and intentions. The language generation unit is used to generate natural and fluent text replies based on the customer's questions and needs, and provide the customer with accurate answers and suggestions. The sentiment analysis unit is used to analyze the emotional tendencies in the customer's language expressions so that the digital employee can better understand the customer's emotional state and provide more personalized services.

[0016] Preferably, the intelligent decision-making module includes a business request unit, which is connected to the rule judgment unit through data transmission technology, and is used to find the corresponding rule set according to the business type and the specific content of the request when a business request comes in. The rule judgment unit is connected to the rule engine processing unit and the learning judgment unit through data transmission technology. The rule engine processing unit is used to automatically handle the business under the premise of meeting the rule conditions. The learning judgment unit is used to perform learning optimization judgment when the rule conditions are not met. The learning judgment unit is connected to the problem definition unit and the data preparation unit through data transmission technology. The problem definition unit is connected to the algorithm through data transmission technology. The algorithm selection unit is connected to the model building unit through data transmission technology, and is used to clarify the financial business problem that needs to be optimized, determine the optimization objectives and constraints, and select a suitable optimization algorithm according to the characteristics and scale of the problem. The algorithm selection unit is connected to the model building unit through data transmission technology, and is used to convert the optimization problem into a mathematical model and determine the specific expressions of the objective function and the constraints. The model building unit is connected to the algorithm solving unit through data transmission technology, and is used to use the selected optimization algorithm to solve the established mathematical model. The algorithm solving unit is connected to the result application unit through data transmission technology, and is used to output the optimization result when the algorithm finds the optimal solution or meets the stopping condition, and make corresponding decisions based on the optimization result.

[0017] Preferably, the data preparation unit is connected to the model training unit through data transmission technology, and is used to train various machine learning models using historical data under the premise of being applicable to machine learning. The model training unit is connected to the model application unit through data transmission technology, and is used to predict corresponding results based on input features. The model application unit is connected to the decision result output unit through data transmission technology, and is used to make corresponding decisions based on the prediction results of the model.

[0018] Preferably, the task execution module includes an automatic process execution unit, an external system integration unit and a task monitoring management unit. The automatic process execution unit is used to automatically execute various financial business processes according to the instructions of the intelligent decision-making module. The external system integration unit is used to integrate with other systems within the financial institution and the systems of external partners to achieve data sharing and business collaboration. The task monitoring management unit is used to monitor and manage the tasks performed by digital employees in real time to ensure the smooth completion of tasks and handle abnormal situations in a timely manner.

[0019] Preferably, the human-computer interaction module includes a voice interaction unit, a text interaction unit, a graphical interface interaction unit and a channel integration unit. The voice interaction unit is used to realize voice interaction between customers and digital employees through voice recognition and speech synthesis technology. The text interaction unit is used for text interaction between customers and digital employees to answer customers' questions and provide services. The graphical interface interaction unit is used to provide a graphical user interface so that customers can operate and manage. The channel integration unit is used for multi-channel integration so that customers can interact with digital employees anytime and anywhere.

[0020] Preferably, the multi-channel integration in the channel integration unit includes but is not limited to mobile APP, web pages, WeChat public accounts and computer clients.

[0021] Preferably, the security and risk management module includes an identity authentication and authorization unit, a data security encryption unit, a risk monitoring and early warning unit, and a background standard management unit. The identity authentication and authorization unit is used to ensure the authenticity and reliability of the customer's identity through a variety of identity authentication methods, and to authorize the operations of digital employees to prevent illegal access and operations. The data security encryption unit is used to ensure the safe storage and transmission of financial data and prevent data leakage and tampering. The risk monitoring and early warning unit is used to monitor the risk factors in financial business in real time and issue early warnings in time. The background standard management unit is used to ensure that the operations of digital employees comply with financial regulatory laws and internal rules and regulations.

[0022] The digital employee intelligent system based on the financial system provided by the present invention has the following beneficial effects:

[0023] 1. The intelligent decision-making module integrates advanced technologies such as rule engines, machine learning algorithms, and optimization algorithms, and can quickly and accurately handle various complex business requests. The rule engine can make decisions quickly according to preset rules, which is applicable to some common business scenarios; the machine learning algorithm can predict customer behavior and assess risks by learning a large amount of historical data, providing a more scientific basis for decision-making; the optimization algorithm can achieve the best decision in asset allocation, investment portfolio, etc., and improve the efficiency of financial institutions. The intelligent decision-making module integrates advanced technologies such as rule engines, machine learning algorithms, and optimization algorithms, and can quickly and accurately handle various complex business requests. The rule engine can make decisions quickly according to preset rules, which is applicable to some common business scenarios; the machine learning algorithm can predict customer behavior and assess risks by learning a large amount of historical data, providing a more scientific basis for decision-making; the optimization algorithm can achieve the best decision in asset allocation, investment portfolio, etc., and improve the efficiency of financial institutions.

[0024] 2. The intelligent decision-making module has a high level of automation and intelligence. It can automatically collect, process and analyze data without human intervention, greatly improving decision-making efficiency. At the same time, it can continuously learn and optimize its own decision-making model, adapt to the ever-changing market environment and customer needs, and improve the accuracy of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 The overall system diagram of the digital employee intelligence system based on the financial system provided for this application;

[0027] Figure 2 A schematic diagram of the data collection and processing module system of the digital employee intelligence system based on the financial system provided for this application;

[0028] Figure 3 A schematic diagram of a natural speech processing module system of a digital employee intelligence system based on a financial system provided for this application;

[0029] Figure 4 A schematic diagram of the intelligent decision-making module system of the digital employee intelligent system based on the financial system provided for this application;

[0030] Figure 5 A system diagram of the task execution module of the digital employee intelligence system based on the financial system provided for this application;

[0031] Figure 6 A schematic diagram of the human-computer interaction module system of the digital employee intelligent system based on the financial system provided for this application;

[0032] Figure 7 System diagram of the security and risk management module of the digital employee intelligent system based on the financial system provided for this application.

[0033] In the figure: 1. Data acquisition and processing module; 2. Natural speech processing module; 3. Intelligent decision-making module; 4. Task execution module; 5. Human-computer interaction module; 7. Safety and risk management module; 8. Data collection unit; 9. Data cleaning unit; 10. Data conversion unit; 11. Data storage unit; 12. Speech recognition unit; 13. Text understanding unit; 14. Language generation unit; 15. Sentiment analysis unit; 16. Business request unit; 17. Rule judgment unit; 18. Rule engine processing unit; 19. Learning judgment unit; 20. Problem definition unit; 21. Algorithm selection unit 1. Selection unit; 2. Model building unit; 2. Algorithm solving unit; 2. Result application unit; 2. Data preparation unit; 2. Model training unit; 2. Model application unit; 2. Decision result output unit; 2. Automatic process execution unit; 3. External system integration unit; 3. Task monitoring and management unit; 3. Voice interaction unit; 3. Text interaction unit; 3. Graphical interface interaction unit; 3. Channel integration unit; 3. Identity authentication and authorization unit; 3. Data security encryption unit; 3. Risk monitoring and early warning unit; 3. Background specification management unit. DETAILED DESCRIPTION

[0034] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples of the specification. The following examples are only used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0035] like Figure 1-Figure 7 As shown, this embodiment proposes a digital employee intelligent system based on a financial system, including:

[0036] Data collection and processing module 1: When a customer initiates a transaction request, asks a question, or has other business needs, the data collection and processing module will take action first;

[0037] Natural speech processing module 2, used for voice interaction. The speech recognition function converts the customer's speech into text, and then the text understanding function analyzes the text content to extract the customer's intention and key information;

[0038] Intelligent decision-making module 3: When facing simple business requests from customers, the rule engine will judge and process according to the preset business rules. When facing complex business requests from customers, the machine learning algorithm can perform risk assessment and customer classification based on historical data, and then optimize the financial business process through the optimization algorithm;

[0039] Task execution module 4 is used for automated processes and will automatically execute the corresponding business processes. During the execution process, it will be integrated with external systems. At the same time, the task monitoring and management function will monitor the task execution status in real time to ensure the smooth completion of the task;

[0040] Human-computer interaction module 5, used for human-computer interaction and reflecting the business processing results;

[0041] The safety and risk management module 7 is used to monitor the entire operation process and perform risk management.

[0042] In this embodiment, the data collection and processing module 1 includes a data collection unit 8, a data cleaning unit 9, a data conversion unit 10 and a data storage unit 11. The data collection unit 8 is used to collect relevant data from various data sources within the financial system and external data channels. The data cleaning unit 9 is used to clean the collected data, remove duplicate, erroneous and incomplete data, and ensure the accuracy and consistency of the data. The data conversion unit 10 is used to convert data in different formats into a unified format for subsequent analysis and processing. The data storage unit 11 is used to store the processed data in a database or data warehouse for easy access and analysis at any time.

[0043] In this embodiment, the natural speech processing module 2 includes a speech recognition unit 12, a text understanding unit 13, a language generation unit 14 and a sentiment analysis unit 15. The speech recognition unit 12 is used to convert the customer's voice instructions or consultations into text form so that the digital employee can understand and process them. The text understanding unit 13 is used to analyze and understand the customer's text input and extract key information and intentions. The language generation unit 14 is used to generate natural and fluent text replies based on the customer's questions and needs, and provide the customer with accurate answers and suggestions. The sentiment analysis unit 15 is used to analyze the emotional tendencies in the customer's language expressions so that the digital employee can better understand the customer's emotional state and provide more personalized services.

[0044] In this embodiment, the intelligent decision-making module 3 includes a business request unit 16, which is connected to the rule judgment unit 17 through data transmission technology, and is used to find the corresponding rule set according to the business type and the specific content of the request when a business request comes in. The rule judgment unit 17 is connected to the rule engine processing unit 18 and the learning judgment unit 19 through data transmission technology. The rule engine processing unit 18 is used to automatically handle the business under the premise of meeting the rule conditions. The learning judgment unit 19 is used to perform learning optimization judgment when the rule conditions are not met. The learning judgment unit 19 is connected to the problem definition unit 20 and the data preparation unit 25 through data transmission technology. The problem definition unit 20 is connected to the problem definition unit 20 through data transmission technology. The technology is connected to the algorithm selection unit 21, which is used to clarify the financial business problem that needs to be optimized, determine the optimization objectives and constraints, and select a suitable optimization algorithm according to the characteristics and scale of the problem. The algorithm selection unit 21 is connected to the model establishment unit 22 through data transmission technology, which is used to convert the optimization problem into a mathematical model and determine the specific expressions of the objective function and the constraints. The model establishment unit 22 is connected to the algorithm solving unit 23 through data transmission technology, which is used to use the selected optimization algorithm to solve the established mathematical model. The algorithm solving unit 23 is connected to the result application unit 24 through data transmission technology, which is used to output the optimization result when the algorithm finds the optimal solution or meets the stopping condition, and make corresponding decisions based on the optimization result.

[0045] In this embodiment, the data preparation unit 25 is connected to the model training unit 26 through data transmission technology, and is used to train various machine learning models using historical data under the premise of being applicable to machine learning. The model training unit 26 is connected to the model application unit 27 through data transmission technology, and is used to predict corresponding results based on input features. The model application unit 27 is connected to the decision result output unit 28 through data transmission technology, and is used to make corresponding decisions based on the prediction results of the model.

[0046] In this embodiment, the task execution module 4 includes an automatic process execution unit 29, an external system integration unit 30 and a task monitoring management unit 31. The automatic process execution unit 29 is used to automatically execute various financial business processes according to the instructions of the intelligent decision-making module. The external system integration unit 30 is used to integrate with other systems within the financial institution and the systems of external partners to achieve data sharing and business collaboration. The task monitoring management unit 31 is used to monitor and manage the tasks performed by digital employees in real time to ensure the smooth completion of the tasks and handle abnormal situations in a timely manner.

[0047] In this embodiment, the human-computer interaction module 5 includes a voice interaction unit 32, a text interaction unit 33, a graphical interface interaction unit 34 and a channel integration unit 35. The voice interaction unit 32 is used to realize voice interaction between customers and digital employees through voice recognition and speech synthesis technology. The text interaction unit 33 is used for text interaction between customers and digital employees to answer customers' questions and provide services. The graphical interface interaction unit 34 is used to provide a graphical user interface so that customers can operate and manage. The channel integration unit 35 is used for multi-channel integration so that customers can interact with digital employees anytime and anywhere.

[0048] In this embodiment, the multi-channel integration in the channel integration unit 35 includes but is not limited to mobile phone APP, web pages, WeChat public accounts and computer clients.

[0049] In this embodiment, the security and risk management module 7 includes an identity authentication and authorization unit 36, a data security encryption unit 37, a risk monitoring and early warning unit 38, and a background standard management unit 39. The identity authentication and authorization unit 36 ​​is used to ensure the authenticity and reliability of the customer's identity through a variety of identity authentication methods, and to authorize the operations of digital employees to prevent illegal access and operations. The data security encryption unit 37 is used to ensure the safe storage and transmission of financial data and prevent data leakage and tampering. The risk monitoring and early warning unit 38 is used to monitor the risk factors in financial business in real time and issue early warnings in time. The background standard management unit 39 is used to ensure that the operations of digital employees comply with financial regulatory laws and internal rules and regulations.

[0050] First, the intelligent decision-making module 3 integrates advanced technologies such as rule engines, machine learning algorithms, and optimization algorithms, and can quickly and accurately handle various complex business requests. The rule engine can make decisions quickly according to preset rules and is applicable to some common business scenarios; the machine learning algorithm can predict customer behavior and assess risks by learning a large amount of historical data, providing a more scientific basis for decision-making; the optimization algorithm can achieve the best decision in asset allocation, investment portfolio, etc., and improve the efficiency of financial institutions. It integrates advanced technologies such as rule engines, machine learning algorithms, and optimization algorithms, and can quickly and accurately handle various complex business requests. The rule engine can make decisions quickly according to preset rules and is applicable to some common business scenarios; the machine learning algorithm can predict customer behavior and assess risks by learning a large amount of historical data, providing a more scientific basis for decision-making; the optimization algorithm can achieve the best decision in asset allocation, investment portfolio, etc., and improve the efficiency of financial institutions;

[0051] Secondly, the intelligent decision-making module 3 has a high level of automation and intelligence. It can automatically collect, process and analyze data without human intervention, which greatly improves decision-making efficiency. At the same time, it can continuously learn and optimize its own decision-making model, adapt to the ever-changing market environment and customer needs, and improve the accuracy of decision-making.

[0052] The above embodiments are only used to illustrate the present invention, but not to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that various combinations, modifications or equivalent substitutions of the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and should be included in the scope of the claims of the present invention.

Claims

1. A digital employee intelligence system based on a financial system, characterized by: include: Data collection and processing module (1), which is used when a customer initiates a transaction request, asks a question, or has other business needs. The data collection and processing module takes action first; Natural speech processing module (2), used for voice interaction. The speech recognition function converts the customer's speech into text, and then the text understanding function analyzes the text content to extract the customer's intention and key information; Intelligent decision-making module (3): When facing simple business requests from customers, the rule engine will make judgments and processes according to preset business rules. When facing complex business requests from customers, the machine learning algorithm can perform risk assessment and customer classification based on historical data, and then optimize the financial business process through optimization algorithms; The task execution module (4) is used for automated processes and will automatically execute the corresponding business processes. During the execution process, it will be integrated with external systems. At the same time, the task monitoring and management function will monitor the task execution status in real time to ensure the smooth completion of the task; A human-computer interaction module (5), used for human-computer interaction and reflecting the business processing results; The safety and risk management module (7) is used to monitor the entire operation process and perform risk management.

2. The digital employee intelligence system based on the financial system according to claim 1 is characterized by: The data collection and processing module (1) comprises a data collection unit (8), a data cleaning unit (9), a data conversion unit (10) and a data storage unit (11); the data collection unit (8) is used to collect relevant data from various data sources within the financial system and external data channels; the data cleaning unit (9) is used to clean the collected data, remove duplicate, erroneous and incomplete data, and ensure the accuracy and consistency of the data; the data conversion unit (10) is used to convert data in different formats into a unified format for subsequent analysis and processing; and the data storage unit (11) is used to store the processed data in a database or a data warehouse for easy access and analysis at any time.

3. The digital employee intelligence system based on the financial system according to claim 1 is characterized by: The natural speech processing module (2) includes a speech recognition unit (12), a text understanding unit (13), a language generation unit (14) and a sentiment analysis unit (15), wherein the speech recognition unit (12) is used to convert the voice instructions or consultations of the customer into text form so that the digital employee can understand and process them, the text understanding unit (13) is used to analyze and understand the text input of the customer and extract key information and intentions, the language generation unit (14) is used to generate natural and fluent text replies based on the customer's questions and needs, and provide the customer with accurate answers and suggestions, and the sentiment analysis unit (15) is used to analyze the emotional tendencies in the customer's language expressions so that the digital employee can better understand the customer's emotional state and provide more personalized services.

4. The digital employee intelligence system based on the financial system according to claim 1 is characterized by: The intelligent decision-making module (3) includes a business request unit (16), the business request unit (16) is connected to a rule judgment unit (17) through data transmission technology, and is used to find a corresponding rule set according to the business type and specific content of the request when a business request comes in. The rule judgment unit (17) is connected to a rule engine processing unit (18) and a learning judgment unit (19) through data transmission technology. The rule engine processing unit (18) is used to automatically handle the business under the premise that the rule conditions are met. The learning judgment unit (19) is used to perform learning optimization judgment when the rule conditions are not met. The learning judgment unit (19) is connected to a problem definition unit (20) and a data preparation unit (25) through data transmission technology. The problem definition unit (20) The algorithm selection unit (21) is connected to the algorithm selection unit (21) through data transmission technology, and is used to clarify the financial business problem to be optimized, determine the optimization goal and constraint conditions, and select a suitable optimization algorithm according to the characteristics and scale of the problem. The algorithm selection unit (21) is connected to the model establishment unit (22) through data transmission technology, and is used to convert the optimization problem into a mathematical model, and determine the specific expressions of the objective function and the constraint conditions. The model establishment unit (22) is connected to the algorithm solving unit (23) through data transmission technology, and is used to use the selected optimization algorithm to solve the established mathematical model. The algorithm solving unit (23) is connected to the result application unit (24) through data transmission technology, and is used to output the optimization result when the algorithm finds the optimal solution or meets the stop condition, and make corresponding decisions based on the optimization result.

5. The digital employee intelligence system based on the financial system according to claim 4 is characterized by: The data preparation unit (25) is connected to the model training unit (26) through data transmission technology, and is used to train various machine learning models using historical data under the premise of being applicable to machine learning. The model training unit (26) is connected to the model application unit (27) through data transmission technology, and is used to predict corresponding results based on input features. The model application unit (27) is connected to the decision result output unit (28) through data transmission technology, and is used to make corresponding decisions based on the prediction results of the model.

6. The digital employee intelligence system based on the financial system according to claim 1 is characterized by: The task execution module (4) includes an automatic process execution unit (29), an external system integration unit (30) and a task monitoring management unit (31). The automatic process execution unit (29) is used to automatically execute various financial business processes according to the instructions of the intelligent decision-making module. The external system integration unit (30) is used to integrate with other systems within the financial institution and the systems of external partners to achieve data sharing and business collaboration. The task monitoring management unit (31) is used to monitor and manage the tasks performed by digital employees in real time to ensure the smooth completion of the tasks and handle abnormal situations in a timely manner.

7. The digital employee intelligence system based on the financial system according to claim 1 is characterized by: The human-computer interaction module (5) includes a voice interaction unit (32), a text interaction unit (33), a graphical interface interaction unit (34) and a channel integration unit (35). The voice interaction unit (32) is used to realize voice interaction between customers and digital employees through voice recognition and speech synthesis technology. The text interaction unit (33) is used for text interaction between customers and digital employees to answer customers' questions and provide services. The graphical interface interaction unit (34) is used to provide a graphical user interface so that customers can operate and manage. The channel integration unit (35) is used for multi-channel integration so that customers can interact with digital employees anytime and anywhere.

8. The digital employee intelligence system based on the financial system according to claim 7 is characterized by: The multi-channel integration in the channel integration unit (35) includes but is not limited to mobile phone APP, web pages, WeChat public accounts and computer clients.

9. The digital employee intelligence system based on the financial system according to claim 1 is characterized by: The security and risk management module (7) comprises an identity authentication and authorization unit (36), a data security encryption unit (37), a risk monitoring and early warning unit (38) and a background standard management unit (39). The identity authentication and authorization unit (36) is used to ensure the authenticity and reliability of the customer's identity through a variety of identity authentication methods, and to authorize the operation of the digital employee to prevent illegal access and operation. The data security encryption unit (37) is used to ensure the safe storage and transmission of financial data and prevent data leakage and tampering. The risk monitoring and early warning unit (38) is used to monitor the risk factors in the financial business in real time and issue early warnings in time. The background standard management unit (39) is used to ensure that the operation of the digital employee complies with financial regulatory laws and internal rules and regulations.

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