Transaction risk early warning method and device, electronic equipment and readable storage medium
By judging transaction risks based on probability models based on historical transaction data and portrait data, the subjectivity and aversion problems of trading risk judgment in the existing technology are solved, and the effect of fast and accurate risk warning and preventing high-risk transactions is achieved.
Patent Information
- Application Number
- CN202310754253.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-06-25
- Publication Date
- 2024-12-27
AI Technical Summary
In the prior art, the risk judgment of trading operation depends on manual experience or simple rules, and has subjectivity and time lag, which leads to the inability to quickly and accurately determine whether there is a risk in the transaction, and is easily avoided by illegal personnel, affecting the progress of normal transactions.
By obtaining historical transaction data and image data of the trading party, determine the probability model of the target event. If the probability is issued within the preset threshold range, take preset operations to prevent high-risk transactions.
It realizes rapid and accurate identification and warning of high-risk transaction operations, reduces invalid early warning information, improves the monitoring and blocking ability of illegal transactions, and ensures property safety.
Smart Images
Figure CN119205320A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of risk prediction, and in particular to a transaction risk early warning method, device, electronic device and readable storage medium. Background Art
[0002] In the Internet age, telecommunications fraud and illegal transactions have become increasingly covert. Therefore, when conducting transactions, it is necessary to monitor the transaction content in real time, and promptly detect and prevent high-risk and illegal transactions to protect people’s property safety.
[0003] At present, the main way to judge the risk of trading operations is to use manual experience judgment or simple rule restrictions, and give risk warning levels. However, manual experience judgment is usually subjective and time-delayed, which may cause wrong judgments due to subjectivity, resulting in too many invalid warning messages issued, making it difficult to identify the real illegal transactions; simple rule restrictions can be easily circumvented by illegal personnel and may affect the normal trading.
[0004] There is currently no effective solution to the problem that related technologies cannot quickly and accurately determine whether a transaction operation involves risks. Summary of the invention
[0005] In order to solve the problems in the related art, the embodiments of the present disclosure provide a transaction risk warning method, device, electronic device and readable storage medium.
[0006] In a first aspect, an embodiment of the present disclosure provides a transaction risk warning method, comprising:
[0007] Determine the target event after performing the current trading operation;
[0008] Determine the probability of the target event occurring based on the profile data of the transaction parties;
[0009] If the probability is within a preset threshold range, an early warning prompt is issued.
[0010] According to an embodiment of the present disclosure, the step of determining a target event after executing the current transaction operation includes:
[0011] Get historical transaction data;
[0012] Determine candidate events after the current transaction operation is executed according to the historical transaction data;
[0013] At least one of the candidate events is selected as a target event.
[0014] According to an embodiment of the present disclosure, selecting at least one of the candidate events as a target event includes:
[0015] Sorting the candidate events in descending order according to their risk levels;
[0016] At least one candidate event ranked high is used as the target event.
[0017] According to an embodiment of the present disclosure, the historical transaction data and the portrait data of the transaction parties have different sources.
[0018] According to an embodiment of the present disclosure, determining the probability of the target event occurring based on the portrait data of the transaction party includes:
[0019] Training a probabilistic model;
[0020] The portrait data of the transaction parties is input into the probability model to obtain the probability of the target event occurring.
[0021] According to an embodiment of the present disclosure, the method further includes:
[0022] If the probability is not within a preset threshold range, a preset operation is performed.
[0023] In a second aspect, an embodiment of the present disclosure provides a transaction risk warning device, including:
[0024] A first determination module is configured to determine a target event after executing the current transaction operation;
[0025] A second determination module is configured to determine the probability of the target event occurring according to the portrait data of the transaction party;
[0026] The early warning prompt module is configured to issue an early warning prompt if the probability is within a preset threshold range.
[0027] According to an embodiment of the present disclosure, the part of the first determination module that determines the target event after executing the current transaction operation is configured as follows:
[0028] Get historical transaction data;
[0029] Determine candidate events after the current transaction operation is executed according to the historical transaction data;
[0030] At least one of the candidate events is selected as a target event.
[0031] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer instructions, and wherein the one or more computer instructions are executed by the processor to implement a method as described in any one of the first aspects.
[0032] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the method described in any one of the first aspects is implemented.
[0033] According to the technical solution provided by the embodiment of the present disclosure, a transaction risk warning method is disclosed, including: determining a target event after executing a current transaction operation; determining the probability of the target event occurring based on the portrait data of the transaction parties; if the probability is within a preset threshold range, issuing a warning prompt.
[0034] The technical solution disclosed in the present invention can achieve the technical effect of quickly and accurately confirming whether there is a risk in the current transaction operation by determining the target event based on the current transaction data, determining the probability of the target event based on the portrait data of the transaction parties, and then issuing an early warning or taking measures for the current transaction operation based on the probability of the target event.
[0035] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings:
[0037] Figure 1 A flow chart of a transaction risk early warning method according to an embodiment of the present disclosure is shown.
[0038] Figure 2 A structural block diagram of a transaction risk warning device according to an embodiment of the present disclosure is shown.
[0039] Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.
[0040] Figure 4 A schematic diagram showing the structure of a computer system suitable for implementing the method according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0041] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.
[0042] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, behaviors, components, parts, or a combination thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or a combination thereof exist or are added.
[0043] It should also be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0044] In the present disclosure, if it involves operations of obtaining user information or user data or displaying user information or user data to others, the operations are all authorized and confirmed by the user, or actively selected by the user.
[0045] In the Internet age, telecommunications fraud and illegal transactions have become increasingly covert. Therefore, when conducting transactions, it is necessary to monitor the transaction content in real time, and promptly detect and prevent high-risk and illegal transactions to protect people’s property safety.
[0046] At present, the main way to judge the risk of trading operations is to use manual experience judgment or simple rule restrictions, and give risk warning levels. However, manual experience judgment is usually subjective and time-delayed, which may cause wrong judgments due to subjectivity, resulting in too many invalid warning messages issued, making it difficult to identify the real illegal transactions; simple rule restrictions can be easily circumvented by illegal personnel and may affect the normal trading.
[0047] There is currently no effective solution to the problem that related technologies cannot quickly and accurately determine whether a transaction operation involves risks.
[0048] The disclosed embodiment discloses a transaction risk warning method, including: determining a target event after executing a current transaction operation; determining the probability of the target event occurring based on the portrait data of the transaction party; and issuing a warning if the probability is within a preset threshold. The disclosed technical solution can achieve the technical effect of quickly and accurately confirming whether the current transaction operation has risks by determining the target event based on the current transaction data, determining the probability of the target event occurring based on the portrait data of the transaction party, and then issuing a warning or taking measures for the current transaction operation based on the probability of the target event occurring.
[0049] Figure 1 FIG. 1 is a flow chart showing a transaction risk early warning method according to an embodiment of the present disclosure. Figure 1 As shown, the transaction risk early warning method includes the following steps S101-S103:
[0050] In step S101, a target event after executing the current transaction operation is determined;
[0051] In step S102, the probability of the target event occurring is determined based on the profile data of the transaction party;
[0052] In step S103, if the probability is within a preset threshold range, a warning prompt is issued.
[0053] In the disclosed method, the early warning prompt includes sending a prompt message of the possible occurrence of the target event to the financial institution and / or the transaction party through one or more forms of telephone, text message, and email, so as to remind the transaction party and the financial institution to pay attention to the current transaction operation and take relevant measures in a timely manner.
[0054] According to an embodiment of the present disclosure, the step of determining a target event after executing the current transaction operation includes:
[0055] Get historical transaction data;
[0056] Determine candidate events after the current transaction operation is executed according to the historical transaction data;
[0057] At least one of the candidate events is selected as a target event.
[0058] In the disclosed method, the historical transaction data includes historical income amount, historical transfer amount, historical transfer frequency, historical transaction object, historical transaction time distribution, etc.
[0059] In the present disclosure, the candidate events are high-risk or illegal transaction events that may occur, such as telecommunications fraud, money laundering, account theft, and the like.
[0060] According to an embodiment of the present disclosure, selecting at least one of the candidate events as a target event includes:
[0061] Sorting the candidate events in descending order according to their risk levels;
[0062] At least one candidate event ranked high is used as the target event.
[0063] In the disclosed method, the candidate events are determined based on historical transaction data, and multiple possible candidate events are determined through historical transaction data, and the candidate events are sorted in descending order according to their risk levels, i.e., the harmfulness of the candidate events. For example, the candidate events and risk levels determined based on historical transaction data are telecommunications fraud-level one risk, misappropriation of accounts-level two risk, and money laundering-level three risk, respectively. Then, the candidate events are sorted in the following order: 1. telecommunications fraud, 2. misappropriation of accounts, 3. money laundering, and telecommunications fraud and misappropriation of accounts with higher risk levels are selected as target events based on the sorting.
[0064] According to an embodiment of the present disclosure, the historical transaction data and the portrait data of the transaction parties have different sources.
[0065] In the disclosed method, the historical transaction data and the portrait data of the transaction parties come from different sources. The implementer of the historical transaction data is the account user to be warned, so the data source is the database of the financial institution where the account of the current user to be warned is located. The portrait data of the transaction party includes not only the portrait data of the transaction initiator, but also the portrait data of the transaction recipient, so the data source of the portrait data of the transaction party is the database of the financial institution where the accounts of each transaction party are located. Determining the probability of the target event by using data from different sources can avoid the misjudgment that may be caused by a single data source, thereby more objectively identifying transaction risks and providing early warning prompts.
[0066] In the disclosed method, the portrait data of the transaction party includes: at least one of consumption portrait data, behavior portrait data, and interest portrait data. The consumption portrait data is the historical consumption data of the transaction party in a recent period of time or a specific time period, including the total consumption amount of the transaction party in the time period, the maximum single consumption amount, the average monthly consumption amount, the place where the consumption behavior occurred, and other data. The behavior portrait data is the historical behavior data of the transaction party in a recent period of time or a specific time period, including the total loan amount of the transaction party in the time period, the maximum single loan amount, the place where the loan behavior occurred, the loan collateral, and other data. The interest portrait data is the historical income source and expenditure destination data of the transaction party in a recent period of time or a specific time period, including work unit, salary income, income interval time, consumer object category, and other data.
[0067] According to an embodiment of the present disclosure, determining the probability of the target event occurring based on the portrait data of the transaction party includes:
[0068] Training a probabilistic model;
[0069] The portrait data of the transaction parties is input into the probability model to obtain the probability of the target event occurring.
[0070] In the disclosed method, the back propagation algorithm is used to adjust the network parameters, and then retraining is performed until convergence or the number of training times is reached. At the same time, the probability model is a deep neural network model with converged training; during the training process, the network nodes of the deep neural network model before training need to be compressed first. In this embodiment, the network nodes of all layers are compressed by dividing them by 2. Node compression can achieve model training convergence using less sample data, and can reduce the probability of overfitting during training; after the node compression, the relevant loss function is selected. In this embodiment, the deep neural network classic loss function is used; at the same time, the loss function needs to be updated. In this embodiment, when updating, the regularization rule is used to regularize the network nodes of all layers of the neural network model before training to form a regularization term, and the regularization term is added to the original loss function to form an updated loss function.
[0071] According to an embodiment of the present disclosure, the method further includes step S104:
[0072] In step S104, if the probability is not within a preset threshold range, a preset operation is performed.
[0073] In the disclosed method, if the probability is no longer within the preset threshold range, it means that a high-risk or illegal target event is very likely to occur and needs to be intervened in time. The preset operations include: suspending the current transaction operation, freezing the transaction party's capital account, freezing the current transaction operation, and recording the location of the transaction party. For example, if the threshold range is 40%, when the target event is telecommunications fraud and the probability of occurrence is 60%, the transaction party's account is frozen and the current transaction operation is frozen to prevent the transaction party from losing money; when the probability of occurrence is 20%, an early warning message is issued to the transaction party and the financial institution to remind the transaction party and the financial institution to pay attention to the current transaction operation and prevent possible target events.
[0074] Figure 2 The structural block diagram of the transaction risk warning device according to the embodiment of the present disclosure is shown. The device can be implemented as part or all of the electronic device through software, hardware or a combination of both.
[0075] like Figure 2 As shown, the transaction risk warning device 200 includes a first determination module 210 , a second determination module 220 , and a warning prompt module 230 .
[0076] A first determination module 210 is configured to determine a target event after executing the current transaction operation;
[0077] The second determination module 220 is configured to determine the probability of the target event occurring according to the portrait data of the transaction party;
[0078] The early warning prompt module 230 is configured to issue an early warning prompt if the probability is within a preset threshold range.
[0079] The technical solution disclosed in the present invention can achieve the technical effect of quickly and accurately confirming whether there is a risk in the current transaction operation by determining the target event based on the current transaction data, determining the probability of the target event based on the portrait data of the transaction parties, and then issuing an early warning or taking measures for the current transaction operation based on the probability of the target event.
[0080] In the disclosed method, the early warning prompt includes sending a prompt message of the possible occurrence of the target event to the financial institution and / or the transaction party through one or more forms of telephone, text message, and email, so as to remind the transaction party and the financial institution to pay attention to the current transaction operation and take relevant measures in a timely manner.
[0081] According to an embodiment of the present disclosure, the step of determining a target event after executing the current transaction operation includes:
[0082] Get historical transaction data;
[0083] Determine candidate events after the current transaction operation is executed according to the historical transaction data;
[0084] At least one of the candidate events is selected as a target event.
[0085] In the disclosed method, the historical transaction data includes historical income amount, historical transfer amount, historical transfer frequency, historical transaction object, historical transaction time distribution, etc.
[0086] In the present disclosure, the candidate events are high-risk or illegal transaction events that may occur, such as telecommunications fraud, money laundering, account theft, and the like.
[0087] According to an embodiment of the present disclosure, selecting at least one of the candidate events as a target event includes:
[0088] Sorting the candidate events in descending order according to their risk levels;
[0089] At least one candidate event ranked high is used as the target event.
[0090] In the disclosed method, the candidate events are determined based on historical transaction data, and multiple possible candidate events are determined through historical transaction data, and the candidate events are sorted in descending order according to their risk levels, i.e., the harmfulness of the candidate events. For example, the candidate events and risk levels determined based on historical transaction data are telecommunications fraud-level one risk, misappropriation of accounts-level two risk, and money laundering-level three risk, respectively. Then, the candidate events are sorted in the following order: 1. telecommunications fraud, 2. misappropriation of accounts, 3. money laundering, and telecommunications fraud and misappropriation of accounts with higher risk levels are selected as target events based on the sorting.
[0091] According to an embodiment of the present disclosure, the historical transaction data and the portrait data of the transaction parties have different sources.
[0092] In the disclosed method, the historical transaction data and the portrait data of the transaction parties come from different sources. The implementer of the historical transaction data is the account user to be warned, so the data source is the database of the financial institution where the account of the current user to be warned is located. The portrait data of the transaction party includes not only the portrait data of the transaction initiator, but also the portrait data of the transaction recipient, so the data source of the portrait data of the transaction party is the database of the financial institution where the accounts of each transaction party are located. Determining the probability of the target event by using data from different sources can avoid the misjudgment that may be caused by a single data source, thereby more objectively identifying transaction risks and providing early warning prompts.
[0093] In the disclosed method, the portrait data of the transaction party includes: at least one of consumption portrait data, behavior portrait data, and interest portrait data. The consumption portrait data is the historical consumption data of the transaction party in a recent period of time or a specific time period, including the total consumption amount of the transaction party in the time period, the maximum single consumption amount, the average monthly consumption amount, the place where the consumption behavior occurred, and other data. The behavior portrait data is the historical behavior data of the transaction party in a recent period of time or a specific time period, including the total loan amount of the transaction party in the time period, the maximum single loan amount, the place where the loan behavior occurred, the loan collateral, and other data. The interest portrait data is the historical income source and expenditure destination data of the transaction party in a recent period of time or a specific time period, including work unit, salary income, income interval time, consumer object category, and other data.
[0094] According to an embodiment of the present disclosure, determining the probability of the target event occurring based on the portrait data of the transaction party includes:
[0095] Training a probabilistic model;
[0096] The portrait data of the transaction parties is input into the probability model to obtain the probability of the target event occurring.
[0097] In the disclosed method, the back propagation algorithm is used to adjust the network parameters, and then retraining is performed until convergence or the number of training times is reached. At the same time, the probability model is a deep neural network model with converged training; during the training process, the network nodes of the deep neural network model before training need to be compressed first. In this embodiment, the network nodes of all layers are compressed by dividing them by 2. Node compression can achieve model training convergence using less sample data, and can reduce the probability of overfitting during training; after the node compression, the relevant loss function is selected. In this embodiment, the deep neural network classic loss function is used; at the same time, the loss function needs to be updated. In this embodiment, when updating, the regularization rule is used to regularize the network nodes of all layers of the neural network model before training to form a regularization term, and the regularization term is added to the original loss function to form an updated loss function.
[0098] According to an embodiment of the present disclosure, the transaction risk warning device further includes:
[0099] The execution module 240 is configured to execute a preset operation if the probability is not within a preset threshold range.
[0100] In the disclosed method, if the probability is no longer within the preset threshold range, it means that a high-risk or illegal target event is very likely to occur and needs to be intervened in time. The preset operations include: suspending the current transaction operation, freezing the transaction party's capital account, freezing the current transaction operation, and recording the location of the transaction party. For example, if the threshold range is 40%, when the target event is telecommunications fraud and the probability of occurrence is 60%, the transaction party's account is frozen and the current transaction operation is frozen to prevent the transaction party from losing money; when the probability of occurrence is 20%, an early warning message is issued to the transaction party and the financial institution to remind the transaction party and the financial institution to pay attention to the current transaction operation and prevent possible target events.
[0101] The present disclosure also discloses an electronic device, Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.
[0102] like Figure 3 As shown, the electronic device includes a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to an embodiment of the present disclosure.
[0103] A transaction risk early warning method, comprising:
[0104] Determine the target event after performing the current trading operation;
[0105] Determine the probability of the target event occurring based on the profile data of the transaction parties;
[0106] If the probability is within a preset threshold range, an early warning prompt is issued.
[0107] The technical solution disclosed in the present invention can achieve the technical effect of quickly and accurately confirming whether there is a risk in the current transaction operation by determining the target event based on the current transaction data, determining the probability of the target event based on the portrait data of the transaction parties, and then issuing an early warning or taking measures for the current transaction operation based on the probability of the target event.
[0108] According to an embodiment of the present disclosure, the step of determining a target event after executing the current transaction operation includes:
[0109] Get historical transaction data;
[0110] Determine candidate events after the current transaction operation is executed according to the historical transaction data;
[0111] At least one of the candidate events is selected as a target event.
[0112] According to an embodiment of the present disclosure, selecting at least one of the candidate events as a target event includes:
[0113] Sorting the candidate events in descending order according to their risk levels;
[0114] At least one candidate event ranked high is used as the target event.
[0115] According to an embodiment of the present disclosure, the historical transaction data and the portrait data of the transaction parties have different sources.
[0116] According to an embodiment of the present disclosure, determining the probability of the target event occurring based on the portrait data of the transaction party includes:
[0117] Training a probabilistic model;
[0118] The portrait data of the transaction parties is input into the probability model to obtain the probability of the target event occurring.
[0119] According to an embodiment of the present disclosure, the transaction risk warning method further includes:
[0120] If the probability is not within a preset threshold range, a preset operation is performed.
[0121] Figure 4 A schematic diagram showing the structure of a computer system suitable for implementing the method according to an embodiment of the present disclosure is shown.
[0122] like Figure 4As shown, the computer system includes a processing unit, which can perform the various methods in the above-mentioned embodiments according to the program stored in the read-only memory (ROM) or the program loaded from the storage part into the random access memory (RAM). In the RAM, various programs and data required for the operation of the computer system are also stored. The processing unit, ROM and RAM are connected to each other through a bus. The input / output (I / O) interface is also connected to the bus.
[0123] The following components are connected to the I / O interface: an input part including a keyboard, a mouse, etc.; an output part including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage part including a hard disk, etc.; and a communication part including a network interface card such as a LAN card, a modem, etc. The communication part performs a communication process via a network such as the Internet. The drive is also connected to the I / O interface as needed. Removable media, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that the computer program read therefrom is installed into the storage part as needed. Among them, the processing unit can be implemented as a processing unit such as a CPU, a GPU, a TPU, an FPGA, an NPU, etc.
[0124] In particular, according to an embodiment of the present disclosure, the method described above can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes a program code for executing the above method. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium.
[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0126] The units or modules involved in the embodiments described in the present disclosure may be implemented by software or programmable hardware. The units or modules described may also be set in a processor, and the names of these units or modules do not constitute limitations on the units or modules themselves in some cases.
[0127] As another aspect, the present disclosure further provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or computer system in the above embodiment; or a computer-readable storage medium that exists independently and is not assembled into a device. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the method described in the present disclosure.
[0128] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other.
Claims
1. A transaction risk early warning method, characterized in that: include: Determine the target event after performing the current trading operation; Determine the probability of the target event occurring based on the profile data of the transaction parties; If the probability is within a preset threshold range, an early warning prompt is issued.
2. The transaction risk early warning method according to claim 1, characterized in that: The target event after the current transaction operation is determined includes: Get historical transaction data; Determine candidate events after the current transaction operation is executed according to the historical transaction data; At least one of the candidate events is selected as a target event.
3. The transaction risk early warning method according to claim 2, characterized in that: The selecting at least one of the candidate events as the target event comprises: Sorting the candidate events in descending order according to their risk levels; At least one candidate event ranked high is used as the target event.
4. The transaction risk early warning method according to claim 2, characterized in that: The historical transaction data and the portrait data of the transaction parties have different sources.
5. The transaction risk early warning method according to claim 1, characterized in that: Determining the probability of the target event occurring according to the profile data of the transaction party includes: Training a probabilistic model; The portrait data of the transaction parties is input into the probability model to obtain the probability of the target event occurring.
6. The transaction risk early warning method according to claim 1, characterized in that: Also includes: If the probability is not within a preset threshold range, a preset operation is performed.
7. A transaction risk early warning device, characterized in that: include: A first determination module is configured to determine a target event after executing the current transaction operation; A second determination module is configured to determine the probability of the target event occurring according to the portrait data of the transaction party; The early warning prompt module is configured to issue an early warning prompt if the probability is within a preset threshold range.
8. The transaction risk early warning device according to claim 7, characterized in that: The part of the first determination module that determines the target event after executing the current transaction operation is configured as follows: Get historical transaction data; Determine candidate events after the current transaction operation is executed according to the historical transaction data; At least one of the candidate events is selected as a target event.
9. An electronic device, characterized in that: The method comprises a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps described in any one of claims 1-6.
10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the method steps described in any one of claims 1 to 6 are implemented.