CN110705995A - Data tagging method and device - Google Patents
Data tagging method and device Download PDFInfo
- Publication number
- CN110705995A CN110705995A CN201910960574.6A CN201910960574A CN110705995A CN 110705995 A CN110705995 A CN 110705995A CN 201910960574 A CN201910960574 A CN 201910960574A CN 110705995 A CN110705995 A CN 110705995A
- Authority
- CN
- China
- Prior art keywords
- abnormal
- information
- transaction
- collection
- account
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Computer Security & Cryptography (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The embodiment of the specification provides a data labeling method and device. The data labeling method comprises the following steps: identifying at least two kinds of identification information based on the original data according to a first preset rule; and adjusting one identification information based on the other identification information.
Description
Technical Field
The present specification relates to the field of data processing technologies, and in particular, to a data tagging method. The specification also relates to a data labeling device, a computing device and a computer readable storage medium.
Background
The labeling processing of the data refers to a process of calibrating positive and negative samples in the data set. When the online payment system processes the bottom line risk, a large amount of transactions, accounts and identity dimensions need to be managed and controlled; the bottom line risks belong to unsupervised risks, the risk labels which can be relied on are based on complaints, the coverage is incomplete, and the timeliness is relatively delayed. However, the huge control magnitude and the strong control means all require the stability and high accuracy of the control strategy in the aspect of systematicness, so that the unsupervised or semi-supervised risk without labels is labeled and supervised, and the label is put into the existing risk assessment system with accuracy and coverage, which becomes the urgent task of the current risk management work.
Taking the supervision of the non-bank payment mechanism by the supervision mechanism as an example, an important aspect is that the non-bank mechanism cannot support the illegal transaction fund payment (or called deposit and recharge) of the network illegal transaction platform (including websites and intelligent applications). Because the social objective has the serious problem of network illegal transactions, and the network payment system can be used for receiving single transaction and transferring payment products, when risk strategy control is deployed, although the accuracy of the strategy system basically reaches 95 percent or even 99 percent, the phenomenon of mis-audit often occurs because the audit quantity is large; in addition, many more concealed illegal transactions are isolated outside the prevention and control and are difficult to find.
Although some data labeling modes exist in the prior art, for example, the method depends on the post-incident attack of a user complaint, if the threshold value is set to be enough for the complaint and the sources are dispersed enough, the accuracy is close to 100%, but the timeliness is poor. In addition, a fishing mode can be adopted to simulate a user to place orders for a relevant platform and intelligent application, the accuracy rate is 100%, but the method is easy to attack and defense by black products, has high construction and operation and maintenance cost in the technical aspect, and needs to consume large audition human resources.
Disclosure of Invention
In view of this, the embodiments of the present specification provide a data tagging method. The present specification also relates to a data labeling apparatus, a computing device, and a computer-readable storage medium, which are used to solve the technical defects in the prior art.
According to a first aspect of embodiments herein, there is provided a data tagging method including: identifying at least two kinds of identification information based on the original data according to a first preset rule; and adjusting one identification information based on the other identification information.
According to a second aspect of embodiments herein, there is provided a data labeling apparatus comprising: the identification module is configured to identify at least two kinds of identification information based on the original data according to a first preset rule; and an adjusting module configured to adjust one of the at least two kinds of identification information based on the other identification information.
According to a third aspect of embodiments herein, there is provided a computing device comprising a memory, a processor and computer instructions stored on the memory and executable on the processor, the processor implementing the steps of the data tagging method when executing the instructions.
According to a fourth aspect of embodiments herein, there is provided a computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the data tagging method.
According to the data tagging method and device, after at least two kinds of identification information are identified based on transaction data, the other kind of identification information can be adjusted based on one kind of identification information in the at least two kinds of identification information, and therefore identification accuracy and coverage of the identification information can be greatly improved. In addition, the whole recognition process can be automatically and intelligently executed, manual participation is not needed, timeliness is obviously improved, and labor cost is saved.
Drawings
FIG. 1 illustrates a block diagram of a computing device provided by an embodiment of the present description;
FIG. 2 is a flow chart illustrating a data tagging method provided by an embodiment of the present specification;
FIG. 3 is a flow chart illustrating adjusting illegal funding collection account information in a data tagging method provided by an embodiment of the present specification;
FIG. 4 is a flow chart illustrating adjusting illegal funding collection account information in a data tagging method provided by an embodiment of the present specification;
FIG. 5 is a flow chart illustrating adjusting illegal funding transaction information in a data tagging method provided by an embodiment of the present specification;
FIG. 6 is a flow chart illustrating the adjustment of illegal funding transaction information in another data tagging method provided by an embodiment of the present specification;
FIG. 7 is a flow chart illustrating a data tagging method provided by an embodiment of the present specification;
FIG. 8 is a flow chart illustrating adjusting exception collection account information in a data tagging method provided by an embodiment of the present specification;
FIG. 9 is a flow chart illustrating adjusting exception collection account information in a data tagging method according to another embodiment of the present disclosure;
FIG. 10 is a flow chart illustrating adjusting exception collection account information in another data tagging method provided by another embodiment of the present specification;
FIG. 11 is a flowchart illustrating adjusting exception collection account information in a data tagging method according to another embodiment of the present disclosure;
FIG. 12 is a flow chart illustrating adjustment of exception transaction information in a data tagging method provided by an embodiment of the present specification;
FIG. 13 is a flow chart illustrating adjustment of anomalous transaction information in a data tagging method according to another embodiment of the present disclosure;
FIG. 14 is a flow chart illustrating adjustment of anomalous transaction information in another data tagging method provided by another embodiment of the present description;
FIG. 15 is a flowchart illustrating adjusting anomalous payment account information in a data tagging method provided by an embodiment of the present specification;
FIG. 16 is a flow chart illustrating adjustment of anomalous payment account information in a data tagging method according to another embodiment of the present specification;
FIG. 17 is a flow chart illustrating adjustment of anomalous payment account information in another data tagging method provided by another embodiment of the present description;
fig. 18 shows a schematic structural diagram of a data labeling apparatus provided in an embodiment of the present specification.
Fig. 19 is a schematic structural diagram illustrating a second data labeling apparatus provided in an embodiment of the present specification.
Fig. 20 is a schematic structural diagram of a third data labeling apparatus provided in an embodiment of the present specification.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.
The terminology used in the description of the one or more embodiments is for the purpose of describing the particular embodiments only and is not intended to be limiting of the description of the one or more embodiments. As used in one or more embodiments of the present specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It will be understood that, although the terms first, second, etc. may be used herein in one or more embodiments to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first can also be referred to as a second and, similarly, a second can also be referred to as a first without departing from the scope of one or more embodiments of the present description. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
In the present application, a data tagging method is provided. The present specification also relates to a data labeling apparatus, a computing device, and a computer-readable storage medium, which are described in detail in the following embodiments one by one.
Fig. 1 shows a block diagram of a computing device 100 provided in an embodiment of the present specification. The components of the computing device 100 include, but are not limited to, memory 110 and processor 120. The processor 120 is coupled to the memory 110 via a bus 130 and a database 150 is used to store data.
Computing device 100 also includes access device 140, access device 140 enabling computing device 100 to communicate via one or more networks 160. Examples of such networks include the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. Access device 140 may include one or more of any type of network interface (e.g., a Network Interface Card (NIC)) whether wired or wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a bluetooth interface, a Near Field Communication (NFC) interface, and so forth.
In one embodiment of the present description, the above-described components of computing device 100 and other components not shown in FIG. 1 may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device architecture shown in FIG. 1 is for purposes of example only and is not limiting as to the scope of the description. Those skilled in the art may add or replace other components as desired.
Computing device 100 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), a mobile phone (e.g., smartphone), a wearable computing device (e.g., smartwatch, smartglasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. Computing device 100 may also be a mobile or stationary server.
Wherein the processor 120 may perform the steps of the data tagging method shown in fig. 2. Fig. 2 shows a flow chart of a data tagging method according to an embodiment of the present specification, including step 202 through step 208.
The data labeling method of the embodiment of the application comprises the following steps:
step 202: at least two kinds of identification information are identified based on the original data according to a first preset rule.
The original data is source data to be labeled, and the identification information is information obtained by labeling the original data, for example, in an illegal transaction identification scenario, the source data to be labeled may be transaction data, and the illegal transaction, a payee of the illegal transaction, or a payee of the illegal transaction may be the labeled identification information. However, it should be understood that the specific contents of the original data and the identification information may be adjusted according to the application scenario of the data tagging method, and the specific contents of the original data and the identification information are not strictly limited in this application.
It should be further understood that the first preset rule for identifying the identification information may also be adjusted according to the requirements of the specific application scenario of the data tagging method, and it is sufficient to obtain various features of the raw data through analysis, and classify the raw data based on the first preset rule to identify the at least two types of identification information, and the specific content of the first preset rule is not strictly limited in the present application.
Step 204: adjusting one identification information of the at least two identification information based on the other identification information.
Although two kinds of identification information are identified based on the first preset rule in step 202, the accuracy and coverage of the identified identification information may not be sufficient at this time, limited by the first preset rule itself. However, since the recognized identification information is derived from the original information, there should be a characteristic correlation between the recognized identification information, and therefore, if one type of identification information can be adjusted by referring to the other identification information at the same time, the accuracy and coverage of the one type of identification information can be inevitably improved.
Therefore, according to the data tagging method provided by the embodiment of the application, after at least two kinds of identification information are identified based on the original data, another kind of identification information can be adjusted based on one kind of identification information in the at least two kinds of identification information, and therefore the identification accuracy and the coverage of the identification information can be greatly improved. In addition, the whole recognition process can be automatically and intelligently executed, manual participation is not needed, timeliness is obviously improved, and labor cost is saved.
In an optional embodiment, the original data is transaction data, and the at least two kinds of identification information include illegal funding collection account information and illegal funding transaction information, so that adjusting one kind of identification information based on the at least two kinds of identification information may include adjusting illegal funding transaction information based on the illegal funding collection account information and adjusting illegal funding collection account information based on the illegal funding transaction information.
In an alternative embodiment, as shown in FIG. 3, adjusting the illegal funding collection account information based on the illegal funding transaction information comprises:
step 302: all collection accounts are obtained based on the transaction information.
Step 304: and judging whether the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is larger than a first preset value or not based on the illegal funding transaction information.
Step 306: and when the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is larger than a first preset value, judging whether the collection account belongs to the illegal funding collection account or not based on the illegal funding collection account information.
Step 308: and when the collection account does not belong to the illegal funding collection account, adding the collection account into the illegal funding collection account information.
When the occupation ratio of the illegal funding transaction participated by the collection account in all the transactions participated by the collection account is larger than the first preset value, the collection account participates in a lot of illegal funding transactions, and the collection account is identified as the illegal funding collection account.
In an alternative embodiment, as shown in FIG. 4, adjusting the illegal funding collection account information based on the illegal funding transaction information comprises:
step 402: and acquiring all illegal funding collection accounts based on the illegal funding collection account information.
Step 404: and based on the illegal funding transaction information and the transaction information, when the occupation ratio of the illegal funding transaction participated by the illegal funding collection account in all transactions participated by the illegal funding collection account is judged to be less than a second preset value, removing the illegal funding collection account from the illegal funding collection account information.
When the occupation ratio of the illegal funding transaction participating in the illegal funding collection account in all transactions participating in the illegal funding collection account is smaller than the second preset value, the illegal funding collection account is shown not to participate in a lot of transactions identified as illegal funding transactions, namely the illegal funding collection account is mistakenly identified as the illegal funding collection account, and the illegal funding collection account is removed from the illegal funding collection account information at the moment so as to improve the identification accuracy of the illegal funding collection account.
In an alternative embodiment, as shown in FIG. 5, adjusting the illegal funding transaction information based on the illegal funding collection account information comprises:
step 502: all collection accounts are obtained based on the transaction information.
Step 504: and based on the illegal funding transaction information and the illegal funding collection account information, when the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is judged to be larger than a third preset value and the collection account belongs to the illegal funding collection account, adding the transaction of which the collection account is not identified as the illegal funding transaction into the illegal funding transaction information.
When the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is judged to be larger than the third preset value, and the collection account is identified as the illegal funding collection account, the other transactions participated by the collection account are probably illegal transactions, and the transactions of which the collection account is not identified as the illegal funding transaction are added into the illegal funding transaction information so as to improve the identification accuracy of the illegal funding transaction.
In an alternative embodiment, as shown in FIG. 6, adjusting the illegal funding transaction information based on the illegal funding collection account information comprises:
step 602: all collection accounts are obtained based on the transaction information.
Step 604: and based on the illegal funding transaction information and the illegal funding collection account information, when the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is judged to be less than a fourth preset value and the collection account does not belong to the illegal funding collection account, removing the transaction identified as the illegal funding transaction from the illegal funding transaction information.
When the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is judged to be less than the fourth preset value, and the collection account does not belong to the illegal funding transaction account, the fact that all transactions participated by the collection account should not be the illegal funding transaction is indicated, and at the moment, in order to further improve the accuracy rate of the illegal funding transaction, the transactions of which the collection account is identified as the illegal funding transaction can be removed from the illegal funding transaction information.
In an alternative embodiment, as shown in fig. 7, the data tagging method may include:
step 702: and identifying abnormal transaction information, abnormal payment account information and abnormal collection account information based on the transaction data according to a first preset rule.
The transaction data may be all transaction information acquired or directly acquired from a third party in a certain time period under a specific application scenario, and the transaction information may include not only information such as the amount, time, location, and account of a transaction, but also account information of a sponsor account and a payee account of the transaction, which participate in the transaction. The abnormal payment account information is attribute information of an abnormal payment account identified according to transaction data based on a preset rule, such as an abnormal payment account number, a probability of identifying the abnormal payment account corresponding to the abnormal payment account number, and the like. The abnormal collection account information is attribute information of an abnormal collection account identified according to the transaction data based on a preset rule, such as an abnormal collection account number and a probability of identifying the abnormal collection account corresponding to the abnormal collection account number.
It should be understood that the first preset rule for identifying the transaction information, the abnormal payment account information, and the abnormal collection account information may be adjusted according to the requirements of the application scenario, and the specific content of the first preset rule is not strictly limited in the present application.
Step 704: and adjusting the abnormal collection account information based on the abnormal transaction information and the abnormal payment account information.
Based on the abnormal transaction information and the abnormal payment account information, whether the abnormal collection account in the abnormal collection account information is accurately identified, whether a clear account is mistakenly judged as the abnormal collection account, and whether the abnormal collection account is missed or not can be judged.
Step 706: and adjusting the abnormal transaction information based on the abnormal payment account information and the abnormal collection account information before or after the adjustment.
Based on the abnormal payment account information and the abnormal collection account information, whether the abnormal transactions in the abnormal transaction information are accurately identified, whether clear transactions are mistakenly judged as abnormal transactions and whether the abnormal transactions are missed can be judged. Since the adjustment of the abnormal charge account information based on the abnormal payment account information and the abnormal charge account information is performed in real time, the abnormal payment account information referred to at this time may be before the adjustment based on step 704 or after the adjustment based on step 704.
Step 708: and adjusting the abnormal payment account information based on the abnormal transaction information and the abnormal collection account information before or after the adjustment.
Based on the abnormal transaction information and the abnormal collection account information, whether the abnormal payment account in the abnormal payment account information is accurately identified, whether a clear account is mistakenly judged as the abnormal payment account, and whether the abnormal payment account is missed or not can be judged. Since the adjustment of the anomalous payment account information based on the anomalous transaction information and the anomalous collection account information is performed in real time, the anomalous transaction information referred to at this time may be before the adjustment based on the step 706 or after the adjustment based on the step 706.
Therefore, according to the data tagging method provided by the embodiment of the application, after the abnormal transaction information, the abnormal payment account information and the abnormal collection account information are identified based on the transaction data, the abnormal transaction information can be further adjusted according to the abnormal payment account information and the abnormal collection account information, the abnormal payment account information can be further adjusted according to the abnormal transaction information and the abnormal collection account information, and meanwhile, the abnormal collection account information can be further adjusted according to the abnormal payment account information and the abnormal transaction information, so that the identification accuracy and the coverage of the abnormal transaction information, the abnormal payment account information and the abnormal collection account information can be greatly improved. In addition, the whole recognition process can be automatically and intelligently executed, manual participation is not needed, timeliness is obviously improved, and labor cost is saved.
In an alternative embodiment, as shown in fig. 8, the specific process of adjusting the abnormal collection account information based on the abnormal transaction information and the abnormal payment account information may include the following steps:
step 802: and acquiring all collection accounts and all payment accounts participating in the transaction based on the transaction data, and acquiring all abnormal payment accounts participating in the abnormal transaction based on the abnormal payment account information.
Step 804: and when the occupation ratio of all the abnormal payment accounts in all the payment accounts is larger than a fifth preset value, acquiring accounts which are not identified as the abnormal payment accounts in all the payment accounts participating in the transaction based on the abnormal payment account information.
Step 806: and adding the acquired account into the abnormal collection account information.
When the proportion of the abnormal payment accounts in all the payment accounts is larger than the fifth preset value, the abnormal payment accounts are more likely to be abnormal transactions, at the moment, all the accounts which participate in the transactions and are not identified as the abnormal collection accounts can be counted as the abnormal collection accounts, and the information of the abnormal collection accounts is added, so that the identification coverage rate of the abnormal collection accounts is improved.
In an alternative embodiment, as shown in fig. 9, the specific process of adjusting the abnormal collection account information based on the abnormal transaction information and the abnormal payment account information may include the following steps:
step 902: and acquiring an abnormal collection account and an abnormal payment account participating in the abnormal transaction based on the abnormal collection account information and the abnormal payment account information.
Step 904: and when the account occupation ratio of the abnormal payment account participating in the abnormal transaction is smaller than a sixth preset value, removing the abnormal payment account participating in the abnormal transaction from the abnormal payment account information.
Since in some abnormal transactions, the abnormal collection account will also be the abnormal payment account. In an illegal transaction scenario, for example, the illegal transaction collection accounts are sometimes rotated. Therefore, when the account occupation ratio of the abnormal collection accounts participating in the abnormal transactions is smaller than the sixth preset value, it indicates that the abnormal transactions are likely not abnormal transactions, and therefore the abnormal collection accounts participating in the abnormal transactions are also likely to be misidentified and need to be removed from the abnormal collection account information, so as to improve the accuracy of identifying the abnormal collection accounts.
In an alternative embodiment, as shown in fig. 10, the specific process of adjusting the abnormal collection account information based on the abnormal transaction information and the abnormal payment account information may include the following steps:
step 1002: and acquiring all transactions of the payee of the abnormal transaction based on the abnormal transaction information.
Step 1004: and when the proportion of the abnormal transactions in all transactions is larger than the seventh preset value, judging whether the payee is in the abnormal payment account information.
Step 1006: when the payee is not in the abnormal receipt account information, adding the account of the payee to the abnormal receipt account information.
When the proportion of the abnormal transactions in all transactions is larger than the seventh preset value, the other transactions are possibly abnormal transactions and are not identified based on the first preset rule, and the accounts of the payee who is not identified as the abnormal collection account can be added into the abnormal collection account information so as to improve the identification coverage rate of the abnormal collection account.
In an alternative embodiment, as shown in fig. 11, the specific process of adjusting the abnormal collection account information based on the abnormal transaction information and the abnormal payment account information may include the following steps:
step 1102: and acquiring the transaction in which the abnormal charge account participates based on the abnormal charge account information.
Step 1104: and based on the abnormal transaction information, when the proportion of the abnormal transactions identified in the transactions involving the abnormal collection account is smaller than an eighth preset value, removing the abnormal collection account from the abnormal collection account information.
When the proportion of the abnormal transactions in the transactions participated by the abnormal collection account is smaller than the eighth preset value, the abnormal collection account is probably not the abnormal collection account but is mistakenly identified, and the abnormal collection account can be removed from the abnormal collection account information at the moment so as to improve the identification accuracy of the abnormal collection account.
In an alternative embodiment, as shown in fig. 12, the specific process of adjusting the abnormal transaction information based on the abnormal payment account information and the abnormal collection account information before or after the adjustment may include the following steps:
step 1202: and acquiring personal information of the abnormal payment account based on the abnormal payment account information.
Step 1204: and based on a second preset rule, when the abnormal payment account is judged to be the clearing account according to the personal information of the abnormal payment account, removing the abnormal transaction participated by the abnormal payment account from the abnormal transaction information.
It should be understood that the second preset rule may be adjusted according to an actual application scenario, for example, when it is found that the abnormal payment account is an account that is unlikely to have a large payment capability after the personal information of the abnormal payment account is retrieved, for example, an underage account indicates that the abnormal payment account is likely to be a clearing account in fact, at this time, the abnormal transaction involving the abnormal payment account may be removed from the abnormal transaction information, so as to improve the accuracy of identifying the abnormal transaction.
In an alternative embodiment, as shown in fig. 13, the specific process of adjusting the abnormal transaction information based on the abnormal payment account information and the abnormal collection account information before or after the adjustment may include the following steps:
step 1302: and acquiring the transaction in which the abnormal charge account participates based on the information of the abnormal charge account before or after the adjustment.
Step 1304: and on the basis of the abnormal transaction information, when the proportion of the abnormal transactions identified in the transactions participated in by the abnormal collection account is greater than a ninth preset value, adding the transactions which are not identified as the abnormal transactions in the transactions participated in by the abnormal collection account into the abnormal transaction information.
When the proportion of the transactions in which the abnormal collection account participates is identified as abnormal transactions is larger than the ninth preset value, the transactions which are not identified as abnormal transactions in the transactions in which the abnormal collection account participates are also likely to be abnormal transactions, and the transactions which are not identified as abnormal transactions can be added into the abnormal transaction information so as to improve the identification coverage rate of the abnormal transactions.
In an alternative embodiment, as shown in fig. 14, the specific process of adjusting the abnormal transaction information based on the abnormal payment account information and the abnormal collection account information before or after the adjustment may include the following steps:
step 1402: a payee that is not identified as an anomalous collection account is obtained based on the transaction data and the anomalous collection account information before or after the adjustment.
Step 1404: and based on the abnormal transaction information, when the proportion of all the transactions identified as abnormal transactions of the payee is judged to be lower than a tenth preset value, removing the transactions identified as abnormal transactions of the payee from the abnormal transaction information.
When the proportion of all the transactions of the payee identified as abnormal transactions is lower than the tenth preset value, the payee shall not be an abnormal payment account, and the transaction in which the payee participates shall not be an abnormal transaction. At this time, the transaction of which the payee is identified as an abnormal transaction can be removed from the abnormal transaction information, so that the identification accuracy of the abnormal transaction is improved.
In an alternative embodiment, as shown in fig. 15, the specific process of adjusting the abnormal payment account information based on the abnormal transaction information and the abnormal collection account information before or after the adjustment may include the following steps:
step 1502: and acquiring all accounts transacting with the abnormal collection account based on the transaction data and the abnormal collection account information.
Step 1504: and on the basis of the abnormal payment account information, when the proportion of all accounts transacted with the abnormal payment account, which are identified as abnormal payment accounts, is judged to be greater than the eleventh preset value, adding accounts, which are not identified as abnormal payment accounts, of all accounts transacted with the abnormal payment account into the abnormal payment account information.
When the proportion of all accounts transacting with the abnormal payment accounts, which are identified as abnormal payment accounts, is greater than the eleventh preset value, the transactions transacted with the abnormal payment accounts are probably all abnormal transactions, and the payment accounts participating in the transactions are also probably all abnormal payment accounts, at the moment, the accounts which are not identified as the abnormal payment accounts in all accounts transacting with the abnormal payment accounts can be added into the abnormal payment account information, so that the identification coverage rate of the abnormal payment accounts is improved.
In an alternative embodiment, as shown in fig. 16, the specific process of adjusting the abnormal payment account information based on the abnormal transaction information and the abnormal collection account information before or after the adjustment may include the following steps:
step 1602: and acquiring a payment account of which the proportion of the participated abnormal transaction in all participated transactions is greater than a twelfth preset value based on the transaction data and the abnormal transaction information before or after the adjustment.
Step 1604: when the acquired payment account is not in the anomalous payment account information, adding the acquired payment account to the anomalous payment account information.
When an exception transaction in which a payment account participates occupies a significant portion of all transactions in which it participates, the payment account is likely to be an exception payment account. Meanwhile, when the payment account is not in the abnormal payment account information, the payment account is not identified based on the first preset rule, and the abnormal payment account information can be added to the payment account so as to improve the identification coverage rate of the abnormal payment account.
In an alternative embodiment, as shown in fig. 17, the specific process of adjusting the abnormal payment account information based on the abnormal transaction information and the abnormal collection account information before or after the adjustment may include the following steps:
step 1702: and acquiring all transactions participated in by the abnormal payment account based on the abnormal payment account information.
Step 1704: based on the abnormal transaction information before or after the adjustment, when the proportion of all transactions in which the abnormal payment account participates, which are identified as abnormal transactions, is less than a thirteenth preset value, the abnormal payment account is removed from the abnormal payment account information.
When the proportion of the abnormal transactions in all transactions in which the abnormal payment account participates is identified to be less than the thirteenth preset value, the transactions in which the abnormal payment account participates may not be the abnormal transactions, and the abnormal payment account is also identified by mistake, at this time, the abnormal payment account can be removed from the information of the abnormal payment account, so as to improve the identification accuracy of the abnormal payment account.
It should be understood that the first preset value, the second preset value, the third preset value, the fourth preset value, the fifth preset value, the sixth preset value, the seventh preset value, the eighth preset value, the ninth preset value, the tenth preset value, the eleventh preset value, the twelfth preset value and the thirteenth preset value involved in the adjustment process shown in the above embodiments of fig. 3 to 17 may be set and adjusted according to the requirements of an actual scene, and the specific size of these preset values is not limited in this application.
It should also be appreciated that the adjustment processes shown in the above embodiments of fig. 3-17 may be combined virtually arbitrarily. In an embodiment of the present application, all the adjustment processes shown in the above embodiments of fig. 3 to 17 may also be implemented simultaneously, so as to implement mutual referencing and mutual adjustment of the abnormal transaction information, the abnormal payment account information, and the abnormal collection account information, so as to further provide accuracy and coverage rate of abnormal transaction identification.
Corresponding to the above method embodiment, the present specification further provides an embodiment of a data tagging apparatus, and fig. 18 shows a schematic structural diagram of a data tagging apparatus provided in an embodiment of the present specification. As shown in fig. 18, the data labeling apparatus 1800 includes:
an identifying module 1801 configured to identify at least two kinds of identification information based on the original data according to a first preset rule; and
an adjusting module 1802 configured to adjust one of the at least two kinds of identification information based on the other identification information.
According to the data labeling device provided by the embodiment of the application, after at least two kinds of identification information are identified based on original data, the other kind of identification information can be adjusted based on one kind of identification information in the at least two kinds of identification information, so that the identification accuracy and the coverage of the identification information can be greatly improved. In addition, the whole recognition process can be automatically and intelligently executed, manual participation is not needed, timeliness is obviously improved, and labor cost is saved.
In an alternative embodiment, as shown in fig. 19, the original data is transaction data, and the at least two kinds of identification information include illegal funding collection account information and illegal funding transaction information;
wherein, the adjusting module 1802 comprises:
a first adjusting unit 18021 configured to adjust the illegal funding transaction information based on the illegal funding collection account information; and
a second adjusting unit 18022, configured to adjust the illegal funding collection account information based on the illegal funding transaction information.
In an optional embodiment, the second adjusting unit 18022 is further configured to: acquiring all collection accounts based on the transaction information; judging whether the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is larger than a first preset value or not based on the illegal funding transaction information; when the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is larger than a first preset value, judging whether the collection account belongs to the illegal funding collection account or not based on the illegal funding collection account information; and adding the collection account into the illegal funding collection account information when the collection account does not belong to the illegal funding collection account.
In an optional embodiment, the second adjusting unit 18022 is further configured to: acquiring all illegal funding collection accounts based on the illegal funding collection account information; and based on the illegal funding transaction information and the transaction information, when the occupation ratio of the illegal funding transaction participated by the illegal funding collection account in all transactions participated by the illegal funding collection account is judged to be less than a second preset value, removing the illegal funding collection account from the illegal funding collection account information.
In an optional embodiment, the first adjusting unit 18021 is further configured to: acquiring all collection accounts based on the transaction information; and based on the illegal funding transaction information and the illegal funding collection account information, when the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is judged to be larger than a third preset value and the collection account belongs to the illegal funding collection account, adding the transaction of which the collection account is not identified as the illegal funding transaction into the illegal funding transaction information.
In an optional embodiment, the first adjusting unit 18021 is further configured to: acquiring all collection accounts based on the transaction information; and based on the illegal funding transaction information and the illegal funding collection account information, when the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is judged to be less than a fourth preset value and the collection account does not belong to the illegal funding collection account, removing the transaction identified as the illegal funding transaction from the illegal funding transaction information.
In an alternative embodiment, as shown in fig. 20, the original data is transaction data, and the at least two kinds of identification information include abnormal transaction information, abnormal payment account information, and abnormal collection account information
Wherein, the adjusting module 1802 comprises:
a third adjusting unit 18023 configured to adjust the abnormal collection account information based on the abnormal transaction information and the abnormal payment account information;
a fourth adjusting unit 18024 configured to adjust the abnormal transaction information based on the abnormal payment account information and the abnormal collection account information before or after the adjustment; and
a fifth adjusting unit 18025 configured to adjust the anomalous payment account information based on the anomalous transaction information and the anomalous collection account information before or after the adjustment.
Therefore, according to the data tagging device 1800 provided by the embodiment of the application, after the abnormal transaction information, the abnormal payment account information and the abnormal collection account information are identified based on the transaction data, the abnormal transaction information can be further adjusted according to the abnormal payment account information and the abnormal collection account information, the abnormal payment account information can be further adjusted according to the abnormal transaction information and the abnormal collection account information, and meanwhile, the abnormal collection account information can be further adjusted according to the abnormal payment account information and the abnormal transaction information, so that the identification accuracy and the coverage of the abnormal transaction information, the abnormal payment account information and the abnormal collection account information can be greatly improved. In addition, the whole recognition process can be automatically and intelligently executed, manual participation is not needed, timeliness is obviously improved, and labor cost is saved.
In an alternative embodiment, the third adjusting unit 18023 of the adjusting module 1802 is further configured to: acquiring all collection accounts and all payment accounts participating in the transaction based on the transaction data, and acquiring all abnormal payment accounts participating in the abnormal transaction based on the abnormal payment account information; when the proportion of all the abnormal payment accounts in all the payment accounts is larger than a fifth preset value, acquiring accounts which are not identified as abnormal payment accounts in all the payment accounts participating in the transaction based on the abnormal payment account information; and adding the acquired account into the abnormal collection account information.
In an alternative embodiment, the third adjusting unit 18023 of the adjusting module 1802 is further configured to: acquiring an abnormal collection account and an abnormal payment account participating in abnormal transaction based on the abnormal collection account information and the abnormal payment account information; and when the account occupation ratio of the abnormal payment account participating in the abnormal transaction is smaller than a sixth preset value, removing the abnormal payment account participating in the abnormal transaction from the abnormal payment account information.
In an alternative embodiment, the third adjusting unit 18023 of the adjusting module 1802 is further configured to: acquiring all transactions of a payee of the abnormal transactions based on the abnormal transaction information; when the proportion of the abnormal transactions in all transactions is larger than a seventh preset value, judging whether a payee is in the abnormal payment account information or not; and adding the account of the payee to the abnormal payment account information when the payee is not in the abnormal payment account information.
In an alternative embodiment, the third adjusting unit 18023 of the adjusting module 1802 is further configured to: acquiring a transaction in which the abnormal collection account participates based on the abnormal collection account information; and based on the abnormal transaction information, when the proportion of the abnormal transactions identified in the transactions participated by the abnormal collection account is smaller than the eighth preset value, removing the abnormal collection account from the abnormal collection account information.
In an alternative embodiment, the adjusting module 1802 and the fourth adjusting unit 18024 are further configured to: acquiring personal information of the abnormal payment account based on the abnormal payment account information; and based on a second preset rule, when the abnormal payment account is judged to be the clearing account according to the personal information of the abnormal payment account, removing the abnormal transaction participated by the abnormal payment account from the abnormal transaction information.
In an alternative embodiment, the adjusting module 1802 and the fourth adjusting unit 18024 are further configured to: acquiring the transaction in which the abnormal collection account participates based on the information of the abnormal collection account before or after the adjustment; and adding the transaction which is not identified as the abnormal transaction in the transaction in which the abnormal collection account participates into the abnormal transaction information when the proportion of the transactions in which the abnormal collection account participates is identified as the abnormal transaction is larger than a ninth preset value based on the abnormal transaction information.
In an alternative embodiment, the adjusting module 1802 and the fourth adjusting unit 18024 are further configured to: obtaining a payee not identified as an abnormal collection account based on the transaction data and the abnormal collection account information before or after the adjustment; and removing the transaction identified as the abnormal transaction by the payee from the abnormal transaction information when the proportion identified as the abnormal transaction in all the transactions by the payee is determined to be lower than a tenth preset value based on the abnormal transaction information.
In an alternative embodiment, the fifth adjusting unit 18025 of the adjusting module 1802 is further configured to: acquiring all accounts transacting with the abnormal collection account based on the transaction data and the abnormal collection account information; and on the basis of the abnormal payment account information, when the proportion of all accounts transacted with the abnormal payment account, which are identified as abnormal payment accounts, is judged to be greater than the eleventh preset value, adding accounts, which are not identified as abnormal payment accounts, of all accounts transacted with the abnormal payment account into the abnormal payment account information.
In an alternative embodiment, the fifth adjusting unit 18025 of the adjusting module 1802 is further configured to: acquiring a payment account of which the proportion of the participated abnormal transaction in all participated transactions is greater than a twelfth preset value based on the transaction data and the abnormal transaction information; and adding the acquired payment account to the abnormal payment account information when the acquired payment account is not in the abnormal payment account information.
In an alternative embodiment, the fifth adjusting unit 18025 of the adjusting module 1802 is further configured to: acquiring all transactions participated in by the abnormal payment account based on the abnormal payment account information; and based on the abnormal transaction information before or after the adjustment, when the proportion of all transactions in which the abnormal payment account participates, which are identified as abnormal transactions, is less than a thirteenth preset value, removing the abnormal payment account from the abnormal payment account information.
The detailed functions and operations of the respective blocks in the data tagging apparatus 1800 have been described in detail in the above data tagging method, and thus, a repetitive description thereof will be omitted herein.
There is also provided in an embodiment of the present specification a computing device comprising a memory, a processor, and computer instructions stored on the memory and executable on the processor, the steps of the data tagging method being implemented when the processor executes the instructions.
An embodiment of the present application also provides a computer readable storage medium, which stores computer instructions, and when the instructions are executed by a processor, the instructions implement the steps of the foregoing data labeling method.
The above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium belongs to the same concept as the technical solution of the data tagging method, and for details that are not described in detail in the technical solution of the storage medium, reference may be made to the description of the technical solution of the data tagging method.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The computer instructions comprise computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The preferred embodiments of the present application disclosed above are intended only to aid in the explanation of the application. Alternative embodiments are not exhaustive and do not limit the application to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.
Claims (20)
1. A method for tagging data, comprising:
identifying at least two kinds of identification information based on the original data according to a first preset rule; and
and adjusting one identification information based on the other identification information.
2. The method of claim 1, wherein the raw data is transaction data, and the at least two types of identifying information include illegal funding collection account information and illegal funding transaction information;
wherein the adjusting of the one identification information based on the at least two identification information comprises:
adjusting the illegal funding transaction information based on the illegal funding collection account information; and
and adjusting the illegal funding collection account information based on the illegal funding transaction information.
3. The method of claim 2, wherein said adjusting said illegal funding collection account information based on said illegal funding transaction information comprises:
acquiring all collection accounts based on the transaction information;
based on the illegal funding transaction information, judging whether the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is larger than a first preset value or not;
when the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is larger than a first preset value, judging whether the collection account belongs to an illegal funding collection account or not based on the illegal funding collection account information; and
and when the collection account does not belong to the illegal funding collection account, adding the collection account into the illegal funding collection account information.
4. The method of claim 2, wherein said adjusting said illegal funding collection account information based on said illegal funding transaction information comprises:
acquiring all illegal funding collection accounts based on the illegal funding collection account information;
and based on the illegal funding transaction information and the transaction information, when the occupation ratio of the illegal funding transaction participated by the illegal funding collection account in all transactions participated by the illegal funding collection account is judged to be less than a second preset value, removing the illegal funding collection account from the illegal funding collection account information.
5. The method of claim 2, wherein said adjusting said illegal funding transaction information based on said illegal funding collection account information comprises:
acquiring all collection accounts based on the transaction information;
and based on the illegal funding transaction information and the illegal funding collection account information, when the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is judged to be larger than a third preset value and the collection account belongs to the illegal funding collection account, adding the transaction of which the collection account is not identified as the illegal funding transaction into the illegal funding transaction information.
6. The method of claim 2, wherein said adjusting said illegal funding transaction information based on said illegal funding collection account information comprises:
acquiring all collection accounts based on the transaction information;
and based on the illegal funding transaction information and the illegal funding collection account information, when the occupation ratio of the illegal funding transaction participated by the collection account in all transactions participated by the collection account is judged to be less than a fourth preset value and the collection account does not belong to the illegal funding collection account, removing the transaction of which the collection account is identified as the illegal funding transaction from the illegal funding transaction information.
7. The method of claim 1, wherein the raw data is transaction data, and the at least two types of identification information include anomalous transaction information, anomalous payment account information, and anomalous collection account information;
wherein the adjusting of the one identification information based on the at least two identification information comprises:
adjusting the abnormal payment account information based on the abnormal transaction information and the abnormal payment account information;
adjusting the anomalous transaction information based on the anomalous payment account information and anomalous collection account information before or after adjustment; and
adjusting the anomalous payment account information based on anomalous transaction information and the anomalous collection account information before or after the adjusting.
8. The method of claim 7, wherein adjusting the anomalous collection account information based on the anomalous transaction information and the anomalous payment account information comprises:
acquiring all collection accounts and all payment accounts participating in the transaction based on the transaction data, and acquiring all abnormal payment accounts participating in the abnormal transaction based on the abnormal payment account information;
when the proportion of all the abnormal payment accounts in all the payment accounts is larger than a fifth preset value, acquiring accounts which are not identified as abnormal collection accounts in all the collection accounts participating in the transaction based on the abnormal collection account information; and
and adding the acquired account into the abnormal collection account information.
9. The method of claim 7, wherein adjusting the anomalous collection account information based on the anomalous transaction information and the anomalous payment account information comprises:
acquiring an abnormal collection account and an abnormal payment account participating in abnormal transaction based on the abnormal collection account information and the abnormal payment account information;
when the account proportion of the abnormal payment accounts participating in the abnormal transaction is smaller than a sixth preset value, removing the abnormal payment accounts participating in the abnormal transaction from the abnormal payment account information.
10. The method of claim 7, wherein adjusting the anomalous collection account information based on the anomalous transaction information and the anomalous payment account information comprises:
acquiring all transactions of a payee of the abnormal transactions based on the abnormal transaction information;
when the proportion of the abnormal transactions in all the transactions is larger than a seventh preset value, judging whether the payee is in the abnormal cash receiving account information or not; and
and when the payee is not in the abnormal payment account information, adding the account of the payee into the abnormal payment account information.
11. The method of claim 7, wherein adjusting the anomalous collection account information based on the anomalous transaction information and the anomalous payment account information comprises:
acquiring a transaction in which an abnormal collection account participates based on the abnormal collection account information; and
based on the abnormal transaction information, when the proportion of the abnormal transactions identified in the transactions involving the abnormal collection account is smaller than an eighth preset value, removing the abnormal collection account from the abnormal collection account information.
12. The method of claim 7, wherein adjusting the anomalous transaction information based on the anomalous payment account information and anomalous collection account information before or after adjustment comprises:
acquiring personal information of an abnormal payment account based on the abnormal payment account information; and
based on a second preset rule, when the abnormal payment account is judged to be a clearing account according to the personal information of the abnormal payment account, removing the abnormal transaction participated by the abnormal payment account from the abnormal transaction information.
13. The method of claim 7, wherein adjusting the anomalous transaction information based on the anomalous payment account information and anomalous collection account information before or after adjustment comprises:
obtaining a transaction in which an abnormal collection account participates based on the abnormal collection account information before or after the adjustment; and
and adding the transaction which is not identified as the abnormal transaction in the transaction in which the abnormal collection account participates into the abnormal transaction information when the proportion of the transactions in which the abnormal collection account participates is identified as the abnormal transaction is larger than a ninth preset value based on the abnormal transaction information.
14. The method of claim 7, wherein adjusting the anomalous transaction information based on anomalous payment account information before or after the anomalous payment account information adjustment comprises:
obtaining a payee not identified as an abnormal collection account based on the transaction data and the abnormal collection account information before or after the adjustment; and
based on the abnormal transaction information, when the proportion of all the transactions of the payee identified as abnormal transactions is judged to be lower than a tenth preset value, removing the transactions of the payee identified as abnormal transactions from the abnormal transaction information.
15. The method of claim 7, wherein the adjusting the anomalous payment account information based on the anomalous transaction information and the anomalous collection account information before or after the adjusting comprises:
acquiring all accounts transacting with the abnormal collection account based on the transaction data and the abnormal collection account information;
and on the basis of the abnormal payment account information, when the proportion of all accounts transacted with the abnormal payment account, which are identified as abnormal payment accounts, is judged to be greater than the eleventh preset value, adding accounts, which are not identified as abnormal payment accounts, of all accounts transacted with the abnormal payment account into the abnormal payment account information.
16. The method of claim 7, wherein the adjusting the anomalous payment account information based on the anomalous transaction information and the anomalous collection account information before or after the adjusting comprises:
acquiring a payment account of which the proportion of the participated abnormal transactions in all participated transactions is greater than a twelfth preset value based on transaction data and the abnormal transaction information before or after the adjustment; and
when the acquired payment account is not in the abnormal payment account information, adding the acquired payment account to the abnormal payment account information.
17. The method of claim 7, wherein the adjusting the anomalous payment account information based on the anomalous transaction information and the anomalous collection account information before or after the adjusting comprises:
acquiring all transactions participated in by the abnormal payment account based on the abnormal payment account information; and
based on the abnormal transaction information before or after the adjustment, when the proportion of all the participated transactions of the abnormal payment account, which are identified as abnormal transactions, is less than a thirteenth preset value, removing the abnormal payment account from the abnormal payment account information.
18. A data tagging apparatus, comprising:
the identification module is configured to identify at least two kinds of identification information based on the original data according to a first preset rule; and
an adjusting module configured to adjust one of the at least two kinds of identification information based on the other identification information.
19. A computing device comprising a memory, a processor, and computer instructions stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1-17 when executing the instructions.
20. A computer-readable storage medium storing computer instructions, which when executed by a processor, perform the steps of the method of any one of claims 1 to 17.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910960574.6A CN110705995B (en) | 2019-10-10 | 2019-10-10 | Data tagging method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910960574.6A CN110705995B (en) | 2019-10-10 | 2019-10-10 | Data tagging method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110705995A true CN110705995A (en) | 2020-01-17 |
| CN110705995B CN110705995B (en) | 2022-08-30 |
Family
ID=69200156
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910960574.6A Active CN110705995B (en) | 2019-10-10 | 2019-10-10 | Data tagging method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110705995B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108053318A (en) * | 2017-12-20 | 2018-05-18 | 北京奇安信科技有限公司 | It is a kind of to the method and device that is identified of merchandising extremely |
| CN108228706A (en) * | 2017-11-23 | 2018-06-29 | ***股份有限公司 | For identifying the method and apparatus of abnormal transaction corporations |
| CN109034583A (en) * | 2018-07-17 | 2018-12-18 | 阿里巴巴集团控股有限公司 | Abnormal transaction identification method, apparatus and electronic equipment |
| CN109583890A (en) * | 2018-11-09 | 2019-04-05 | 阿里巴巴集团控股有限公司 | Recognition methods, device and the equipment of abnormal trading object |
| CN110163618A (en) * | 2019-05-31 | 2019-08-23 | 深圳前海微众银行股份有限公司 | Extremely detection method, device, equipment and the computer readable storage medium traded |
| CN110276620A (en) * | 2019-06-28 | 2019-09-24 | 深圳前海微众银行股份有限公司 | It is a kind of to determine the method and device traded extremely |
-
2019
- 2019-10-10 CN CN201910960574.6A patent/CN110705995B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108228706A (en) * | 2017-11-23 | 2018-06-29 | ***股份有限公司 | For identifying the method and apparatus of abnormal transaction corporations |
| CN108053318A (en) * | 2017-12-20 | 2018-05-18 | 北京奇安信科技有限公司 | It is a kind of to the method and device that is identified of merchandising extremely |
| CN109034583A (en) * | 2018-07-17 | 2018-12-18 | 阿里巴巴集团控股有限公司 | Abnormal transaction identification method, apparatus and electronic equipment |
| CN109583890A (en) * | 2018-11-09 | 2019-04-05 | 阿里巴巴集团控股有限公司 | Recognition methods, device and the equipment of abnormal trading object |
| CN110163618A (en) * | 2019-05-31 | 2019-08-23 | 深圳前海微众银行股份有限公司 | Extremely detection method, device, equipment and the computer readable storage medium traded |
| CN110276620A (en) * | 2019-06-28 | 2019-09-24 | 深圳前海微众银行股份有限公司 | It is a kind of to determine the method and device traded extremely |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110705995B (en) | 2022-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111275546B (en) | Financial customer fraud risk identification method and device | |
| US11244232B2 (en) | Feature relationship recommendation method, apparatus, computing device, and storage medium | |
| CN110717758B (en) | Abnormal transaction identification method and device | |
| CN112785086A (en) | Credit overdue risk prediction method and device | |
| CN109919608B (en) | Identification method, device and server for high-risk transaction main body | |
| CN110675263B (en) | Risk identification method and device for transaction data | |
| CN112766825A (en) | Enterprise financial service risk prediction method and device | |
| CN113282623A (en) | Data processing method and device | |
| CN102902674A (en) | Service group classifying method and system | |
| CN110705995B (en) | Data tagging method and device | |
| CN113485993A (en) | Data identification method and device | |
| CN110796450B (en) | Trusted relationship processing method and device | |
| CN110610290B (en) | Inter-connected merchant risk management and control method and system thereof | |
| CN112579773A (en) | Risk event grading method and device | |
| CN117036001A (en) | Risk identification processing method, device and equipment for transaction service and storage medium | |
| CN109241249B (en) | Method and device for determining burst problem | |
| CN111340281B (en) | Prediction model training method and device | |
| CN114611149A (en) | Data processing method and device | |
| CN110020728B (en) | Service model reinforcement learning method and device | |
| CN111383026B (en) | Method and device for identifying abnormal transaction behaviors | |
| CN110808978B (en) | Real name authentication method and device | |
| CN110009368B (en) | Data processing method and device, computing equipment and storage medium | |
| CN112907266A (en) | User screening model acquisition method, financial information pushing method and related device | |
| CN111552846B (en) | Method and device for identifying suspicious relationships | |
| CN114155038B (en) | Epidemic situation affected user identification method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |