Risk list supervision system and method
The risk list monitoring system uses transaction risk models and machine learning to automatically analyze high-risk transaction accounts, solving the problems of high labor costs and low accuracy in judgment in existing technologies, and achieving efficient and accurate transaction monitoring and account withdrawal bans.
Patent Information
- Application Number
- CN202411732803.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2024-10-11
- Filing Date
- 2024-11-29
- Publication Date
- 2026-04-14
AI Technical Summary
In existing technologies, financial institutions face challenges in identifying high-risk, abnormal trading accounts due to high labor costs, insufficient timeliness, and overly broad warning indicators, resulting in low accuracy.
The risk list monitoring system utilizes a database, risk analysis unit, risk list generation unit, and server to analyze transaction data through a transaction risk model, calculate risk scores, and generate a list of risky accounts when thresholds are reached. The system automatically blocks withdrawals from these accounts and updates the model through machine learning to improve accuracy.
It enables efficient and accurate identification of high-risk trading accounts, reduces labor costs, improves the accuracy and efficiency of judgment, and ensures the reliability and correctness of data transmission through distributed storage.
Smart Images
Figure CN121860738A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a monitoring system and method, and more particularly, to a risk list monitoring system and method for identifying high-risk abnormal trading accounts. Background Technology
[0002] With the development of internet and financial technology, people can choose other methods of transaction besides cash, such as electronic payment, online banking remittance, and online transfer. Since transfers and payments through automated devices (such as mobile banking, online banking, ATMs, and web ATMs) do not require face-to-face transactions, online fraud has also emerged. In recent years, with the updating of fraud methods and the increasing infiltration of financial institutions by fraud groups to engage in illegal activities, financial institutions typically take relevant regulatory measures against accounts suspected of being illegal or exhibiting abnormal transaction patterns.
[0003] Generally, financial institutions establish abnormal transaction warning indicators or rules. When an account's transaction data triggers these indicators or rules, a warning is generated, and subsequent actions are taken. However, with current technology, financial institutions manually review and audit transaction data one by one to decide whether to regulate an account. This not only consumes significant time and manpower resources for review but also faces timeliness issues with insufficient monitoring of high-risk abnormal accounts. Furthermore, existing warning indicators or rules are too generalized, too broad, and lack sufficient risk identification, resulting in a large number of duplicate and invalid alerts, thereby reducing the accuracy of identifying abnormal accounts.
[0004] Therefore, a solution is needed to immediately monitor high-risk, abnormal trading accounts after identification, in order to address existing technical issues. Summary of the Invention
[0005] In view of this, the purpose of this invention is to provide a risk list monitoring system and method that can promptly and effectively identify high-risk abnormal trading accounts and monitor them immediately, thereby solving the problems of prior art. This invention can improve accuracy and efficiency, reduce labor costs, and enhance the accuracy of identifying abnormal accounts.
[0006] To achieve the above objectives, the present invention discloses a risk list monitoring system, characterized by comprising:
[0007] A database used to store account information for an account and corresponding transaction flow information for that account;
[0008] A risk analysis unit is connected to the database and includes a transaction risk model, which is used to analyze the transaction cash flow data to generate at least one risk factor corresponding to the transaction cash flow data, and calculate the weight of the at least one risk factor to generate a risk score.
[0009] A risk list generation unit, connected to the risk analysis unit and pre-stored with a risk standard threshold, generates and sends a risk account list when the risk score of the transaction flow data exceeds the risk standard threshold. This risk account list includes the account information.
[0010] A server is connected to the risk list generation unit. The server receives the risk account list generated by the risk list generation unit and marks the account corresponding to the account information in the risk account list as temporarily blocked.
[0011] The system further includes multiple storage devices connected to the risk list generation unit and the server. The risk account list generated by the risk list generation unit is stored in the storage devices in a distributed manner, and the server receives the risk account list from the storage devices.
[0012] The server includes a verification unit that generates and sends a confirmation signal based on the risk account list received by the server. When the risk account list generation unit does not receive the confirmation signal, it generates an alert signal and resends the risk account list.
[0013] The risk analysis unit updates the transaction risk model using machine learning based on the list of risky accounts, the transaction flow data, and all the accounts that have been marked.
[0014] The risk analysis unit updates the trading risk model using a gradient enhancement algorithm.
[0015] The system further includes a police database connected to the risk analysis unit. The police database contains information on multiple alerted accounts. The risk analysis unit updates the transaction risk model using machine learning based on the list of risky accounts, the transaction flow data, all the registered accounts, and the information on these alerted accounts.
[0016] A risk list supervision method was also disclosed, characterized by the following steps:
[0017] Stores account information for an account and corresponding transaction flow information for that account;
[0018] A risk analysis unit analyzes the transaction cash flow data using a transaction risk model to generate at least one risk factor corresponding to the transaction cash flow data, and calculates the weight of the at least one risk factor to generate a risk score.
[0019] A risk list generation unit generates a risk account list when the risk score of the transaction flow data exceeds a risk standard threshold, wherein the risk account list contains the account information; and
[0020] A server receives the risk account list generated by the risk list generation unit and marks the account corresponding to the account information in the risk account list as temporarily banned.
[0021] This further includes the following steps:
[0022] The list of risky accounts is stored on multiple storage devices using a distributed storage method;
[0023] Furthermore, the step of the server receiving the list of risky accounts generated by the risk list generation unit further includes the following steps:
[0024] The server receives the list of risky accounts from these storage devices.
[0025] The step of the server receiving the list of risky accounts generated by the risk list generation unit further includes the following steps:
[0026] A verification unit of the server generates and sends a confirmation signal based on the list of risky accounts received by the server; and
[0027] If the risk list generation unit does not receive the confirmation signal, it generates an alert signal and resends the risk account list.
[0028] This further includes the following steps:
[0029] The risk analysis unit updates the transaction risk model using machine learning based on the list of risky accounts, the transaction flow data, all the previously noted accounts, and multiple alert accounts in a police database.
[0030] In summary, the risk list monitoring system and method of the present invention can automatically analyze and judge high-risk transactions and automatically identify high-risk accounts through an intelligent transaction risk model, without relying on manual interpretation, thereby improving accuracy and efficiency and reducing labor costs. Furthermore, the risk list monitoring system of the present invention can store and transmit the risk account list through a distributed file storage mechanism to ensure data transmission and interoperability. Moreover, the risk list monitoring system of the present invention includes a verification function for the risk account list to ensure data accuracy. In addition, the risk list monitoring system of the present invention can update the transaction risk model through machine learning and can further analyze account data collected by government agencies, thereby improving the accuracy of identifying abnormal accounts. Attached Figure Description
[0031] Figure 1 A functional block diagram of a risk list monitoring system according to a specific embodiment of the present invention is shown.
[0032] Figure 2 A functional block diagram of a risk list monitoring system according to a specific embodiment of the present invention is shown.
[0033] Figure 3 A flowchart illustrating the steps of a risk list monitoring method according to a specific embodiment of the present invention is shown.
[0034] Figure 4 A flowchart illustrating the steps of a risk list monitoring method according to a specific embodiment of the present invention is shown.
[0035] Figure 5 A flowchart illustrating the steps of a risk list monitoring method according to a specific embodiment of the present invention is shown.
[0036] Figure 6 A flowchart illustrating the steps of a risk list monitoring method according to a specific embodiment of the present invention is shown. Detailed Implementation
[0037] To make the advantages, spirit, and features of the present invention more easily and clearly understood, detailed descriptions and discussions will follow with reference to specific embodiments and the accompanying drawings. It is important to note that these specific embodiments are merely representative examples of the present invention, and the specific methods, apparatuses, conditions, materials, etc., exemplified are not intended to limit the present invention or the corresponding specific embodiments. Furthermore, the devices in the figures are only used to illustrate their relative positions and are not drawn to scale; this is to be stated beforehand.
[0038] In various embodiments of this disclosure, the expression "or" includes any or all combinations of the words listed simultaneously. For example, the expression "A or B" may include A, may include B, or may include both A and B. Furthermore, the indefinite articles "a," "an," and "an" preceding the means or elements of the invention are not restrictive in terms of the number of means or elements (i.e., the number of times they appear). Therefore, "a" should be interpreted as including one or at least one, and singular means or elements also include multiple forms unless the quantity clearly refers to a singular form.
[0039] In the description of this specification, references to terms such as "one specific embodiment," "another specific embodiment," or "partial specific embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment is included in at least one embodiment of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments.
[0040] Please see Figure 1 . Figure 1 A functional block diagram of a risk list monitoring system 1 according to a specific embodiment of the present invention is shown. Figure 1 As shown, in this specific embodiment, the risk list monitoring system 1 includes a database 10, a risk analysis unit 11, a risk list generation unit 12, and a server 13. The risk analysis unit 11 is connected to the database 10, the risk list generation unit 12 is connected to the risk analysis unit 11, and the server 13 is connected to the risk list generation unit 12. In practice, the risk analysis unit 11 and the risk list generation unit 12 can be analysis chips or processing chips. The risk analysis unit 11 and the risk list generation unit 12 can each be a single chip, or they can be integrated into the same chip. Furthermore, the risk analysis unit 11 and the risk list generation unit 12 can also be integrated into a computing device (such as a computer or server), and the computing device and the server 13 can communicate with each other via wired or wireless connection.
[0041] In this specific embodiment, database 10 is used to store account information and transaction flow data for multiple accounts. In practice, database 10 can store an account list, which includes multiple accounts and their account information (e.g., customer name, account number, account status, etc.). Transaction flow data can be data generated from account transactions (e.g., deposits, withdrawals, transfers, deductions, etc.), and each transaction flow data can correspond to one account. In practical applications, database 10 can be connected to the computers of financial institution staff, or to ATMs or other transaction devices. When a customer makes a transaction, the computer or transaction device stores each transaction flow data in database 10.
[0042] In this specific embodiment, the risk analysis unit 11 includes a transaction risk model 111, which analyzes transaction flow data stored in the database 10 to generate at least one risk factor corresponding to the transaction flow data. In practice, the risk factor can be a risk indicator item of abnormal flow, such as: transaction type, account status, transaction amount, transaction frequency, transaction account, etc. The transaction risk model 111 can analyze multiple historical abnormal account data and historical abnormal transaction data through machine learning methods (such as: supervised learning, unsupervised learning, etc.) to summarize and generate risk factors. In practical applications, the risk analysis unit 11 will analyze all transaction flow data stored in the database 10 one by one and find the risk factor for each transaction flow data.
[0043] Furthermore, the trading risk model 111 calculates the weights of the risk factors in the trading flow data to generate a corresponding risk score for the trading flow data. In practice, each risk factor can correspond to a risk weight score. The risk weight score can be determined based on the importance or risk level of the risk factor, or it can be determined by the trading risk model 111 through machine learning analysis of multiple historical abnormal account data and historical abnormal transaction data. In practical applications, after the risk analysis unit 11 identifies all the risk factors in the trading flow data, it calculates the weights of all risk factors to generate a risk score for the trading flow data between 0 and 1. It is worth noting that the risk score for the trading flow data is not limited to the aforementioned format; the risk score can also range from 0 to 100, and the risk score can also be presented as a risk level.
[0044] In this specific embodiment, the risk list generation unit 12 pre-stores a risk standard threshold, and generates a risk account list based on the risk score of the transaction flow data and the risk standard threshold. In practice, the risk standard threshold can be a threshold value used to judge abnormal transactions, and can be determined based on historical data or professional experience. In practical applications, when the risk score of the transaction flow data generated by the transaction risk model 111 is greater than the risk standard threshold, it indicates that the transaction is a high-risk abnormal transaction. At this time, the risk list generation unit 12 generates a risk account list, and the risk account list contains account information corresponding to the transaction flow data of that transaction.
[0045] Furthermore, the risk list generation unit 12 will send the list of risky accounts to the server 13. In practice, the risk list generation unit 12 may send the list of risky accounts to the server 13 on each business day or at other specific times to publish information on high-risk abnormal accounts within the financial industry. In addition, before sending the list of risky accounts, the risk list generation unit 12 will first confirm the communication status between the computing device and the server 13. Only after confirming that the connection between the computing device and the server 13 is normal will the risk list generation unit 12 send the list of risky accounts to the server 13.
[0046] In this specific embodiment, server 13 receives the risk account list generated by risk list generation unit 12 and marks the accounts corresponding to the account information in the risk account list as temporarily restricted from withdrawal. In practice, server 13 can be a computer or server of the central system of a financial institution, and server 13 can also connect to database 10 and obtain the aforementioned account list. Further, server 13 may include a marking unit 131, and the marking unit 131 may be a chip. The marking unit 131 can mark the accounts corresponding to the account information in the account list as temporarily restricted from withdrawal according to the risk account list. Temporary restriction of an account means that the account cannot perform any transaction activities, such as: being unable to use debit cards, transfers, electronic payment functions, or financial services such as account opening, loans, and remittances. In addition, in practical applications, after server 13 marks the accounts in the account list according to the risk account list, server 13 can further transmit the marked account list to the computer of the financial institution's staff for subsequent processing. In one specific embodiment, server 13 can also transmit the list of marked accounts to the account system of financial institutions, and the account system can temporarily disable the trading function of accounts marked as temporarily suspended in the account list. Therefore, the risk list monitoring system of the present invention can automatically analyze and judge high-risk transactions and automatically mark abnormal accounts through intelligent transaction risk models, without relying on manual interpretation, thereby improving accuracy and efficiency and reducing labor costs.
[0047] Please see Figure 2 . Figure 2 A functional block diagram of a risk list monitoring system 1 according to a specific embodiment of the present invention is shown. Figure 2As shown, in this specific embodiment, the risk list monitoring system 1 further includes multiple storage devices 15 connected to the risk list generation unit 12 and the server 13. After the risk list generation unit 12 generates a list of risky accounts, it stores the list in multiple storage devices 15 in a distributed manner. In practice, the risk list generation unit 12 can use distributed file system software to distribute the risk account list files across multiple storage devices 15. In practical applications, the risk list generation unit 12 can divide the risk account list files into multiple sub-files and store each sub-file in at least one storage device 15. The server 13 can read multiple sub-files from multiple storage devices 15 to receive and obtain the risk account list. Since each sub-file is stored in multiple storage devices 15, when one storage device 15 is damaged or disconnected, the server 13 can still obtain the risk account list from other storage devices 15. Therefore, the risk list monitoring system of the present invention can store and transmit the risk account list through a distributed file storage mechanism to ensure data transmission and interoperability.
[0048] In this specific embodiment, server 13 further includes a verification unit 132, which may be a chip. Verification unit 132 is used to generate and send a confirmation signal based on the risk account list received by server 13. In practice, when server 13 has indeed received the risk account list, verification unit 132 will generate a confirmation signal and immediately send it back to risk list generation unit 12. When server 13 has not received the risk account list, verification unit 132 will not generate a confirmation signal. Further, when risk list generation unit 12 receives a confirmation signal, it indicates that the risk account list has been successfully transmitted to server 13. When risk list generation unit 12 does not receive a confirmation signal, it indicates that the risk account list has not been transmitted to server 13. At this time, risk list generation unit 12 generates an alert signal, and financial institutions can check whether there is any abnormality in the connection between risk list generation unit 12 and server 13 based on the alert signal, where the alert signal may be text, sound, etc. The risk list generation unit 12 can resend the risk account list to the server 13 for subsequent monitoring operations.
[0049] Furthermore, in this specific embodiment, the verification unit can use the account information of all registered accounts and the account information of the risk account list. In practice, since account registration directly affects all customer transactions, the verification unit 132 can further compare the account information of all accounts in the account list registered by the registration unit 131 with the account information of the risk account list. In practical applications, the verification unit 132 can compare the number of accounts in the registered account list with the number of accounts in the risk account list, and can also compare the content of the account information in the registered account list and the risk account list. When the information in the registered account list and the information in the risk account list do not match, it indicates that the server 13 has not received a complete risk account list. At this time, the verification unit 132 will also generate a warning signal, and the server 13 will send the warning signal to the risk list generation unit 12. Further, when the risk list generation unit 12 receives the warning signal, the risk list generation unit 12 will resend the risk account list according to the warning signal. Therefore, the risk list monitoring system of the present invention includes a verification function for the risk account list to ensure the accuracy of the data.
[0050] In this specific embodiment, after the risk list generation unit 12 generates the risk account list, the risk analysis unit 11 updates the trading risk model 111 using machine learning based on the risk account list, transaction flow data, and all the accounts already annotated. In practice, the risk analysis unit 11 can use the transaction flow data stored in the database 10, the risk account list generated by the risk list generation unit 12, and all account data in the account list annotated by the annotation unit 131 as input data, and analyze this data using a gradient boosting algorithm to update the trading risk model 111. This allows the trading risk model 111 to summarize more risk factors and adjust / update the weight of each risk factor, thereby improving accuracy.
[0051] In this specific embodiment, the risk list monitoring system 1 further includes a police database 18 connected to the risk list generation unit 12. In practice, the police database 18 can be a database of a police unit and contains information on multiple alert accounts. The alert account information can be information on abnormal transaction accounts collected by the police unit. The risk analysis unit 11 can receive multiple alert account information from the police database 18 and can update the transaction risk model using machine learning based on the risk account list, transaction flow data, all registered accounts, and the alert account information. Therefore, the risk list monitoring system of the present invention can also be further analyzed with account information collected by government agencies to improve accuracy.
[0052] Please see Figure 3 . Figure 3 A flowchart illustrating the steps of a risk list monitoring method according to a specific embodiment of the present invention is shown. Figure 3 The steps and procedures can be accessed through Figure 1 The risk list monitoring system will be used to implement this. For example... Figure 1 and Figure 3 As shown, the risk list supervision method includes the following steps: Step S1: Store the account information and the corresponding transaction flow information of the account to the database 10; Step S2: The risk analysis unit 11 analyzes the transaction flow information using the transaction risk model 111 to generate at least one risk factor for the corresponding transaction flow information, and calculates the weight of the at least one risk factor to generate a risk score for the corresponding transaction flow information; Step S3: The risk list generation unit 12 generates a risk account list when the risk score of the transaction flow information is greater than the risk standard threshold, wherein the risk account list contains account information; and Step S4: The server 13 receives the risk account list generated by the risk list generation unit 12, and marks the accounts corresponding to the account information in the risk account list as temporarily banned from withdrawal.
[0053] Please see Figure 4 . Figure 4 A flowchart illustrating the steps of a risk list monitoring method according to a specific embodiment of the present invention is shown. Figure 4 The steps and procedures can be accessed through Figure 2 The risk list monitoring system will be used to implement this. For example... Figure 2 and Figure 4 As shown, after step S3 of the risk list monitoring method, the following step is further included: Step S5: The risk account list is stored in multiple storage devices 15 in a distributed storage manner. Furthermore, in Figure 1 In step S4, the step of the server receiving the risk account list generated by the risk list generation unit is further divided into the following steps: Step S41: The server 13 receives the risk account list from the storage devices 15.
[0054] Please see Figure 5 . Figure 5 A flowchart illustrating the steps of a risk list monitoring method according to a specific embodiment of the present invention is shown. Figure 5 The steps and procedures can be accessed through Figure 2 The risk list monitoring system will be used to implement this. For example... Figure 2 and Figure 5As shown, after step S3 of the risk list monitoring method, the following steps are further included: Step S41: The verification unit 131 of server 13 generates and sends a confirmation signal based on the risk account list received by server 13; and Step S42: The risk list generating unit 12 determines whether a confirmation signal has been received. If the determination result is negative, then step S43 is executed: The risk list generating unit 12 generates a warning signal and resends the risk account list, and then step S41 is executed again; if the determination result is positive, then step S44 is executed: Server 13 marks the accounts corresponding to the account information in the risk account list as temporarily banned. In addition, after step S44 of the risk list monitoring method, the following steps are further included: Step S6: The verification unit 131 of server 13 compares all marked accounts with the risk account list; and Step S7: The verification unit 131 generates a warning signal when all marked accounts and the risk account list do not match.
[0055] Please see Figure 6 . Figure 6 A flowchart illustrating the steps of a risk list monitoring method according to a specific embodiment of the present invention is shown. Figure 6 The steps and procedures can be accessed through Figure 2 The risk list monitoring system will be used to implement this. For example... Figure 2 and Figure 6 As shown, following step S4 of the risk list monitoring method, the following steps are further included: Step S8: The risk analysis unit 11 updates the transaction risk model 111 using machine learning based on the risk account list, transaction flow data, all registered accounts, and multiple alert account data in the police database 18. It is worth noting that... Figures 4 to 6 The steps can be combined in any way, and Figure 5 Steps S6, S7 and Figure 6 Step S8 can be executed simultaneously.
[0056] In summary, the risk list monitoring system of this invention can automatically analyze and judge high-risk transactions and automatically identify high-risk accounts through an intelligent transaction risk model, without relying on manual interpretation, thereby improving accuracy and efficiency and reducing labor costs. Furthermore, the risk list monitoring system of this invention can store and transmit the risk account list through a distributed file storage mechanism to ensure data transmission and interoperability. Moreover, the risk list monitoring system of this invention includes a verification function for the risk account list to ensure data accuracy. In addition, the risk list monitoring system of this invention can update the transaction risk model through machine learning and can further analyze account data collected by government agencies, thereby improving the accuracy of identifying abnormal accounts.
[0057] The detailed description of the preferred embodiments above is intended to more clearly illustrate the features and spirit of the present invention, and is not intended to limit the scope of the invention to the preferred embodiments disclosed above. Rather, the aim is to cover various modifications and equivalent arrangements within the scope of the patent claims made by this invention. Therefore, the scope of the patent claims made by this invention should be interpreted in the broadest possible sense based on the foregoing description, so as to cover all possible modifications and equivalent arrangements.
Claims
1. A risk list monitoring system, characterized in that... Includes: A database used to store account information for an account and corresponding transaction flow information for that account; A risk analysis unit is connected to the database and includes a transaction risk model, which is used to analyze the transaction cash flow data to generate at least one risk factor corresponding to the transaction cash flow data, and calculate the weight of the at least one risk factor to generate a risk score. A risk list generation unit is connected to the risk analysis unit and a risk standard threshold is pre-stored. When the risk score of the transaction flow data is greater than the risk standard threshold, the risk list generation unit generates and sends a risk account list, wherein the risk account list contains the account information. as well as A server is connected to the risk list generation unit. The server receives the risk account list generated by the risk list generation unit and marks the account corresponding to the account information in the risk account list as temporarily blocked.
2. The risk list monitoring system as described in claim 1, characterized in that, The system further includes multiple storage devices connected to the risk list generation unit and the server, wherein the risk account list generated by the risk list generation unit is stored in the storage devices in a distributed manner, and the server receives the risk account list from the storage devices.
3. The risk list monitoring system as described in claim 1, characterized in that, The server includes a verification unit that generates and sends a confirmation signal based on the risk account list received by the server. When the risk account list generation unit does not receive the confirmation signal, it generates an alert signal and resends the risk account list.
4. The risk list monitoring system as described in claim 1, characterized in that, The risk analysis unit updates the transaction risk model using machine learning based on the list of risky accounts, the transaction flow data, and all the accounts that have been marked.
5. The risk list monitoring system as described in claim 4, characterized in that, The risk analysis unit updates the trading risk model using a gradient boosting algorithm.
6. The risk list monitoring system as described in claim 1, characterized in that, The system further includes a police database connected to the risk analysis unit. The police database contains information on multiple alerted accounts. The risk analysis unit updates the transaction risk model using machine learning based on the list of risky accounts, the transaction flow data, all the accounts that have been marked, and the information on these alerted accounts.
7. A risk list supervision method, characterized in that... Includes the following steps: Stores account information for an account and corresponding transaction flow information for that account; A risk analysis unit analyzes the transaction cash flow data using a transaction risk model to generate at least one risk factor corresponding to the transaction cash flow data, and calculates the weight of the at least one risk factor to generate a risk score. A risk list generation unit generates a risk account list when the risk score of the transaction flow data is greater than a risk standard threshold, wherein the risk account list contains the account information. as well as A server receives the risk account list generated by the risk list generation unit and marks the account corresponding to the account information in the risk account list as temporarily banned.
8. The risk list supervision method as described in claim 7, characterized in that, Further steps include: The list of risky accounts is stored on multiple storage devices using a distributed storage method; Furthermore, the step of the server receiving the list of risky accounts generated by the risk list generation unit further includes the following steps: The server receives the list of risky accounts from these storage devices.
9. The risk list supervision method as described in claim 7, characterized in that, The step of the server receiving the list of risky accounts generated by the risk list generation unit further includes the following steps: A verification unit of the server generates and sends a confirmation signal based on the list of risky accounts received by the server; and If the risk list generation unit does not receive the confirmation signal, it generates an alert signal and resends the risk account list.
10. The risk list supervision method as described in claim 7, characterized in that, Further steps include: The risk analysis unit updates the transaction risk model using machine learning based on the list of risky accounts, the transaction flow data, all the previously noted accounts, and multiple alert accounts in a police database.