Account identification method and device, storage medium and electronic device

Abstract

The application discloses an account identification method and device, a storage medium and an electronic device. The method comprises the following steps: obtaining a crown mark identifier of a to-be-identified object of a target account request processing, wherein the crown mark identifier is used for indicating a printing batch of the to-be-identified object, and the crown mark identifier is a unique identity identifier of the to-be-identified object; determining a transaction path of the to-be-identified object and a crown mark coincidence degree according to the crown mark identifier, wherein the crown mark coincidence degree is used for indicating a probability that different accounts use the same to-be-identified object; determining an account that meets a preset relationship with the target account in the transaction path as an associated account; inputting the crown mark coincidence degree and transaction flow data into a target identification model to obtain a risk level of the associated account, wherein the target identification model is obtained by training sample transaction flow data corresponding to a sample account of a high risk level in advance. The application solves the technical problem of low identification accuracy of a risk account.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to an account identification method and apparatus, storage medium, and electronic device. Background Technology

[0002] In related technologies, the lack of effective digital tracking methods for goods transactions has become a major challenge for identifying risky accounts. Traditional risk assessment methods often rely on information such as transaction amount, frequency, and time. However, due to the limited performance of transaction data, it is difficult to accurately capture abnormal behavior patterns based solely on the above information when dealing with complex goods transactions between multiple accounts, resulting in a low accuracy rate in identifying risky accounts.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides an account identification method and apparatus, storage medium and electronic device to at least solve the technical problem of low accuracy in identifying risky accounts.

[0005] According to one aspect of the embodiments of this application, an account identification method is provided, comprising: obtaining the serial number identifier of an object to be identified in a target account request processing, wherein the serial number identifier is used to indicate the printing batch of the object to be identified, and the serial number identifier is a unique identifier of the object to be identified; determining the transaction path and serial number overlap of the object to be identified based on the serial number identifier, wherein the serial number overlap is used to indicate the probability that different accounts use the same object to be identified; identifying accounts in the transaction path that satisfy a preset relationship with the target account as associated accounts; inputting the serial number overlap and transaction flow data into a target identification model to obtain the risk level of the associated accounts, wherein the target identification model is pre-trained using sample transaction flow data corresponding to sample accounts with high risk levels.

[0006] According to another aspect of the embodiments of this application, an account identification device is also provided, comprising: an acquisition module, configured to acquire the serial number identifier of an object to be identified in a target account request processing, wherein the serial number identifier is used to indicate the printing batch of the object to be identified, and the serial number identifier is a unique identifier of the object to be identified; a first determination module, configured to determine the transaction path and serial number overlap of the object to be identified based on the serial number identifier, wherein the serial number overlap is used to indicate the probability that different accounts use the same object to be identified; a second determination module, configured to determine accounts in the transaction path that satisfy a preset relationship with the target account as associated accounts; and an identification module, configured to input the serial number overlap and transaction flow data into a target identification model to obtain the risk level of the associated account, wherein the target identification model is pre-trained using sample transaction flow data corresponding to sample accounts with high risk levels.

[0007] Optionally, the device is configured to determine the transaction path and serial number overlap of the object to be identified based on the serial number identifier in the following manner: determining the source account of the object to be identified based on the serial number identifier; obtaining first transaction data when the source account uses the object to be identified for transactions, and obtaining second transaction data indicated by the transaction flow data of the target account; and determining the ratio of the first transaction data and the second transaction data as the serial number overlap.

[0008] Optionally, the device is configured to determine the transaction path of the object to be identified based on the serial number identifier in the following manner: determining transaction records according to the serial number identifier; determining a group of accounts that use the object to be identified for transactions, and transaction flow data between the accounts, based on the transaction records, wherein the group of accounts includes the target account, and the transaction flow data is used to indicate the flow order and flow time of the object to be identified; and generating the transaction path based on the group of accounts and the transaction flow data.

[0009] Optionally, the device is used to identify accounts in the transaction path that satisfy a preset relationship with the target account as associated accounts in the following manner: when the object to be identified is transferred from the first account to the target account, the first account is identified as the associated account; when the object to be identified is transferred from the target account to the second account, the second account is identified as the associated account.

[0010] Optionally, the device is further configured to: in response to the target account obtaining the object to be identified from the ATM or counter for the first transaction, determine the target account as the starting account in the transaction path, wherein the object to be identified has not been obtained by any other account before being obtained by the target account; determine the M accounts following the starting account in the transaction path as the second account, wherein M is a positive integer and the value of M is related to the current transaction time of the object to be identified.

[0011] Optionally, the device is further configured to: in response to the target account obtaining the object to be identified from other accounts for non-first transactions and requesting to store the object to be identified, determine the target account as an intermediate account in the transaction path; determine the N accounts in the transaction path preceding the intermediate account as the first account, wherein N is a positive integer and the value of N is related to the current transaction time of the object to be identified.

[0012] Optionally, the device is used to input the serial number overlap and the transaction flow data of the associated account into the target recognition model in the following manner to determine the risk level of the associated account: determining the transaction time, transaction frequency and transaction amount of the object to be identified based on the transaction flow data of the associated account; inputting the serial number overlap, the transaction time, the transaction frequency and the transaction amount into the target recognition model to determine the risk level.

[0013] Optionally, the device is further configured to: acquire the serial number identifier; determine the source account of the object to be identified based on the serial number identifier; acquire first transaction data when the source account uses the object to be identified for transactions, and acquire second transaction data indicated by the transaction flow data of the target account; determine the ratio of the first transaction data and the second transaction data as the serial number overlap, wherein the serial number overlap is used to indicate the probability that different accounts use the same object to be identified; determine transaction records according to the serial number identifier, and determine a group of accounts using the object to be identified for transactions, and transaction flow data between accounts, based on the transaction records; generate the transaction path based on the group of accounts and the transaction flow data; in response to the target account acquiring the object to be identified from other accounts for non-first transactions and requesting to store the object to be identified, determine the target account as an intermediate account in the transaction path; determine the N accounts in the transaction path preceding the intermediate account as... The first account is defined as follows: N is a positive integer, and the value of N is related to the current transaction time of the object to be identified; when the object to be identified is transferred from the first account to the target account, the first account is identified as the associated account; in response to the target account obtaining the object to be identified for the first transaction from an ATM or counter, the target account is identified as the starting account in the transaction path; M accounts following the starting account in the transaction path are identified as second accounts, where M is a positive integer, and the value of M is related to the current transaction time of the object to be identified; when the object to be identified is transferred from the target account to the second account, the second account is identified as the associated account; the transaction time, transaction frequency, and transaction amount of the object to be identified are determined based on the transaction flow data of the associated account; the serial number overlap, transaction time, transaction frequency, and transaction amount are input into the target identification model to determine the risk level of the associated account.

[0014] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to execute the above-described account identification method when it is run.

[0015] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the account identification method described above.

[0016] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the account identification method described above through the computer program.

[0017] In this embodiment, the method involves obtaining the serial number identifier of the object to be identified in the target account request processing. The serial number identifier indicates the printing batch of the object and serves as its unique identifier. The transaction path and serial number overlap of the object are determined based on the serial number identifier. The serial number overlap indicates the probability that different accounts use the same object. Accounts in the transaction path that satisfy a preset relationship with the target account are identified as associated accounts. The serial number overlap and transaction flow data are input into the target identification model to obtain the risk level of the associated accounts. The target identification model is pre-trained using sample transaction flow data corresponding to high-risk account samples, thereby improving the accuracy of risk account identification and achieving more precise risk assessment and early warning. This solves the technical problem of low accuracy in risk account identification. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a schematic diagram of an application environment for an optional account recognition method according to an embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating an optional account identification method according to an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of an optional account identification method according to an embodiment of this application;

[0022] Figure 4 This is a schematic diagram of the structure of an optional account recognition device according to an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of the structure of an optional account recognition product according to an embodiment of this application;

[0024] Figure 6 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] The present application will be described below with reference to embodiments:

[0028] According to one aspect of the embodiments of this application, an account identification method is provided. Optionally, in this embodiment, the above-described account identification method can be applied to, for example... Figure 1 The hardware environment shown consists of server 101 and terminal device 103. For example... Figure 1As shown, server 101 is connected to terminal 103 via a network and can be used to provide services to terminal devices or applications 107 installed on terminal devices. Applications can be video applications, instant messaging applications, browser applications, educational applications, game applications, etc. Database 105 can be set up on the server or independently of the server to provide data storage services for server 101, such as a game data storage server. The network mentioned above can include, but is not limited to, wired networks and wireless networks. The wired network includes local area networks, metropolitan area networks, and wide area networks. The wireless network includes Bluetooth, WIFI, and other networks that enable wireless communication. Terminal device 103 can be a terminal configured with an application, and can include, but is not limited to, at least one of the following: mobile phones (such as Android phones, iOS phones, etc.), laptops, tablets, handheld computers, MID (Mobile Internet Devices), PADs, desktop computers, smart TVs, smart voice interaction devices, smart home appliances, vehicle terminals, aircraft, virtual reality (VR) terminals, augmented reality (AR) terminals, mixed reality (MR) terminals, and other computer devices. The server mentioned above can be a single server, a server cluster composed of multiple servers, or a cloud server.

[0029] Combination Figure 1 As shown, the above account identification method can be executed by an electronic device, which can be a terminal device or a server. The above account identification method can be implemented by the terminal device or the server respectively, or by the terminal device and the server together.

[0030] The above is merely an example, and this embodiment does not impose any specific limitations.

[0031] Alternatively, as an alternative implementation method, such as Figure 2 As shown, the above account identification methods include:

[0032] S202, Obtain the serial number identifier of the object to be identified in the target account request processing, wherein the serial number identifier is used to indicate the printing batch of the object to be identified, and the serial number identifier is the unique identifier of the object to be identified;

[0033] Optionally, in this embodiment, the process of obtaining the serial number identifier of the target account requesting processing refers to the system automatically reading and recording the serial number on each banknote from the cash transactions of the target account, in order to track the circulation history and source information of the banknote. The serial number identifier is a unique code assigned by the printing plant to each batch of banknotes. It not only contains detailed information about the printing batch, but also serves as a unique identifier for each banknote, ensuring that the circulation path and transaction records of each banknote can be accurately tracked in a large-scale cash circulation environment. This includes, but is not limited to, automatically collecting serial number data through cash handling equipment (such as ATMs, banknote counting machines, etc.), or having bank staff manually enter the serial number into the system when handling cash, to ensure the comprehensiveness and accuracy of the data.

[0034] It should be noted that the serial number identifier of the object to be identified in the target account request can be obtained in various ways. It can be obtained automatically by cash processing equipment, manually entered during manual operation, or indirectly through a serial number data interface provided by a third-party organization.

[0035] Furthermore, there are various possibilities for the storage and management of serial number identifiers, such as storing them in a local database, cloud storage services, or using blockchain technology to ensure their immutability and high security. This application does not limit this approach.

[0036] S204, determine the transaction path and serial number overlap of the object to be identified based on the serial number identifier, wherein the serial number overlap is used to indicate the probability that different accounts use the same object to be identified;

[0037] Optionally, in this embodiment of the application, the above-mentioned determination of the transaction path of the object to be identified based on the serial number identifier refers to the system tracing and constructing the entire process path of the object to be identified from printing and circulation to the current transaction through algorithm analysis based on the collected serial number data. This path records in detail all the accounts that the object to be identified passes through, as well as the time, location and amount of each transaction. The serial number overlap is used to quantify the potential connection between different accounts due to the use of the same object to be identified in this process. By statistically analyzing the frequency and proportion of serial numbers appearing repeatedly in multiple accounts, the probability of fund flow association between different accounts is indicated. This includes, but is not limited to, calculating the serial number repetition rate by comparing transaction records between different accounts, or using more complex statistical methods, such as Bayesian estimation, to determine the serial number overlap.

[0038] It should be noted that there are various methods to determine the transaction path. The system can use time series analysis, graph database technology, etc., to efficiently track and organize the circulation history of serial number identifiers.

[0039] There are also multiple possibilities for calculating the overlap of serial numbers. In addition to basic frequency comparison, it is also possible to combine multi-dimensional data such as transaction amount, transaction frequency and geographical location information, and use machine learning models to predict the strength of association between accounts.

[0040] Furthermore, the analysis of transaction paths and serial number overlap can be performed on a single currency note, or it can be extended to a group of currencies or a specific amount of currency, to accommodate data processing needs of different scales. This application does not limit this.

[0041] S206, Identify accounts in the transaction path that satisfy a preset relationship with the target account as associated accounts;

[0042] Optionally, in this embodiment, identifying accounts in the transaction path that satisfy a preset relationship with the target account as associated accounts means that the system, based on a determined transaction path, filters out other accounts that have a specific connection with the target account in time and space. This preset relationship can be a rule set based on parameters such as time interval, geographical location, transaction amount, or serial number overlap. This includes, but is not limited to, identifying accounts that frequently receive or send currencies with the same serial number as the target account within a short period, or accounts that have conducted multiple transactions with the target account in the same location, as candidates for associated accounts.

[0043] It should be noted that the conditions for fulfilling the preset relationship can be flexibly set to adapt to the needs of different scenarios and risk levels. For example, the preset relationship can be a direct predecessor or successor of an account in a transaction path, or it can be an indirect connection of accounts linked through multiple hops of transactions.

[0044] Furthermore, preset relationships can also be determined based on factors such as transaction amount, transaction frequency, and overlap of serial numbers. In practical applications, the system can dynamically adjust the parameters of these preset relationships based on real-time transaction data and historical risk cases to more accurately identify potentially related risky accounts; this application does not limit this.

[0045] S208. Input the serial number overlap and transaction flow data into the target recognition model to obtain the risk level of the associated account. The target recognition model is pre-trained using sample transaction flow data corresponding to sample accounts with high risk levels.

[0046] Optionally, in this embodiment, inputting serial number overlap and transaction flow data into the target recognition model refers to using a model built with machine learning or deep learning techniques. This model uses the analyzed serial number overlap data and specific transaction flow information (such as transaction time, amount, location, etc.) as input features to assess the risk level of related accounts. By learning the transaction behavior and serial number flow patterns of known high-risk sample accounts, the target recognition model can automatically identify the characteristic patterns of potentially risky accounts. This includes, but is not limited to, using serial number overlap as a quantitative indicator to assess the relationship between accounts, and combining this with specific details of transaction flow to score or classify related accounts for risk.

[0047] It should be noted that the training and application methods of the above-mentioned target recognition model can be diversified. The model may adopt supervised learning, unsupervised learning or semi-supervised learning strategies. At the same time, the model architecture can also be a rule-based system, decision tree, support vector machine, neural network, etc. The specific choice depends on the data characteristics and model performance requirements of the actual application scenario.

[0048] Furthermore, the data input into the model can undergo further feature engineering, such as smoothing time-series data, normalizing prefix overlap data, or converting geographic location information into distance or region coding to enhance the model's generalization ability and prediction accuracy. This application does not limit this.

[0049] In an exemplary embodiment, taking the application scenario of commercial bank cash transaction monitoring as an example, the implementation process of the account identification method in this application is specifically demonstrated:

[0050] S1. Acquisition of Serial Number Identification: When the target account makes cash deposits or withdrawals at bank branches or ATMs, cash handling equipment (such as banknote counters, sorting machines, or ATMs) automatically reads and records the serial number identification on each banknote. This data is uploaded to the bank's back-end system. The system further binds these serial number identifications with the target account's transaction history data, forming a serial number-transaction history association record, providing basic data for subsequent analysis.

[0051] S2. Determining the overlap between transaction paths and serial number prefixes: Based on the uploaded serial number identifiers and transaction records, the backend system constructs a graph containing all transactions, where nodes represent accounts and edges represent the flow of serial numbers. The system uses graph analysis algorithms (such as breadth-first search, depth-first search, or graph database queries) to track the transaction paths of the objects to be identified (i.e., banknotes with specific serial numbers), and simultaneously calculates the overlap of serial numbers between different accounts, thus assessing the potential correlation in fund flows between different accounts due to the use of the same banknotes.

[0052] S3, Identification of Associated Accounts: Based on the results of transaction path analysis, combined with preset relationship thresholds (such as time windows, geographical ranges, or transaction amount thresholds), the system determines which accounts have direct or indirect connections with the target account in the transaction path. Accounts that meet the preset relationship criteria are considered associated accounts of the target account, and these accounts are further included in the risk assessment scope.

[0053] S4, Risk Level Assessment: Finally, the system inputs the serial number overlap data and transaction flow data of the identified related accounts into a pre-trained target recognition model. This model is trained based on historical high-risk account samples and their transaction flow data, and can learn key features for identifying risky accounts from the flow patterns and transaction patterns of serial numbers. The model output is the risk level score of the related account, indicating the likelihood of the account participating in malicious operations or fraudulent activities, thus providing the bank with a quantitative basis for risk warning and management.

[0054] This application embodiment employs the acquisition of the serial number identifier of the object to be identified in the target account request processing. The serial number identifier indicates the printing batch of the object and serves as its unique identification. Based on the serial number identifier, the transaction path and serial number overlap of the object to be identified are determined. The serial number overlap indicates the probability that different accounts use the same object to be identified. Accounts in the transaction path that satisfy a preset relationship with the target account are identified as associated accounts. The serial number overlap and transaction flow data are input into the target identification model to obtain the risk level of the associated accounts. The target identification model is pre-trained using sample transaction flow data corresponding to high-risk sample accounts, thereby improving the accuracy of risk account identification and achieving more precise risk assessment and early warning. This solves the technical problem of low accuracy in risk account identification.

[0055] As an optional approach, determining the transaction path and serial number overlap of the target object based on the serial number identifier includes: determining the source account of the target object based on the serial number identifier; obtaining first transaction data when the source account uses the target object for transactions, and obtaining second transaction data indicated by the transaction flow data of the target account; and determining the ratio of the first transaction data and the second transaction data as the serial number overlap.

[0056] Optionally, in this embodiment, determining the transaction path of the object to be identified based on the serial number identifier refers to tracing the entire process of the object from printing and circulation to the current transaction through serial number data, thereby confirming the source account of the object. Obtaining the first transaction data of the source account and the second transaction data of the target account means that the system extracts transaction details associated with the object to be identified from the transaction records, including information such as transaction time, transaction amount, and transaction location. Determining the ratio of the first transaction data and the second transaction data as the serial number overlap is to quantify the possibility of association between the two accounts due to the use of the same currency by comparing the transaction data of the source account and the target account when using the same object, for subsequent risk assessment.

[0057] It should be noted that the methods for comparing and calculating the overlap of data in this process can be varied, including but not limited to directly calculating the overlap ratio of transaction amounts, the proximity of transaction times, or overlapping geographical areas, as well as combining other transaction characteristics such as transaction frequency and transaction type for comprehensive evaluation. This application does not limit this.

[0058] In an exemplary embodiment, taking the processing of a cash deposit transaction as an example, the system first reads and records the serial number identifier of the deposited cash. Then, through the serial number data tracing function, it determines the source account of this cash, that is, the account that has recently withdrawn cash using the same serial number identifier. Then, the system extracts the withdrawal transaction data (first transaction data) of the source account and the deposit transaction data (second transaction data) of the target account, calculates the ratio of the two sets of data in terms of amount and time, and obtains the serial number overlap.

[0059] Through the embodiments of this application, a method combining serial number data tracking and transaction data analysis is adopted to accurately identify potential account associations and risky behaviors in cash transactions, thereby enhancing the ability of financial institutions to combat malicious operations and fraud and effectively improving the level of financial security.

[0060] As an optional approach, determining the transaction path of the object to be identified based on the aforementioned serial number identifier includes: determining transaction records according to the aforementioned serial number identifier; determining a group of accounts that use the object to be identified for transactions, and transaction flow data between the accounts, based on the aforementioned transaction records, wherein the aforementioned group of accounts includes the aforementioned target account, and the aforementioned transaction flow data is used to indicate the flow order and flow time of the object to be identified; and generating the aforementioned transaction path based on the aforementioned group of accounts and the aforementioned transaction flow data.

[0061] Optionally, in this embodiment, the aforementioned serial number refers to a unique serial number on each banknote, used to distinguish and track different batches of currency. Transaction records refer to detailed information about each cash transaction stored in the banking system, including but not limited to transaction amount, transaction time, location, and the accounts involved. A group of accounts refers to multiple bank accounts identified in the transaction records as having used the same serial number for transactions. These accounts are linked together through the transaction flow data of the identified object, showing the circulation path of the currency. The transaction flow data records in detail the flow order and time of the identified object among a group of accounts, and is key information for constructing the transaction path.

[0062] It should be noted that the methods for determining transaction records and generating transaction paths can be diverse. For example, transaction records can be obtained automatically through serial number recognition devices or manually verified and entered. The generation of transaction paths may rely on complex data matching and sorting algorithms to ensure accurate tracking of the flow of the object to be identified. In addition, transaction flow data may also include information such as transaction amount and transaction type to provide a more comprehensive transaction background, which is not limited in this application.

[0063] In an exemplary embodiment, the system first filters relevant transaction records by serial number identifier. Suppose target account A deposits cash containing a specific serial number identifier on a certain day. The system automatically searches all historical transaction records containing that serial number identifier to identify a group of accounts—that is, a set of accounts that used the same serial number identifier for transactions. Subsequently, based on the timestamps and transaction sequences in the transaction records, the system reconstructs the circulation order and timeline of the objects to be identified among these accounts, generating a transaction path. For example, if account B withdraws some cash from account C within two hours before target account A's deposit, and the withdrawn cash contains the same serial number identifier, the system will identify account B as one of the groups of accounts related to target account A and reflect this circulation relationship in the transaction path.

[0064] Through the embodiments of this application, a method combining serial number identification and transaction flow data is adopted to achieve accurate tracking of the currency circulation path in cash transactions, improve the ability to identify potential risk associations between accounts, and achieve the purpose of enhancing risk prevention and control and effectively identifying fraudulent behavior, thus providing financial institutions with a powerful security tool.

[0065] As an optional approach, the above-mentioned determination of accounts in the transaction path that satisfy the preset relationship with the target account as associated accounts includes: when the object to be identified is transferred from the first account to the target account, the first account is determined as the associated account; when the object to be identified is transferred from the target account to the second account, the second account is determined as the associated account.

[0066] Optionally, in this embodiment, the transaction path refers to the currency circulation trajectory tracked by the serial number identifier, revealing the entire process of currency flowing from one account to another, including but not limited to the order and timing of currency flow. The target account is the bank account currently being monitored or analyzed, while the first account and the second account refer to the accounts located at the preceding and following transaction nodes in the transaction path, respectively, and are directly associated with the target account through currency flow identified by specific serial numbers.

[0067] It should be noted that the setting of preset relationships can be flexible and diverse. In addition to direct predecessor and successor accounts, it can also include accounts that have multiple transactions with the target account that have the same serial number within a certain time window, or accounts that are indirectly related through multi-hop transactions. Furthermore, the criteria for determining preset relationships may also include factors such as the size of the transaction amount and the degree of serial number overlap, which are not limited in this application.

[0068] In an exemplary embodiment, assume that the object to be identified, identified by serial number X, is initially withdrawn from account A. Subsequently, X flows through a series of transactions, eventually flowing into target account Y. During this process, account B is the last account that directly used X in transactions before X flowed to target account Y. Based on this, the system automatically identifies account B as one of the associated accounts of target account Y, i.e., the first account. Similarly, if target account Y uses X for transactions, and X is then deposited into account C, the system will also identify account C as an associated account of target account Y, i.e., the second account. This process ensures that the system can accurately identify the predecessor and successor accounts directly related to the target account, providing accurate data support for subsequent risk assessment.

[0069] Through the embodiments of this application, a method combining serial number tracking and account preset relationship determination is adopted to achieve accurate identification of direct associations between accounts in a cash transaction network, enhance the ability to quickly locate risky accounts, and achieve the goal of improving the efficiency and accuracy of financial institutions in risk prevention and control in the areas of malicious operation and anti-fraud, thereby promoting the safety and stability of the financial environment.

[0070] As an optional approach, the method further includes: in response to the target account obtaining the first transaction object from the ATM or counter, determining the target account as the starting account in the transaction path, wherein the object to be identified has not been obtained by any other account before being obtained by the target account; determining the M accounts following the starting account in the transaction path as the second account, wherein M is a positive integer and the value of M is related to the current transaction time of the object to be identified.

[0071] Optionally, in this embodiment, the target account refers to a bank account currently being monitored or analyzed by the system, which plays the role of the starting point in the transaction path. The object to be identified refers to cash with a specific serial number identifier, which can track the cash's circulation history. The first transaction refers to the first transaction between the target account and the specific object to be identified, which typically means that the target account is the first account to appear in the serial number identifier record, i.e., the cash has not been used by any other account before. The transaction path is the entire process of cash circulation tracked and recorded according to the serial number identifier; it includes all accounts related to the object to be identified and their transaction details. The starting account is the first account to transact with the object to be identified in the transaction path, while the M accounts refer to the M consecutive transaction accounts following the starting account in the transaction path, which are related to the time of the object to be identified's first transaction and subsequent circulation.

[0072] It should be noted that the value of M can be set according to different strategies, such as the interval between the first transaction time of the target object and the current time, or a preset fixed value. This depends on the specific needs and risk assessment strategies of the financial institution. Furthermore, the transaction path between the target account (the starting account) and the M second accounts may exhibit various complex forms, including linear, branching, or cyclical paths, which this application does not limit.

[0073] In an exemplary embodiment, suppose the object to be identified, with serial number 12345678, is withdrawn from an ATM for the first time by target account A. At this point, the system marks account A as the starting account in the transaction path. Subsequently, the system tracks the circulation of this serial number identifier and finds that it flows through accounts B, C, and D, with account D being the account that most recently transacted with the object to be identified before the current time. If M is set to 2, then the system will identify accounts B and C as associated accounts of target account A, i.e., the second accounts, because they are located at the two transaction nodes after the starting account A in the transaction path.

[0074] Through the embodiments of this application, a method is adopted to track the target account's first transaction and determine the subsequent M transaction accounts as associated accounts. This achieves the accurate positioning of potentially risky accounts in cash transactions, improves the real-time nature and effectiveness of risk assessment, and achieves the purpose of enhancing the risk prevention and control capabilities of financial institutions, timely detecting and preventing illegal activities, and effectively improving the security and stability of the financial system.

[0075] As an optional approach, the method further includes: in response to the target account obtaining the object to be identified from other accounts for non-first transactions and requesting the storage of the object to be identified, identifying the target account as an intermediate account in the transaction path; identifying the N accounts in the transaction path preceding the intermediate account as the first account, wherein N is a positive integer and the value of N is related to the current transaction time of the object to be identified.

[0076] Optionally, in this embodiment, the intermediate account refers to a bank account that is neither the starting account nor the final holding account in the circulation path of the serial number identifier, but rather lies between the two. A non-first-time transaction target refers to a cash item with a specific serial number identifier that has appeared multiple times in the current account's transaction records, indicating that the cash has been used by other accounts in previous transactions. The transaction path is the cash circulation trajectory traced based on the serial number identifier; it includes all accounts that have transacted with the target object and their transaction order. N accounts refer to the N consecutive transaction accounts preceding the intermediate account in the transaction path; these accounts are directly or indirectly related to the current transaction time of the target object, and the value of N depends on the specific risk assessment strategy.

[0077] It should be noted that the value of N and the method for determining the first account can be varied. For example, the size of N can be dynamically adjusted based on the difference between the current transaction time and the first transaction time of the object to be identified, or it can be determined according to a preset fixed value. At the same time, the determination of the first account can be based on the most recent transaction time, or it can be based on a comprehensive consideration of factors such as transaction frequency and transaction amount. This application does not limit this.

[0078] In an exemplary embodiment, cash with serial number Z is deposited into target account C in a non-first-time deposit transaction. The system uses serial number tracking technology to identify the transaction records preceding Z and marks account C as an intermediate account in the transaction path. Assuming N is set to 4, this means the system needs to identify the four transaction accounts preceding account C, i.e., the first accounts. By analyzing the transaction records, the system finds that Z flows through accounts D, E, F, and G in sequence, with account G being the deposit account immediately preceding account C, and account D being the withdrawal account for Z's first transaction. Therefore, accounts D, E, F, and G are identified as the first accounts related to this deposit transaction for subsequent correlation analysis and risk assessment.

[0079] Through the embodiments of this application, a method for determining intermediate accounts and first accounts based on the circulation path of serial number identification is adopted, which realizes the accurate positioning of risk accounts in the cash transaction network, enhances the analytical depth of financial institutions when monitoring cash flow, and achieves the purpose of improving risk prevention and control capabilities, effectively identifying potential malicious operations or fraudulent activities, and providing strong technical support for maintaining financial security.

[0080] As an optional approach, the above-mentioned serial number overlap and transaction data of the related accounts are input into the target identification model to determine the risk level of the related accounts, including: determining the transaction time, transaction frequency and transaction amount of the target to be identified based on the transaction data of the related accounts; and inputting the serial number overlap, transaction time, transaction frequency and transaction amount into the target identification model to determine the risk level.

[0081] Optionally, in this embodiment, the aforementioned serial number overlap refers to an indicator that quantifies the potential financial connection between accounts by analyzing the recurrence of serial number identifiers across different accounts, including but not limited to the number of overlaps, overlap frequency, and overlap probability. The aforementioned transaction log data covers detailed records of all account transactions, including but not limited to transaction time, transaction frequency, and transaction amount. This data is used to depict the account's transaction behavior characteristics. The aforementioned target recognition model is a model built using machine learning technology, used to assess and predict the risk level of an account based on the input feature data. The model's training data may come from historical high-risk account cases and normal account cases to learn the characteristic patterns that distinguish risky accounts from ordinary accounts.

[0082] It should be noted that the methods for analyzing transaction time, frequency, and amount can be diverse. For example, transaction time analysis can consider the specific moment a transaction occurs, or divide a day into multiple time periods for statistical analysis; transaction frequency can be measured by the number of transactions within a specific time period, which may involve daily, weekly, or monthly statistics; transaction amount analysis can consider the size of a single transaction, or the total transaction amount or average transaction amount over a certain period. Furthermore, the target recognition model can employ various machine learning algorithms, such as support vector machines, decision trees, and neural networks, and this application does not impose any limitations on these methods.

[0083] In an exemplary embodiment, assume that the target object identified by serial number X has overlapping transactions with target account A and account B. The system first determines characteristics of X, such as transaction time, frequency, and amount, based on the transaction logs of accounts A and B. For example, X appears in withdrawal transactions in account A on Monday morning, while account B made deposit transactions containing X on Tuesday afternoon and Wednesday morning. The system will count X's transaction frequency in account B as two times, with transaction amounts of 200 yuan and 150 yuan respectively. Subsequently, these characteristic data, along with the serial number overlap (assuming an overlap of 0.5), are input into the target identification model. After calculation and evaluation, the model outputs that account B has a medium risk level, indicating that the account may have some malicious operation or fraud risk and requires further monitoring and review.

[0084] Through the embodiments of this application, a risk assessment method based on serial number overlap and transaction flow data characteristics is adopted to achieve intelligent identification and classification of potential risky accounts. This improves the efficiency and accuracy of financial institutions in preventing and controlling malicious operations and fraud risks, achieves the purpose of timely warning and effective response to illegal activities, and provides strong technical support for maintaining financial order and security.

[0085] As an optional approach, the above method also includes:

[0086] Obtain the above-mentioned serial number identifier;

[0087] The source account of the aforementioned object to be identified is determined based on the aforementioned serial number identifier;

[0088] Obtain the first transaction data when the aforementioned source account conducts transactions using the aforementioned target object, and obtain the second transaction data indicated by the transaction flow data of the aforementioned target account;

[0089] The ratio of the first transaction data and the second transaction data is determined as the serial number overlap, wherein the serial number overlap is used to indicate the probability that different accounts use the same object to be identified.

[0090] The transaction records are determined according to the aforementioned serial number identifiers, and a group of accounts that used the aforementioned object to conduct transactions are identified based on the aforementioned transaction records, as well as the transaction flow data between the accounts;

[0091] The above transaction path is generated based on the above set of accounts and the above transaction flow data;

[0092] In response to the target account obtaining the object to be identified from other accounts for non-first transactions and requesting to store the object to be identified, the target account is identified as an intermediate account in the transaction path; the N accounts in the transaction path preceding the intermediate account are identified as the first account, where N is a positive integer and the value of N is related to the current transaction time of the object to be identified;

[0093] In the case where the object to be identified is transferred from the first account to the target account, the first account is identified as the associated account.

[0094] In response to the target account obtaining the first transaction from the ATM or counter, the target account is determined as the starting account in the transaction path; the M accounts following the starting account in the transaction path are determined as the second accounts, where M is a positive integer and the value of M is related to the current transaction time of the target account.

[0095] In the case where the object to be identified is transferred from the target account to the second account, the second account will be identified as the associated account.

[0096] Based on the transaction data of the aforementioned associated accounts, the transaction time, transaction frequency, and transaction amount of the aforementioned target individuals were determined.

[0097] By inputting the aforementioned serial number overlap, transaction time, transaction frequency, and transaction amount into the aforementioned target recognition model, the aforementioned risk level of the aforementioned related accounts is determined.

[0098] In one exemplary embodiment, the account identification method proposed in this application can be used to identify risky accounts using serial number data through a machine learning model.

[0099] First, the terms appearing in the embodiments of this application are explained as follows:

[0100] Serial Number: The serial number refers to the combination of letters and numbers printed on banknotes to identify the printing batch. Specifically, the "prefix" refers to the two or three letters printed on the banknote to identify the printing batch, which are arranged and printed by the banknote printing plant according to certain rules. The "number" refers to the seven or eight Arabic numerals that follow the prefix. Each genuine banknote has a unique serial number, giving each banknote its unique identity.

[0101] Machine learning models: A machine learning model is an algorithmic model that can automatically learn and improve from data to accomplish a specific task. This type of model does not require human programming to specify each step; instead, it analyzes the data through algorithms, extracts features, learns patterns, and then uses this learned knowledge to predict new, unknown data or make decisions.

[0102] Considering the difficulties in risk identification in current technologies: Due to the physical nature of cash transactions, unlike electronic transactions, they do not leave clear records, rendering traditional data analysis-based risk identification methods ineffective in handling cash transactions; underutilization of serial number data: Serial numbers are unique identifiers of banknotes and provide information about their circulation, but current financial security systems do not fully utilize this, especially in linking accounts and assessing potential risks; low accuracy in identifying account relationships: Traditional risk management models may not be able to accurately identify connections between accounts through transaction patterns, especially given the frequency and high concealment of cash transactions; insufficient ability to identify high-risk accounts: Due to the special nature of cash transactions, existing risk identification models struggle to capture behavioral characteristics related to malicious operations, fraud, and other financial crimes.

[0103] Therefore, the embodiments of this application aim to address the limitations of traditional financial risk identification methods in handling cash transactions, particularly their inadequacy in identifying financial crimes such as malicious manipulation and fraud. Traditional methods often rely on non-physical information such as transaction amount, frequency, and time for analysis. However, the embodiments of this application introduce serial number data into a machine learning model, identifying duplicate serial numbers between accounts by tracking and analyzing serial numbers in cash transactions. This not only improves the accuracy of identifying correlations between accounts but also helps to more effectively detect and assess high-risk accounts involved in malicious manipulation and fraud, thereby strengthening the security of the financial system. In this way, the present invention improves account risk identification capabilities, strengthens financial security systems, and reduces the occurrence of malicious manipulation and fraud.

[0104] For example, Figure 3 This is a schematic diagram of an optional account identification method according to an embodiment of this application, such as... Figure 3As shown, by analyzing and comparing cash transaction behavior between different bank accounts, and the overlap of serial numbers involved in these transactions, the risk level of bank accounts can be identified and assessed, including but not limited to:

[0105] S1, Serial Number Data Acquisition and Processing: Collecting and organizing serial number data generated during cash transactions. This process includes recording data for cash withdrawals and deposits, automatically reading and recording the serial number information of banknotes in cash transactions using machines such as banknote counters, sorting machines, and ATMs, ensuring that the serial number of banknotes in each transaction is accurately recorded. This data is then transmitted securely to a central database for centralized management and processing.

[0106] S2, Analysis of Overlapping Serial Numbers: First, data preprocessing is performed to clean the collected serial number data, removing non-standard formats and erroneous data to ensure the accuracy of subsequent analysis; second, based on the cleaned serial number data and the logical order of withdrawal before deposit, the overlapping serial numbers between different accounts are quickly matched; finally, data such as the time interval between transactions of different accounts, the number and frequency of overlapping serial numbers are obtained.

[0107] S3, Machine Learning Model Training: Extract key features from multiple dimensions such as serial number overlap, transaction time, transaction frequency, and transaction amount obtained in step 2), and use historical data and known high-risk accounts (such as accounts involved in malicious operations or accounts controlled by fraudsters) as black samples to train machine learning models (such as neural networks) to determine the risk level of related accounts.

[0108] S4, Related Risk Account Identification: Extract key features from multiple dimensions such as serial number data and transaction data, input them into the model, predict the risk level of related accounts, and generate a risk assessment report.

[0109] S5. Establish a risk account database: Regularly maintain and update high-risk accounts, including those clearly involved in cases or malicious operations from the existing accounts, while continuously adding newly discovered risk accounts.

[0110] In summary, this application's embodiments obtain serial number data from cash transaction records, including serial number information in cash withdrawal and deposit transactions, as the basis for subsequent analysis; analyze cash transactions between different accounts to identify overlapping serial numbers, thereby quantifying the correlation strength between accounts; design and train a machine learning model, and based on the serial number overlap and the analysis results of the machine learning model, determine whether there is a risk association between accounts for malicious operations or fraudulent activities, and the severity of such associations, thereby judging the potential risk level of the accounts; also establish a database of known high-risk accounts such as those involved in malicious operations and fraud, for model training and as a reference for assessing the risk of new accounts; through the adaptive capability of the machine learning model and the dynamically updated high-risk account database, a risk account assessment method that can be continuously updated and optimized is formed, systematically solving the problem of risk account identification in the financial field.

[0111] The embodiments of this application effectively enhance the ability to identify potentially high-risk accounts such as those involved in malicious operations and fraud, reduce missed and incorrect judgments, accelerate the identification process of risky accounts, effectively improve work efficiency, reduce human resources and risks that may be caused by human error, and improve the level of early warning and prevention of unknown risky accounts.

[0112] In other words, by utilizing serial number data at a deeper level, the accuracy and efficiency of identifying risky accounts are effectively improved. Compared to existing technologies, the embodiments of this application employ advanced machine learning models, which can not only capture complex account association patterns but also achieve rapid response in actual operation. Specifically, by automatically tracking and analyzing the overlap of serial numbers between different accounts, the correlation between accounts can be more accurately assessed, thereby identifying potentially malicious, fraudulent, and other high-risk accounts.

[0113] Furthermore, by combining existing risk account data, this invention can predict and warn of potential future associated risks through reasoning methods, significantly enhancing risk prediction capabilities.

[0114] In summary, this invention provides financial institutions with more powerful and flexible risk management tools through efficient data analysis and intelligent risk identification strategies. It helps to build a safer and more transparent financial environment, enabling more effective monitoring and control of financial risks and protecting the legitimate financial order from harm.

[0115] It is understood that in the specific embodiments of this application, data such as user information are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0116] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0117] According to another aspect of the embodiments of this application, an account recognition device for implementing the above-described account recognition method is also provided. For example... Figure 4 As shown, the device includes:

[0118] The acquisition module 402 is used to acquire the serial number identifier of the object to be identified in the target account request processing. The serial number identifier is used to indicate the printing batch of the object to be identified and is a unique identifier of the object to be identified.

[0119] The first determining module 404 is used to determine the transaction path and serial number overlap of the object to be identified based on the serial number identifier, wherein the serial number overlap is used to indicate the probability that different accounts use the same object to be identified;

[0120] The second determining module 406 is used to determine accounts in the transaction path that satisfy a preset relationship with the target account as associated accounts;

[0121] The identification module 408 is used to input the serial number overlap and transaction flow data into the target identification model to obtain the risk level of the associated account. The target identification model is pre-trained using sample transaction flow data corresponding to sample accounts with high risk levels.

[0122] As an optional solution, the above-mentioned device is used to determine the transaction path and serial number overlap of the object to be identified based on the serial number identifier in the following manner: determining the source account of the object to be identified based on the serial number identifier; obtaining first transaction data when the source account uses the object to be identified to conduct transactions, and obtaining second transaction data indicated by the transaction flow data of the target account; and determining the ratio of the first transaction data and the second transaction data as the serial number overlap.

[0123] As an optional solution, the above-mentioned device is used to determine the transaction path of the object to be identified based on the above-mentioned serial number identifier in the following manner: determining transaction records according to the above-mentioned serial number identifier; determining a group of accounts that use the object to be identified for transactions, and transaction flow data between the accounts, based on the above-mentioned transaction records, wherein the group of accounts includes the target account, and the transaction flow data is used to indicate the flow order and flow time of the object to be identified; and generating the above-mentioned transaction path based on the group of accounts and the transaction flow data.

[0124] As an optional solution, the above-mentioned device is used to identify accounts in the transaction path that satisfy a preset relationship with the target account as associated accounts in the following manner: when the object to be identified is transferred from the first account to the target account, the first account is identified as the associated account; when the object to be identified is transferred from the target account to the second account, the second account is identified as the associated account.

[0125] As an optional solution, the above-mentioned device is further configured to: in response to the target account obtaining the object to be identified from the ATM or counter for the first transaction, determine the target account as the starting account in the transaction path, wherein the object to be identified has not been obtained by any other account before being obtained by the target account; determine the M accounts in the transaction path that are after the starting account as the second account, wherein M is a positive integer and the value of M is related to the current transaction time of the object to be identified.

[0126] As an optional solution, the above-mentioned device is further configured to: in response to the target account obtaining the object to be identified from other accounts for non-first transactions and requesting to store the object to be identified, determine the target account as an intermediate account in the transaction path; determine the N accounts in the transaction path preceding the intermediate account as the first account, wherein the N is a positive integer and the value of the N is related to the current transaction time of the object to be identified.

[0127] As an optional solution, the above-mentioned device is used to input the above-mentioned serial number overlap and the transaction flow data of the above-mentioned associated accounts into the target recognition model in the following manner to determine the risk level of the above-mentioned associated accounts: based on the transaction flow data of the above-mentioned associated accounts, determine the transaction time, transaction frequency and transaction amount of the above-mentioned object to be identified; input the above-mentioned serial number overlap, the above-mentioned transaction time, the above-mentioned transaction frequency and the above-mentioned transaction amount into the above-mentioned target recognition model to determine the above-mentioned risk level.

[0128] As an optional solution, the above-mentioned device is further configured to: acquire the aforementioned serial number identifier; determine the source account of the object to be identified based on the aforementioned serial number identifier; acquire first transaction data when the source account uses the object to be identified for transactions, and acquire second transaction data indicated by the transaction flow data of the target account; determine the ratio of the aforementioned first transaction data and the aforementioned second transaction data as the serial number overlap, wherein the serial number overlap is used to indicate the probability that different accounts use the same object to be identified; determine transaction records according to the aforementioned serial number identifier, and determine a group of accounts that use the object to be identified for transactions, and transaction flow data between accounts, based on the aforementioned transaction records; generate the aforementioned transaction path based on the aforementioned group of accounts and the aforementioned transaction flow data; in response to the target account acquiring the object to be identified from other accounts for non-first transactions and requesting to store the object to be identified, determine the target account as an intermediate account in the aforementioned transaction path; and identify the N accounts preceding the intermediate account in the aforementioned transaction path. The first account is identified, where N is a positive integer and its value is related to the current transaction time of the object to be identified. When the object to be identified is transferred from the first account to the target account, the first account is identified as the associated account. In response to the target account obtaining the object to be identified for the first transaction from an ATM or counter, the target account is identified as the starting account in the transaction path. The M accounts following the starting account in the transaction path are identified as second accounts, where M is a positive integer and its value is related to the current transaction time of the object to be identified. When the object to be identified is transferred from the target account to the second account, the second account is identified as the associated account. Based on the transaction flow data of the associated account, the transaction time, transaction frequency, and transaction amount of the object to be identified are determined. The serial number overlap, transaction time, transaction frequency, and transaction amount are input into the target identification model to determine the risk level of the associated account.

[0129] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0130] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0131] According to one aspect of this application, a computer program product is provided, the computer program product comprising a computer program.

[0132] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0133] Figure 5 A schematic block diagram of a computer system architecture for implementing an electronic device according to embodiments of the present application is shown.

[0134] It should be noted that, Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0135] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM). The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output interface 505 (I / O interface) is also connected to the bus 504.

[0136] The following components are connected to the input / output interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a local area network card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0137] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit 501, it performs various functions defined in the system of this application.

[0138] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable media 511. When the computer program is executed by central processing unit 501, it performs various functions provided in the embodiments of this application.

[0139] According to another aspect of the embodiments of this application, an electronic device for implementing the above-described account identification method is also provided. This electronic device may be... Figure 1 The terminal device or server shown. This embodiment uses this electronic device as an example for illustration. Figure 6 As shown, the electronic device includes a memory 602 and a processor 604. The memory 602 stores a computer program, and the processor 604 is configured to execute the steps in any of the above method embodiments via the computer program.

[0140] Optionally, in this embodiment, the aforementioned electronic device may be located in at least one of a plurality of network devices in a computer network.

[0141] Optionally, in this embodiment, the processor may be configured to execute the methods in the embodiments of this application via a computer program.

[0142] Alternatively, as those skilled in the art will understand, Figure 6 The structure shown is for illustrative purposes only. Figure 6 This does not limit the structure of the aforementioned electronic devices. For example, the electronic device may also include components that are more... Figure 6 The more or fewer components shown (such as network interfaces, etc.), or having the same Figure 6 The different configurations shown.

[0143] The memory 602 can be used to store software programs and modules, such as the program instructions / modules corresponding to the account identification method and device in this embodiment. The processor 604 executes various functional applications and data processing by running the software programs and modules stored in the memory 602, thereby realizing the aforementioned account identification method. The memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 602 may further include memory remotely located relative to the processor 604, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Specifically, the memory 602 may be used, but is not limited to, to store information such as information about the object to be identified. As an example, such as... Figure 6 As shown, the memory 602 may include, but is not limited to, the acquisition module 402, the first determination module 404, the second determination module 406, and the identification module 408 in the account recognition device. Furthermore, it may include, but is not limited to, other module units in the account recognition device, which will not be elaborated upon in this example.

[0144] Optionally, the transmission device 606 described above is used to receive or send data via a network. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 606 includes a Network Interface Controller (NIC), which can be connected to other network devices and routers via a network cable to communicate with the Internet or a local area network. In another example, the transmission device 606 is a radio frequency (RF) module, used for wireless communication with the Internet.

[0145] In addition, the aforementioned electronic device also includes: a display 608 for displaying the aforementioned risk account; and a connection bus 610 for connecting the various module components in the aforementioned electronic device.

[0146] In other embodiments, the aforementioned terminal device or server can be a node in a distributed system, wherein the distributed system can be a blockchain system, which is a distributed system formed by connecting multiple nodes through network communication. The nodes can form a peer-to-peer network, and any form of computing device, such as a server, terminal, or other electronic device, can become a node in the blockchain system by joining this peer-to-peer network.

[0147] According to one aspect of this application, a computer-readable storage medium is provided, wherein a processor of an electronic device reads computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the electronic device to perform the account recognition method provided in the various alternative implementations of the above-described account recognition aspect.

[0148] Optionally, in this embodiment, the computer-readable storage medium described above may be configured to store methods for performing the embodiments of this application.

[0149] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0150] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0151] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more electronic devices to execute all or part of the steps of the methods described in the various embodiments of this application.

[0152] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed application can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0154] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0155] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0156] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An account identification method, characterized in that, include: Obtain the serial number identifier of the object to be identified in the target account request processing, wherein the serial number identifier is used to indicate the printing batch of the object to be identified, and the serial number identifier is a unique identifier of the object to be identified; The transaction path and serial number overlap of the target object are determined based on the serial number identifier. The serial number overlap indicates the probability that different accounts use the same target object. Specifically, this includes: determining the source account of the target object based on the serial number identifier; obtaining first transaction data from the source account when using the target object for transactions, and obtaining second transaction data indicated by the transaction flow data of the target account; and determining the ratio of the first transaction data to the second transaction data as the serial number overlap. The process of identifying accounts in the transaction path that satisfy a preset relationship with the target account as associated accounts specifically includes: when the object to be identified is transferred from a first account to the target account, the first account is identified as the associated account; when the object to be identified is transferred from the target account to a second account, the second account is identified as the associated account. The serial number overlap and transaction flow data are input into the target recognition model to obtain the risk level of the associated account. The target recognition model is pre-trained using sample transaction flow data corresponding to sample accounts with high risk levels. The step of determining the transaction path of the object to be identified based on the serial number identifier includes: determining transaction records according to the serial number identifier; determining a group of accounts that use the object to be identified for transactions, and transaction flow data between the accounts, wherein the group of accounts includes the target account, and the transaction flow data is used to indicate the flow order and flow time of the object to be identified; and generating the transaction path based on the group of accounts and the transaction flow data.

2. The method according to claim 1, characterized in that, The method further includes: In response to the target account obtaining the object to be identified from the ATM or counter for the first transaction, the target account is determined as the starting account in the transaction path, wherein the object to be identified has not been obtained by any other account before being obtained by the target account; The M accounts following the starting account in the transaction path are identified as the second account, where M is a positive integer and the value of M is related to the current transaction time of the object to be identified.

3. The method according to claim 1, characterized in that, The method further includes: In response to the target account obtaining the object to be identified from other accounts for non-first transactions and requesting to store the object to be identified, the target account is identified as an intermediate account in the transaction path; The N accounts preceding the intermediate account in the transaction path are identified as the first account, where N is a positive integer and the value of N is related to the current transaction time of the object to be identified.

4. The method according to claim 1, characterized in that, The step of inputting the serial number overlap and the transaction data of the associated account into the target recognition model to determine the risk level of the associated account includes: Based on the transaction data of the associated accounts, the transaction time, transaction frequency, and transaction amount of the object to be identified are determined; The risk level is determined by inputting the serial number overlap, transaction time, transaction frequency, and transaction amount into the target recognition model.

5. The method according to claim 1, characterized in that, The method further includes: Obtain the serial number identifier; The source account of the object to be identified is determined based on the serial number identifier; Obtain first transaction data when the source account conducts transactions using the object to be identified, and obtain second transaction data indicated by the transaction flow data of the target account; The ratio of the first transaction data to the second transaction data is determined as the serial number overlap, wherein the serial number overlap is used to indicate the probability that different accounts use the same object to be identified; The transaction records are determined according to the serial number identifier, and a group of accounts that used the object to be identified to conduct transactions are identified based on the transaction records, as well as the transaction flow data between the accounts; The transaction path is generated based on the set of accounts and the transaction flow data; In response to the target account obtaining the object to be identified from other accounts that is not the first transaction and requesting to store the object to be identified, the target account is identified as an intermediate account in the transaction path; the N accounts in the transaction path preceding the intermediate account are identified as the first account, where N is a positive integer and the value of N is related to the current transaction time of the object to be identified; When the object to be identified is transferred from the first account to the target account, the first account is identified as the associated account; In response to the target account obtaining the first transaction object from the ATM or counter, the target account is determined as the starting account in the transaction path; the M accounts following the starting account in the transaction path are determined as the second accounts, where M is a positive integer and the value of M is related to the current transaction time of the target object; In the case where the object to be identified is transferred from the target account to the second account, the second account is identified as the associated account; Based on the transaction data of the associated account, the transaction time, transaction frequency, and transaction amount of the object to be identified are determined; The overlap of the serial number, the transaction time, the transaction frequency, and the transaction amount are input into the target recognition model to determine the risk level of the associated account.

6. An account recognition device, characterized in that, include: The acquisition module is used to acquire the serial number identifier of the object to be identified in the target account request processing, wherein the serial number identifier is used to indicate the printing batch of the object to be identified, and the serial number identifier is the unique identifier of the object to be identified; The first determining module is used to determine the transaction path and serial number overlap of the object to be identified based on the serial number identifier, wherein the serial number overlap is used to indicate the probability that different accounts use the same object to be identified, specifically including: determining the source account of the object to be identified based on the serial number identifier; obtaining first transaction data when the source account uses the object to be identified for transactions, and obtaining second transaction data indicated by the transaction flow data of the target account; determining the ratio of the first transaction data and the second transaction data as the serial number overlap. The second determining module is used to determine accounts in the transaction path that satisfy a preset relationship with the target account as associated accounts. Specifically, it includes: when the object to be identified is transferred from the first account to the target account, determining the first account as the associated account; when the object to be identified is transferred from the target account to the second account, determining the second account as the associated account. The identification module is used to input the serial number overlap and transaction flow data into the target identification model to obtain the risk level of the associated account. The target identification model is pre-trained using sample transaction flow data corresponding to sample accounts with high risk levels. The device is used to determine the transaction path of the object to be identified based on the serial number identifier in the following manner: determining transaction records according to the serial number identifier; determining a group of accounts that use the object to be identified for transactions, and transaction flow data between the accounts, wherein the group of accounts includes the target account, and the transaction flow data is used to indicate the flow order and flow time of the object to be identified; and generating the transaction path based on the group of accounts and the transaction flow data.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein the computer program can be executed by an electronic device to perform the method described in any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1 to 5.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 5 through the computer program.

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