Transaction behavior detection method, device, electronic device and readable storage medium

Abstract

This application discloses a transaction behavior detection method, device, electronic device, and readable storage medium, which are applied to the field of financial technology, including: obtaining a credit database corresponding to a user, selecting a first target credit database from the credit database; selecting a second target credit database from the first target credit database; performing transaction behavior detection on the user based on the target credit data in the second target credit database to obtain a transaction behavior detection result; if the transaction behavior detection result shows that the transaction behavior is normal and the first target credit database has been selected, it is determined that the user has not engaged in abnormal transaction behavior; if the transaction behavior detection result shows that the transaction behavior is normal and the first target credit database has not been selected, returning to the first target credit database to select the second target credit database; if the transaction behavior detection result shows that the transaction behavior is abnormal, it is determined that the user has engaged in abnormal transaction behavior. This application solves the technical problem of high resource consumption in transaction behavior detection.

Description

Technical Field

[0001] The present application relates to the field of financial technology (Fi ntech), and in particular to a transaction behavior detection method, device, electronic device and readable storage medium. Background Art

[0002] With the continuous development of financial technology, more and more technologies are being applied in the financial field, but the financial industry also has higher requirements for users' trading behaviors.

[0003] At present, in order to detect users' transaction behaviors, user data in all credit databases are usually collected, and then the user's transaction behaviors are detected based on the user data. However, the query fees for querying all credit databases are high, and the detection of all user data may easily result in a long time required for transaction behavior detection, resulting in high cost and resource consumption for transaction behavior detection. Summary of the Invention

[0004] The main purpose of this application is to provide a transaction behavior detection method, device, electronic device and readable storage medium, aiming to solve the technical problem of high cost and resource consumption in transaction behavior detection in the existing technology.

[0005] To achieve the above objectives, the present application provides a transaction behavior detection method, which is applied to a transaction behavior detection device. The transaction behavior detection method includes:

[0006] Acquire at least one credit database corresponding to the user to be detected, and select at least one first target credit database corresponding to the user to be detected from each of the credit databases;

[0007] Selecting a second target credit database from each of the first target credit databases;

[0008] performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result;

[0009] If the transaction behavior detection result shows that the transaction behavior is normal and all the first target credit databases have been selected, it is determined that the user to be detected has no abnormal transaction behavior;

[0010] If the transaction behavior detection result is that the transaction behavior is normal and the first target credit databases have not been selected, then return to the execution step of selecting a second target credit database from each of the first target credit databases;

[0011] If the transaction behavior detection result is that the transaction behavior is abnormal, it is determined that the user to be detected has abnormal transaction behavior.

[0012] Optionally, the second target credit database includes a first target credit sub-database and a second target credit sub-database, the target credit data includes target first credit data and target second credit data, and the transaction behavior detection result includes a transaction behavior preliminary detection result and a transaction behavior in-depth detection result.

[0013] The step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result includes:

[0014] Performing a preliminary transaction behavior detection on the user to be detected based on the target first credit data in the first target credit sub-database to obtain the preliminary transaction behavior detection result; or

[0015] Based on the target second credit information data in the second target credit information sub-database, an in-depth transaction behavior detection is performed on the user to be detected to obtain the in-depth transaction behavior detection result.

[0016] Optionally, the step of selecting at least one first target credit database corresponding to the user to be detected from each of the credit databases includes:

[0017] Obtaining the credit resource usage of the user to be detected corresponding to each of the credit databases;

[0018] According to the usage of each of the credit resources, each of the first target credit databases corresponding to the user to be detected is selected from each of the credit databases.

[0019] Optionally, before the step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain the transaction behavior detection result, the method further includes:

[0020] Querying the user to be detected in the second target credit database to obtain a target query result type, wherein the target query result type includes at least one of an existing data type, a non-existing data type, and a query abnormality type;

[0021] If the target query result type includes the existing data type and / or the non-existing data type, it is determined that the query of the second target credit database is completed;

[0022] If the target query result type includes the query exception type, it is determined that the second target credit database has not been queried completely, and database query exception information is output for operation and maintenance personnel to repair the second target credit database.

[0023] Optionally, after the step of determining that the second target credit database has not been queried completely if the target query result type includes the query exception type, and outputting database query exception information, the method further includes:

[0024] Obtaining the allowed waiting time corresponding to the user to be detected;

[0025] If the second target credit database is repaired within the allowed waiting time, querying the second target credit database for the user to be detected to obtain target credit data, and executing the steps of: performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result;

[0026] If the second target credit database is not repaired within the allowed waiting time, the second target credit database is removed from the first target credit database, and the process returns to the step of selecting a second target credit database from each of the first target credit databases.

[0027] Optionally, the step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result includes:

[0028] Obtaining access rules corresponding to target credit data in the second target credit database, and executing the access rules to obtain access authority information corresponding to the user to be detected;

[0029] According to the access permission information, a transaction behavior detection is performed on the user to be detected to obtain a transaction behavior detection result.

[0030] Optionally, the transaction behavior detection method further includes:

[0031] Selecting a random credit database other than the first target credit database from each of the credit databases;

[0032] Performing transaction behavior detection on the user to be detected based on the random credit data in the random credit database to obtain a random behavior detection result;

[0033] If the random behavior detection result is that the transaction behavior is abnormal, the first target credit database is updated according to the random credit database.

[0034] To achieve the above objectives, the present application further provides a transaction behavior detection device, which includes:

[0035] an acquisition module, configured to acquire at least one credit database corresponding to the user to be detected, and select at least one first target credit database corresponding to the user to be detected from each of the credit databases;

[0036] a selection module, configured to select a second target credit database from each of the first target credit databases;

[0037] a detection module, configured to perform transaction behavior detection on the user to be detected based on the target credit data in the second target credit database, and obtain a transaction behavior detection result;

[0038] a first determination module configured to determine that the user to be detected has no abnormal transaction behavior if the transaction behavior detection result indicates that the transaction behavior is normal and all first target credit databases have been selected;

[0039] a return module, configured to return to the step of selecting a second target credit database from each of the first target credit databases if the transaction behavior detection result indicates that the transaction behavior is normal and the first target credit databases have not yet been selected;

[0040] The second determination module is configured to determine that the user to be detected has abnormal transaction behavior if the transaction behavior detection result is that the transaction behavior is abnormal.

[0041] Optionally, the second target credit database includes a first target credit sub-database and a second target credit sub-database, the target credit data includes target first credit data and target second credit data, and the transaction behavior detection result includes a transaction behavior preliminary detection result and a transaction behavior in-depth detection result.

[0042] The detection module is further configured to:

[0043] Performing a preliminary transaction behavior detection on the user to be detected based on the target first credit data in the first target credit sub-database to obtain the preliminary transaction behavior detection result; or

[0044] Based on the target second credit information data in the second target credit information sub-database, an in-depth transaction behavior detection is performed on the user to be detected to obtain the in-depth transaction behavior detection result.

[0045] Optionally, the acquisition module is further configured to:

[0046] Obtaining the credit resource usage of the user to be detected corresponding to each of the credit databases;

[0047] According to the usage of each of the credit resources, each of the first target credit databases corresponding to the user to be detected is selected from each of the credit databases.

[0048] Optionally, before the step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result, the transaction behavior detection device is further configured to:

[0049] Querying the user to be detected in the second target credit database to obtain a target query result type, wherein the target query result type includes at least one of an existing data type, a non-existing data type, and a query abnormality type;

[0050] If the target query result type includes the existing data type and / or the non-existing data type, it is determined that the query of the second target credit database is completed;

[0051] If the target query result type includes the query exception type, it is determined that the second target credit database has not been queried completely, and database query exception information is output for operation and maintenance personnel to repair the second target credit database.

[0052] Optionally, after the step of determining that the second target credit database has not been queried if the target query result type includes the query exception type and outputting database query exception information, the transaction behavior detection device is further configured to:

[0053] Obtaining the allowed waiting time corresponding to the user to be detected;

[0054] If the second target credit database is repaired within the allowed waiting time, querying the second target credit database for the user to be detected to obtain target credit data, and executing the steps of: performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result;

[0055] If the second target credit database is not repaired within the allowed waiting time, the second target credit database is removed from the first target credit database, and the process returns to the step of selecting a second target credit database from each of the first target credit databases.

[0056] Optionally, the detection module is further configured to:

[0057] Obtaining access rules corresponding to target credit data in the second target credit database, and executing the access rules to obtain access authority information corresponding to the user to be detected;

[0058] According to the access permission information, a transaction behavior detection is performed on the user to be detected to obtain a transaction behavior detection result.

[0059] Optionally, the transaction behavior detection device is further used to:

[0060] Selecting a random credit database other than the first target credit database from each of the credit databases;

[0061] Performing transaction behavior detection on the user to be detected based on the random credit data in the random credit database to obtain a random behavior detection result;

[0062] If the random behavior detection result is that the transaction behavior is abnormal, the first target credit database is updated according to the random credit database.

[0063] The present application also provides an electronic device, which includes: a memory, a processor, and a program of the transaction behavior detection method stored in the memory and runnable on the processor. When the program of the transaction behavior detection method is executed by the processor, the steps of the transaction behavior detection method as described above can be implemented.

[0064] The present application also provides a computer-readable storage medium, on which is stored a program for implementing the transaction behavior detection method. When the program of the transaction behavior detection method is executed by a processor, the steps of the transaction behavior detection method as described above are implemented.

[0065] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned transaction behavior detection method when executed by a processor.

[0066] The present application provides a transaction behavior detection method, device, electronic device and readable storage medium. Compared with the method of collecting user data from all credit databases and then detecting the user's transaction behavior based on the user data, the present application obtains at least one credit database corresponding to the user to be detected, selects at least one first target credit database corresponding to the user to be detected in each of the credit databases; selects a second target credit database in each of the first target credit databases; performs transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result; if the transaction behavior detection result is that the transaction behavior is normal and all the first target credit databases have been selected, it is determined that the user to be detected has no abnormal transaction behavior; if the transaction behavior detection result is that the transaction behavior is normal and all the first target credit databases have not been selected, then return to the execution step: select a second target credit database in each of the first target credit databases; if the transaction behavior detection result is that the transaction behavior is abnormal, then It is determined that the user to be detected has abnormal transaction behavior, which realizes the cyclic judgment of the user's transaction behavior. Therefore, when abnormal transaction behavior is detected, the judgment of the user's transaction behavior detection is output and the loop is stopped immediately. There is no need to query and detect all first target credit databases. Since not all credit databases in a large number of credit databases are effectively helpful for detecting the user's transaction behavior, there may be cases where the user has not used the applications corresponding to some credit databases. Therefore, by selecting a small number of target credit databases from a large number of credit databases corresponding to the user to be detected, the target credit data in a small number of target credit databases can be used to detect the transaction behavior of the user to be detected. Even if all first target credit databases are queried, the number of queried credit databases is still low, which reduces the time cost and money cost of transaction behavior detection, avoids the high query fee consumed by querying all credit databases, and avoids the technical defect that it takes a long time to detect transaction behavior when detecting all user data, thereby reducing the cost and resources consumed by transaction behavior detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0068] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0069] Figure 1This is a flowchart of the first embodiment of the transaction behavior detection method of this application;

[0070] Figure 2 This is a flow chart of the second embodiment of the transaction behavior detection method of this application;

[0071] Figure 3 Schematic diagram of the structure of the device involved in the transaction behavior detection method in the embodiment of the present application;

[0072] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the transaction behavior detection method in the embodiment of the present application.

[0073] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0074] To make the above-mentioned purposes, features, and advantages of the present application more clearly understood, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0075] Example 1

[0076] The present application provides a transaction behavior detection method. In the first embodiment of the transaction behavior detection method of the present application, referring to Figure 1 , the transaction behavior detection method includes:

[0077] Step S10, obtaining at least one credit database corresponding to the user to be detected, and selecting at least one first target credit database corresponding to the user to be detected from each of the credit databases;

[0078] Step S20, selecting a second target credit database from each of the first target credit databases;

[0079] Step S30, performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result;

[0080] Step S40: If the transaction behavior detection result shows that the transaction behavior is normal and all the first target credit databases have been selected, it is determined that the user to be detected has no abnormal transaction behavior;

[0081] Step S50: If the transaction behavior detection result is that the transaction behavior is normal and the first target credit databases have not been selected, then return to the step of selecting a second target credit database from the first target credit databases;

[0082] Step S60: If the transaction behavior detection result is that the transaction behavior is abnormal, it is determined that the user to be detected has abnormal transaction behavior.

[0083] It is understandable that the credit database contains a database of transaction data that may be generated by all users, and the more transaction data, the more accurate the detection of the user's transaction behavior. When different users have different corresponding credit databases that generate more transaction data, therefore, when all credit databases are tested for all users, it is easy to incur excessive cost and resource consumption.

[0084] In this embodiment, it should be noted that the first target credit database is a credit database that more accurately detects the transaction behavior of the user to be detected. The second target credit database is a credit database corresponding to the user to be detected that belongs to the first target credit database.

[0085] Exemplarily, steps S10 to S60 include: obtaining at least one credit database corresponding to the user to be detected, selecting at least one first target credit database corresponding to the user to be detected from each of the credit databases; selecting a second target credit database from each of the first target credit databases; querying the user to be detected in the second target credit database to obtain target credit data, performing transaction behavior detection on the user to be detected based on the target credit data to obtain a transaction behavior detection result; if the transaction behavior detection result is that the transaction behavior is normal and all of the first target credit databases have been selected, then it is determined that the user to be detected has no abnormal transaction behavior; if the transaction behavior detection result is that the transaction behavior is normal and all of the first target credit databases have not been selected, then returning to the execution step: selecting a second target credit database from each of the first target credit databases; if the transaction behavior detection result is that the transaction behavior is abnormal, then it is determined that the user to be detected has abnormal transaction behavior.

[0086] Wherein, in step S30, the second target credit database includes the first target credit sub-database and the second target credit sub-database, the target credit data includes the target first credit data and the target second credit data, and the transaction behavior detection result includes the transaction behavior preliminary detection result and the transaction behavior in-depth detection result.

[0087] The step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result includes:

[0088] Step A10: performing a preliminary transaction behavior detection on the user to be detected based on the target first credit data in the first target credit sub-database to obtain the preliminary transaction behavior detection result; or

[0089] Step A20: Performing an in-depth transaction behavior detection on the user to be detected based on the target second credit data in the second target credit sub-database to obtain the in-depth transaction behavior detection result.

[0090] It is understandable that when there are a large number of second target credit databases that can be selected from the first target credit database, and / or, when the transaction behavior detection results obtained based on the target credit data in the second target credit database are all normal transaction results, multiple rounds of selection of the second target credit database and detection and judgment of the transaction behavior are required, which makes the detection efficiency of the transaction behavior detection low.

[0091] In this embodiment, it should be noted that the number of the first target credit investigation sub-database can be single or multiple. The number of the second target credit investigation sub-database can be single or multiple. The first target credit investigation sub-database and the second target credit investigation sub-database are different, and the number of the first target credit investigation sub-database is smaller than the number of the second target credit investigation sub-database.

[0092] Exemplarily, steps A10 to A20 include: querying the first target credit sub-database for the user to be detected to obtain target first credit data; performing preliminary transaction behavior detection on the user to be detected based on the target first credit data to obtain preliminary transaction behavior detection results; or, querying the second target credit sub-database for the user to be detected to obtain target second credit data; performing in-depth transaction behavior detection on the user to be detected based on the target second credit data to obtain in-depth transaction behavior detection results. By simplifying the multi-round detection process of target credit data into a two-round detection process, the detection efficiency of transaction behavior detection is improved.

[0093] Wherein, in step S10, the step of selecting at least one first target credit database corresponding to the user to be detected from each of the credit databases includes:

[0094] Step S11, obtaining the credit resource usage of the user to be detected corresponding to each of the credit databases;

[0095] Step S12: selecting the first target credit databases corresponding to the user to be detected from the credit databases according to the usage of the credit resources.

[0096] In this embodiment, it should be noted that the credit resource usage is the usage of the credit resource by the user to be tested. The credit resource can be an application or other resources such as a bank card. The credit resource usage includes at least one of the frequency of use, transaction amount information and breach of contract information.

[0097] Exemplarily, steps S11 to S12 include: obtaining the credit resource usage of the user to be detected for each of the credit databases, the credit resource usage including at least one of usage frequency, transaction amount information and breach of contract information; and selecting the first target credit databases corresponding to the user to be detected in each of the credit databases based on the usage frequency and / or transaction amount information and / or breach of contract information.

[0098] As an example, step S12 includes: selecting each of the first target credit databases whose usage frequency is greater than a preset frequency threshold from each of the credit databases.

[0099] As an example, step S12 includes: selecting each first target credit database whose transaction amount information is greater than a preset amount threshold from each credit database.

[0100] As an example, step S12 includes: the breach of contract information includes the number of breaches, and a target first database is selected from each of the credit databases, where the number of breaches is greater than a preset first number threshold.

[0101] As an example, step S12 includes: obtaining a first weight corresponding to the frequency of use, a second weight corresponding to the transaction amount information, and a third weight corresponding to the breach of contract information; weighting the frequency of use, the transaction amount information, and the breach of contract information according to the first weight, the second weight, and the third weight, respectively, to obtain a weighted result; and selecting each of the first target credit databases from each of the credit databases according to the weighted result.

[0102] Wherein, in step S20, the step of selecting a second target credit database from each of the first target credit databases includes:

[0103] According to the usage of the credit resources, each of the first target credit databases is sorted to obtain a sorting result, wherein the sorting result is used to characterize the transaction behavior detection capability of each of the first target credit databases for the user to be detected. According to the sorting result, a second target credit database is selected from each of the first target credit databases.

[0104] As an example, each of the first target credit databases is sorted according to the usage frequency or the transaction amount information or the breach of contract information to obtain a sorting result. The sorting result is that the transaction behavior detection capability of each of the first target credit databases for the user to be detected is sorted from strong to weak. According to the sorting result, the second target credit database with the strongest transaction behavior detection capability is selected from each of the first target credit databases.

[0105] As an example, obtain the first preset database number corresponding to the first target credit sub-database and the second preset database number corresponding to the second target credit sub-database, wherein the first preset database number is less than the second preset database number; according to the sorting result, select at least one first target credit sub-database with the strongest transaction behavior detection capability among the first preset database number from each of the first target credit databases; according to the sorting result, select at least one second target credit sub-database with the strongest transaction behavior detection capability among the second preset database number from each of the first target credit databases other than the first target credit sub-database.

[0106] The embodiment of the present application provides a transaction behavior detection method. Compared with the method of collecting user data from all credit databases and then detecting the transaction behavior of the user based on the user data, the embodiment of the present application obtains at least one credit database corresponding to the user to be detected, selects at least one first target credit database corresponding to the user to be detected in each of the credit databases; selects a second target credit database in each of the first target credit databases; performs transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result; if the transaction behavior detection result is that the transaction behavior is normal and all the first target credit databases have been selected, it is determined that the user to be detected has no abnormal transaction behavior; if the transaction behavior detection result is that the transaction behavior is normal and all the first target credit databases have not been selected, then return to the execution step: select a second target credit database in each of the first target credit databases; if the transaction behavior detection result is that the transaction behavior is abnormal, then determine the The user to be detected has abnormal transaction behavior, which realizes the cyclic judgment of the user's transaction behavior. Therefore, when the transaction behavior is abnormal, the judgment of the user's transaction behavior detection is output and the loop is stopped immediately. There is no need to query and detect all the first target credit databases. Since not all credit databases in a large number of credit databases are effectively helpful for the detection of the user's transaction behavior, there may be cases where the user has not used the applications corresponding to some credit databases. Therefore, by selecting a small number of target credit databases from the large number of credit databases corresponding to the user to be detected, the target credit data in a small number of target credit databases can be used to detect the transaction behavior of the user to be detected. Even if all the first target credit databases are queried, the number of credit databases queried is still low, which reduces the time cost and money cost of transaction behavior detection, avoids the high query fee of querying all credit databases, and avoids the technical defect that it takes a long time to detect transaction behavior when detecting all user data, thereby reducing the cost and resources consumed by transaction behavior detection.

[0107] Example 2

[0108] Further, based on the first embodiment of the present application, in another embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 ,

[0109] Wherein, in step S30, before the step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain the transaction behavior detection result, the step further includes:

[0110] Step B10: querying the user to be detected in the second target credit database to obtain a target query result type, wherein the target query result type includes at least one of an existing data type, a non-existing data type, and a query abnormality type;

[0111] Step B20: If the target query result type includes the existing data type and / or the non-existing data type, it is determined that the query of the second target credit database is completed;

[0112] Step B30: If the target query result type includes the query exception type, it is determined that the second target credit database has not been queried, and database query exception information is output for operation and maintenance personnel to repair the second target credit database.

[0113] It is understandable that when querying the second target credit database, there may be problems with the query interface or network problems, resulting in query abnormalities. It may also be the case that there is no transaction data of the user to be detected in a certain credit database. Therefore, before conducting transaction behavior detection on the user to be detected, it is necessary to judge the query results of the second target credit database, so as to avoid the impact of query abnormalities or non-existence of transaction data on the detection of transaction behavior, thereby improving the detection accuracy of transaction behavior detection.

[0114] Wherein, in step B30, after the step of determining that the second target credit database has not been queried if the target query result type includes the query exception type and outputting database query exception information, the method further includes:

[0115] Step B40, obtaining the allowed waiting time corresponding to the user to be detected;

[0116] Step B50: If the second target credit database is repaired within the allowed waiting time, query the second target credit database for the user to be detected to obtain target credit data, and then execute the following step: perform transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain transaction behavior detection results;

[0117] Step B60: If the second target credit database is not repaired within the allowed waiting time, the second target credit database is removed from the first target credit database, and the process returns to the step of selecting a second target credit database from each of the first target credit databases.

[0118] It is understandable that when an abnormal query type occurs in the second target credit database, the operation and maintenance personnel may fail to repair the second target credit database in a timely manner. If they continue to wait for the second target credit database to be repaired, the transaction behavior detection will take a longer time.

[0119] In this embodiment, it should be noted that the allowed waiting time is the total detection time allowed during the transaction behavior detection process of the user to be detected.

[0120] Exemplarily, steps B40 to B60 include: obtaining the allowed waiting time corresponding to the user to be detected, and determining whether the second target credit database has been repaired within the allowed waiting time; if the second target credit database has been repaired within the allowed waiting time, querying the user to be detected in the second target credit database to obtain target credit data, and executing the step: performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result; if the second target credit database has not been repaired within the allowed waiting time, removing the second target credit database from the first target credit database, and returning to execute the step: selecting the second target credit database from each of the first target credit databases, and setting the allowed waiting time, thereby avoiding the technical defect of a long detection time required for transaction behavior detection due to the operation and maintenance personnel failing to repair the second target credit database in a timely manner, thereby improving the detection efficiency of transaction behavior detection.

[0121] Wherein, in step S30, the step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain the transaction behavior detection result includes:

[0122] Step S31, obtaining the access rules corresponding to the target credit data in the second target credit database, and executing the access rules to obtain the access authority information corresponding to the user to be detected;

[0123] Step S32: performing transaction behavior detection on the user to be detected based on the access authority information to obtain a transaction behavior detection result.

[0124] In this embodiment, it should be noted that the access rule is a rule for determining whether a user has access authority.

[0125] Exemplarily, steps S31 to S32 include: obtaining the access rules corresponding to the target credit data in the second target credit database, and executing the access rules to obtain the access permission information corresponding to the user to be detected, the access permission information including access information and non-access information; if the access permission information is the access information, then the transaction behavior detection result is determined to be normal transaction behavior; if the access permission information is the non-access information, then the transaction behavior detection result is determined to be abnormal transaction behavior.

[0126] Wherein, in step S60, after the step of determining that the user to be detected does not have abnormal transaction behavior if the transaction behavior detection result is normal and all the first target credit databases have been selected, the method further includes:

[0127] Step S70, selecting a random credit database other than the first target credit database from the credit databases;

[0128] Step S80, performing transaction behavior detection on the user to be detected based on the random credit data in the random credit database to obtain a random behavior detection result;

[0129] Step S90: If the random behavior detection result is that the transaction behavior is abnormal, the first target credit database is updated according to the random credit database.

[0130] It is understandable that since the transaction behavior of the user to be detected is not a fixed parameter, if the transaction behavior of the user to be detected is only detected on the target credit data in the fixed second target credit database, it is easy to result in low detection accuracy of the transaction behavior detection.

[0131] In this embodiment, it should be noted that the random credit database is a database randomly selected from the credit database except the first target credit database, and the number of the random credit databases can be single or multiple.

[0132] Exemplarily, steps S70 to S80 include: selecting a random credit database other than the first target credit database from each of the credit databases; querying the user to be detected in the random credit database to obtain random credit data; performing transaction behavior detection on the user to be detected based on the random credit data to obtain a random behavior detection result; if the random behavior detection result is that the transaction behavior is abnormal, adding the random credit database to the first target credit database to update the first target credit database; if the random behavior detection result is that the transaction behavior is normal, returning to the execution step: selecting a random credit database other than the first target credit database from each of the credit databases, and randomly detecting the credit database regularly or periodically, so that the first target credit database corresponding to the user to be detected can be updated in real time, thereby improving the detection accuracy of transaction behavior detection.

[0133] The embodiment of the present application provides a transaction behavior detection method. Compared with the method of collecting user data from all credit databases and then detecting the transaction behavior of the user based on the user data, the embodiment of the present application obtains at least one credit database corresponding to the user to be detected, selects at least one first target credit database corresponding to the user to be detected in each of the credit databases; selects a second target credit database in each of the first target credit databases; performs transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result; if the transaction behavior detection result is that the transaction behavior is normal and all the first target credit databases have been selected, it is determined that the user to be detected has no abnormal transaction behavior; if the transaction behavior detection result is that the transaction behavior is normal and all the first target credit databases have not been selected, then return to the execution step: select a second target credit database in each of the first target credit databases; if the transaction behavior detection result is that the transaction behavior is abnormal, then determine the The user to be detected has abnormal transaction behavior, which realizes the cyclic judgment of the user's transaction behavior. Therefore, when the transaction behavior is abnormal, the judgment of the user's transaction behavior detection is output and the loop is stopped immediately. There is no need to query and detect all the first target credit databases. Since not all credit databases in a large number of credit databases are effectively helpful for the detection of the user's transaction behavior, there may be cases where the user has not used the applications corresponding to some credit databases. Therefore, by selecting a small number of target credit databases from the large number of credit databases corresponding to the user to be detected, the target credit data in a small number of target credit databases can be used to detect the transaction behavior of the user to be detected. Even if all the first target credit databases are queried, the number of credit databases queried is still low, which reduces the time cost and money cost of transaction behavior detection, avoids the high query fee of querying all credit databases, and avoids the technical defect that it takes a long time to detect transaction behavior when detecting all user data, thereby reducing the cost and resources consumed by transaction behavior detection.

[0134] Example 3

[0135] The present application also provides a transaction behavior detection device, referring to Figure 3 , the transaction behavior detection device includes:

[0136] an acquisition module, configured to acquire at least one credit database corresponding to the user to be detected, and select at least one first target credit database corresponding to the user to be detected from each of the credit databases;

[0137] a selection module, configured to select a second target credit database from each of the first target credit databases;

[0138] a detection module, configured to perform transaction behavior detection on the user to be detected based on the target credit data in the second target credit database, and obtain a transaction behavior detection result;

[0139] a first determination module configured to determine that the user to be detected has no abnormal transaction behavior if the transaction behavior detection result indicates that the transaction behavior is normal and all first target credit databases have been selected;

[0140] a return module, configured to return to the step of selecting a second target credit database from each of the first target credit databases if the transaction behavior detection result indicates that the transaction behavior is normal and the first target credit databases have not yet been selected;

[0141] The second determination module is configured to determine that the user to be detected has abnormal transaction behavior if the transaction behavior detection result is that the transaction behavior is abnormal.

[0142] Optionally, the second target credit database includes a first target credit sub-database and a second target credit sub-database, the target credit data includes target first credit data and target second credit data, and the transaction behavior detection result includes a transaction behavior preliminary detection result and a transaction behavior in-depth detection result.

[0143] The detection module is further configured to:

[0144] Performing a preliminary transaction behavior detection on the user to be detected based on the target first credit data in the first target credit sub-database to obtain the preliminary transaction behavior detection result; or

[0145] Based on the target second credit information data in the second target credit information sub-database, an in-depth transaction behavior detection is performed on the user to be detected to obtain the in-depth transaction behavior detection result.

[0146] Optionally, the acquisition module is further configured to:

[0147] Obtaining the credit resource usage of the user to be detected corresponding to each of the credit databases;

[0148] According to the usage of each of the credit resources, each of the first target credit databases corresponding to the user to be detected is selected from each of the credit databases.

[0149] Optionally, before the step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result, the transaction behavior detection device is further configured to:

[0150] Querying the user to be detected in the second target credit database to obtain a target query result type, wherein the target query result type includes at least one of an existing data type, a non-existing data type, and a query abnormality type;

[0151] If the target query result type includes the existing data type and / or the non-existing data type, it is determined that the query of the second target credit database is completed;

[0152] If the target query result type includes the query exception type, it is determined that the second target credit database has not been queried completely, and database query exception information is output for operation and maintenance personnel to repair the second target credit database.

[0153] Optionally, after the step of determining that the second target credit database has not been queried if the target query result type includes the query exception type and outputting database query exception information, the transaction behavior detection device is further configured to:

[0154] Obtaining the allowed waiting time corresponding to the user to be detected;

[0155] If the second target credit database is repaired within the allowed waiting time, querying the second target credit database for the user to be detected to obtain target credit data, and executing the steps of: performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result;

[0156] If the second target credit database is not repaired within the allowed waiting time, the second target credit database is removed from the first target credit database, and the process returns to the step of selecting a second target credit database from each of the first target credit databases.

[0157] Optionally, the detection module is further configured to:

[0158] Obtaining access rules corresponding to target credit data in the second target credit database, and executing the access rules to obtain access authority information corresponding to the user to be detected;

[0159] According to the access permission information, a transaction behavior detection is performed on the user to be detected to obtain a transaction behavior detection result.

[0160] Optionally, the transaction behavior detection device is further used to:

[0161] Selecting a random credit database other than the first target credit database from each of the credit databases;

[0162] Performing transaction behavior detection on the user to be detected based on the random credit data in the random credit database to obtain a random behavior detection result;

[0163] If the random behavior detection result is that the transaction behavior is abnormal, the first target credit database is updated according to the random credit database.

[0164] The transaction behavior detection device provided in this application utilizes the transaction behavior detection method described in the aforementioned embodiments, resolving the technical issue of high cost and resource consumption associated with transaction behavior detection. Compared to the prior art, the transaction behavior detection device provided in this application's embodiments achieves the same beneficial effects as the transaction behavior detection method described in the aforementioned embodiments. Other technical features of this transaction behavior detection device are the same as those disclosed in the aforementioned embodiments and are not further elaborated upon here.

[0165] Example 4

[0166] An embodiment of the present application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the transaction behavior detection method in the above embodiment.

[0167] Reference below Figure 4 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0168] like Figure 4 As shown, the electronic device may include a processing device (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0169] Typically, the following systems can be connected to the I / O interface: input devices such as a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices such as a magnetic tape, hard disk, etc.; and communication devices. The communication device can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figures show electronic devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have instead.

[0170] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart 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 carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0171] The electronic device provided in this application utilizes the transaction behavior detection method described in the aforementioned embodiments, resolving the technical issue of high cost and resource consumption associated with transaction behavior detection. Compared to the prior art, the electronic device provided in this application's embodiments achieves the same beneficial effects as the transaction behavior detection method described in the aforementioned embodiments. Other technical features of this electronic device are the same as those disclosed in the aforementioned embodiments and are not further elaborated upon here.

[0172] It should be understood that various parts of the present disclosure can be implemented with hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0173] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0174] Example 5

[0175] This embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon, and the computer-readable program instructions are used to execute the transaction behavior detection method in the above embodiment.

[0176] The computer-readable storage medium provided in the embodiment of the present application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. A more specific example of a computer-readable storage medium can include, but is not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by an instruction execution system, a system or a device or used in combination therewith. The program code contained in the computer-readable storage medium can be transmitted with any appropriate medium, including but not limited to: an electric wire, an optical cable, RF (radio frequency), etc., or any suitable combination thereof.

[0177] The computer-readable storage medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0178] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: obtains at least one credit database corresponding to the user to be detected, and selects at least one first target credit database corresponding to the user to be detected from each of the credit databases; selects a second target credit database from each of the first target credit databases; performs transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result; if the transaction behavior detection result is that the transaction behavior is normal and all the first target credit databases have been selected, it is determined that the user to be detected has no abnormal transaction behavior; if the transaction behavior detection result is that the transaction behavior is normal and all the first target credit databases have not been selected, then return to the execution step: select a second target credit database from each of the first target credit databases; if the transaction behavior detection result is that the transaction behavior is abnormal, it is determined that the user to be detected has abnormal transaction behavior.

[0179] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0180] The flow charts 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 application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of 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 than that 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 flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0181] The modules described in the embodiments of the present disclosure may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0182] The computer-readable storage medium provided in this application stores computer-readable program instructions for executing the aforementioned transaction behavior detection method, thereby resolving the technical issue of high cost and resource consumption associated with transaction behavior detection. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this embodiment of the application are similar to those of the transaction behavior detection method provided in the aforementioned embodiment, and are not further elaborated here.

[0183] Example 6

[0184] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned transaction behavior detection method when executed by a processor.

[0185] The computer program product provided in this application solves the technical problem of high cost and resource consumption in transaction behavior detection. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as those of the transaction behavior detection method provided in the above embodiments, and will not be elaborated here.

[0186] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A transaction behavior detection method, characterized in that: The transaction behavior detection method includes: Acquire at least one credit database corresponding to the user to be detected, and select at least one first target credit database corresponding to the user to be detected from each of the credit databases; Selecting a second target credit database from each of the first target credit databases; performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result; If the transaction behavior detection result shows that the transaction behavior is normal and all the first target credit databases have been selected, it is determined that the user to be detected has no abnormal transaction behavior; If the transaction behavior detection result is that the transaction behavior is normal and the first target credit databases have not been selected, then return to the execution step of selecting a second target credit database from each of the first target credit databases; If the transaction behavior detection result is that the transaction behavior is abnormal, it is determined that the user to be detected has abnormal transaction behavior; Before the step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain the transaction behavior detection result, the method further includes: Querying the user to be detected in the second target credit database to obtain a target query result type, wherein the target query result type includes at least one of an existing data type, a non-existing data type, and a query abnormality type; If the target query result type includes the existing data type and / or the non-existing data type, it is determined that the query of the second target credit database is completed; If the target query result type includes the query exception type, it is determined that the query of the second target credit database has not been completed, and database query exception information is output for operation and maintenance personnel to repair the second target credit database; After the step of determining that the second target credit database has not been queried completely if the target query result type includes the query exception type and outputting database query exception information, the method further includes: Obtaining the allowed waiting time corresponding to the user to be detected; If the second target credit database is repaired within the allowed waiting time, querying the second target credit database for the user to be detected to obtain target credit data, and executing the steps of: performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result; If the second target credit database is not repaired within the allowed waiting time, the second target credit database is removed from the first target credit database, and the process returns to the step of selecting a second target credit database from each of the first target credit databases.

2. The transaction behavior detection method according to claim 1, characterized in that: The second target credit database includes a first target credit sub-database and a second target credit sub-database, the target credit data includes target first credit data and target second credit data, and the transaction behavior detection result includes a transaction behavior preliminary detection result and a transaction behavior in-depth detection result. The step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result includes: Performing a preliminary transaction behavior detection on the user to be detected based on the target first credit data in the first target credit sub-database to obtain the preliminary transaction behavior detection result; or Based on the target second credit information data in the second target credit information sub-database, an in-depth transaction behavior detection is performed on the user to be detected to obtain the in-depth transaction behavior detection result.

3. The transaction behavior detection method according to claim 1, characterized in that: The step of selecting at least one first target credit database corresponding to the user to be detected from each of the credit databases includes: Obtaining the credit resource usage of the user to be detected corresponding to each of the credit databases; According to the usage of each of the credit resources, each of the first target credit databases corresponding to the user to be detected is selected from each of the credit databases.

4. The transaction behavior detection method according to claim 1, characterized in that: The step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result includes: Obtaining access rules corresponding to target credit data in the second target credit database, and executing the access rules to obtain access authority information corresponding to the user to be detected; According to the access permission information, a transaction behavior detection is performed on the user to be detected to obtain a transaction behavior detection result.

5. The transaction behavior detection method according to claim 1, characterized in that: The transaction behavior detection method further includes: Selecting a random credit database other than the first target credit database from each of the credit databases; Performing transaction behavior detection on the user to be detected based on the random credit data in the random credit database to obtain a random behavior detection result; If the random behavior detection result is that the transaction behavior is abnormal, the first target credit database is updated according to the random credit database.

6. A transaction behavior detection device, characterized in that: The transaction behavior detection device includes: an acquisition module, configured to acquire at least one credit database corresponding to the user to be detected, and select at least one first target credit database corresponding to the user to be detected from each of the credit databases; a selection module, configured to select a second target credit database from each of the first target credit databases; a detection module, configured to perform transaction behavior detection on the user to be detected based on the target credit data in the second target credit database, and obtain a transaction behavior detection result; Before the step of performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain the transaction behavior detection result, the detection module is further used to query the user to be detected in the second target credit database to obtain a target query result type, wherein the target query result type includes at least one of an existing data type, a non-existing data type, and a query exception type; if the target query result type includes the existing data type and / or the non-existing data type, it is determined that the query of the second target credit database is completed; if the target query result type includes the query exception type, it is determined that the query of the second target credit database is not completed, and database query exception information is output for operation and maintenance personnel to repair the second target credit database; After the step of determining that the second target credit database has not been queried if the target query result type includes the query exception type, and outputting database query exception information, the detection module is further configured to obtain an allowed waiting time corresponding to the user to be detected; if the second target credit database is repaired within the allowed waiting time, querying the user to be detected in the second target credit database to obtain target credit data, and executing the step of: performing transaction behavior detection on the user to be detected based on the target credit data in the second target credit database to obtain a transaction behavior detection result; if the second target credit database is not repaired within the allowed waiting time, removing the second target credit database from the first target credit database, and returning to the step of: selecting a second target credit database from each of the first target credit databases; a first determination module configured to determine that the user to be detected has no abnormal transaction behavior if the transaction behavior detection result indicates that the transaction behavior is normal and all first target credit databases have been selected; a return module, configured to return to the step of selecting a second target credit database from each of the first target credit databases if the transaction behavior detection result indicates that the transaction behavior is normal and the first target credit databases have not yet been selected; The second determination module is configured to determine that the user to be detected has abnormal transaction behavior if the transaction behavior detection result is that the transaction behavior is abnormal.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the transaction behavior detection method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program for implementing the transaction behavior detection method, and the program for implementing the transaction behavior detection method is executed by a processor to implement the steps of the transaction behavior detection method according to any one of claims 1 to 5.

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