Transaction evaluation method and device, electronic equipment, medium and product

Abstract

The embodiment of the invention provides a transaction evaluation method and device, electronic equipment, a medium and a product, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring historical transaction data of a user, and determining a rule hit by the historical transaction data in a rule set according to the preset rule set; wherein the rule set comprises a first rule and a second rule, the first rule is used for identifying whether artwork transactions exist or not, and the second rule is used for identifying whether abnormal behaviors exist or not; and determining a transaction evaluation result of the user according to the number of the hit rules and / or the assignment of each hit rule. According to the method, the artwork money laundering behavior can be accurately identified.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a transaction evaluation method, device, electronic device, medium, and product. Background Art

[0002] As the scale of my country's art market continues to expand, money laundering activities that use art to conceal criminal proceeds and their benefits are becoming increasingly common. Criminals engage in money laundering through the sale, investment, and speculation of art, posing a significant threat to social and economic order and financial stability. Therefore, an effective method for identifying money laundering through art transactions is urgently needed. Summary of the Invention

[0003] This application provides a transaction evaluation method, device, electronic device, medium and product for accurately identifying money laundering activities involving artworks.

[0004] In a first aspect, the present application provides a transaction evaluation method, comprising:

[0005] Obtaining historical transaction data of a user, and determining, based on a pre-defined rule set, which rule in the rule set the historical transaction data matches; wherein the rule set includes a first rule and a second rule, the first rule being used to identify whether there is an artwork transaction, and the second rule being used to identify whether there is abnormal behavior;

[0006] According to the number of the matched rules and / or the value assigned to each matched rule, it is determined that the transaction evaluation result of the user is that money laundering behavior occurs or the transaction is normal.

[0007] In a second aspect, the present application provides a risk prediction device, comprising:

[0008] An acquisition module is configured to acquire historical transaction data of a user and, based on a pre-defined rule set, determine which rule in the rule set the historical transaction data matches; wherein the rule set includes a first rule and a second rule, the first rule being configured to identify whether there is an artwork transaction and the second rule being configured to identify whether there is abnormal behavior;

[0009] The determination module is used to determine, based on the number of the hit rules and / or the value assigned to each of the hit rules, whether the transaction evaluation result of the user involves money laundering or is normal.

[0010] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0011] The memory stores computer-executable instructions;

[0012] The processor executes the computer-executable instructions stored in the memory to implement the above method.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to implement the method as described above when executed by a processor.

[0014] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which implements the method described above when executed by a processor.

[0015] The transaction evaluation method, device, electronic device, medium and product provided in this application analyze the user's historical transaction data from two dimensions, whether the user has engaged in art transactions and whether the user has abnormal transaction behavior, based on a preset rule set. According to the number of rules hit by the historical transaction data in the rule set and / or the assignment of each hit rule, the user's transaction evaluation result is obtained as whether there is money laundering behavior or the transaction is normal, which can accurately identify whether the user has laundered money through art transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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.

[0017] Figure 1 A flowchart of a transaction evaluation method provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the structure of a transaction evaluation device provided in an embodiment of the present application;

[0019] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0020] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0021] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0022] It should be noted that the transaction evaluation method, device, electronic device, medium and product provided in this application can be used in the field of artificial intelligence, and can also be used in any field other than artificial intelligence. The application field of the transaction evaluation method, device, electronic device, medium and product in this application is not limited.

[0023] As the scale of my country's art market continues to expand, money laundering activities that use art to conceal criminal proceeds and their benefits are becoming increasingly common. Criminals engage in money laundering through the sale, investment, and speculation of art, posing a significant threat to social and economic order and financial stability. Therefore, an effective method for identifying money laundering through art transactions is urgently needed.

[0024] The transaction evaluation method, device, electronic device, medium and product provided in this application are intended to solve the above technical problems of the prior art.

[0025] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0026] Figure 1 This is a flow chart of the transaction evaluation method provided in the embodiment of this application. Figure 1 As shown, the method includes:

[0027] S101. Obtain historical transaction data of a user, and determine, based on a pre-defined rule set, which rule the historical transaction data matches within the rule set; wherein the rule set includes a first rule and a second rule, the first rule being used to identify whether there is an artwork transaction, and the second rule being used to identify whether there is abnormal behavior;

[0028] S102: Determine, based on the number of matched rules and / or the value assigned to each matched rule, whether the transaction evaluation result of the user involves money laundering or is normal.

[0029] In a specific implementation, a rule set can be pre-set. The multiple rules in the rule set are divided into two categories: first rule and second rule. The first rule is used to identify whether the user has engaged in art transactions, and the second rule is used to identify whether the user has abnormal transaction behavior. Therefore, the rule set can identify whether the user has laundered money through art transactions through the two dimensions of the first rule and the second rule, thereby realizing feature indexing and indicator modeling. When evaluating a user's transactions, the user's historical transaction data is obtained, and it is determined whether the historical transaction data hits each rule in the rule set. Based on the number of rules hit and / or the value assigned to each rule hit, the transaction evaluation result is output as money laundering behavior or normal transaction. This can accurately identify whether the user has laundered money through art transactions and issue a timely warning.

[0030] The transaction evaluation method provided in this embodiment is based on a preset rule set, and analyzes the user's historical transaction data from two dimensions: whether the user has engaged in art transactions and whether the user has abnormal transaction behavior. According to the number of rules hit by the historical transaction data in the rule set and / or the assignment of each hit rule, the user's transaction evaluation result is obtained as whether there is money laundering behavior or the transaction is normal, which can accurately identify whether the user has laundered money through art transactions.

[0031] In one possible implementation method, the first rule includes:

[0032] Users are involved in the art industry;

[0033] Also, the user's transaction objects involve the art industry;

[0034] Also, the user's transaction remarks involve the art industry.

[0035] In a specific implementation, it is possible to accurately determine whether a user has ever conducted art transactions by judging whether the user himself, the user's transaction object, and the user's transaction remarks involve the art industry.

[0036] Exemplarily, the user's transaction object involving the art industry may include, within the ninth time, the name of the user's transaction object includes art-related fields, including but not limited to auction, art, gallery, antique, appraisal, e-commerce, information technology, e-commerce, digital, exhibition, culture, collection, fine arts, etc.

[0037] Furthermore, in order to avoid identifying a user's normal art transaction behavior as money laundering behavior, the user's transaction remarks involving the art industry may specifically include that within the ninth time, the name of the user's transaction object involves the art industry, and the single transaction amount between the user and the transaction object is not less than the preset amount.

[0038] Exemplarily, the user's transaction remarks involving the art industry may include, within the tenth time period, the transaction summary / transaction remarks / source and purpose of funds in the user's transaction flow sheet include: appraisal, report fee, NFT (non-fungible token), Non-Fungible-Token, famous paintings, figures, digital collections, auctions, collections, art, antiques, fine arts, cultural relics, sculptures, paintings, treasure appraisal, micro-auctions, auction items, calligraphy and paintings, sketches, tapestries, curtains, sculptures, statues, ceramics, porcelain, enamel, photography, photos, inscriptions, coins, seals and other related fields. At least one item.

[0039] In a possible implementation method, the second rule includes:

[0040] Abnormal cash transaction amounts;

[0041] and / or, an unusual number of large transactions;

[0042] and / or, abnormalities in electronic banking transaction channels;

[0043] and / or, there is a transitional nature of funds in the account;

[0044] And / or, there is an association relationship between the transaction object and the user.

[0045] In specific implementation, through analyzing money laundering cases, we can learn that: (1) Cash can conceal the source of funds and is a common method of money laundering. (2) The transaction amount of money laundering activities is usually large. (3) Electronic banking services are convenient and fast, with few restrictions on time and location. Customer information is hidden and can be operated remotely, making it a common method of online money laundering. (4) Using accounts to transfer funds can allow illegal funds to enter the financial system and is a common means of online money laundering. (5) Using related accounts for cross-covering can conceal illegal activities and is a common method of money laundering. Therefore, we can analyze whether users have abnormal transaction behavior based on the above five aspects.

[0046] Exemplarily, the abnormal cash transaction amount includes: within a first period of time, the cumulative amount of cash deposited and withdrawn by the user is not less than a first amount.

[0047] Exemplarily, the abnormal number of large-value transactions includes, within the second time period, the cumulative number of transactions in which the user's single transaction amount is greater than or equal to the second amount is greater than or equal to the second number.

[0048] Exemplarily, the electronic banking transaction channel abnormality includes: within a third time period, the amount of debit transactions conducted by the user through the electronic banking channel is greater than a third amount, and the ratio of the number of debit transactions in the electronic banking channel to the total number of debit transactions is not less than a third ratio.

[0049] Exemplarily, the account funds are transitional, including: within a fourth time period, the ratio of the user's cumulative credit transaction amount to the cumulative debit transaction amount minus one is not greater than a fourth threshold.

[0050] Exemplarily, the existence of an association relationship between the transaction object and the user includes: within the fifth time, the number of associated transaction objects that are associated persons or associated enterprises among the user's transaction objects reaches a preset number, and the cumulative transaction amount with the associated transaction objects is not less than the fifth amount.

[0051] In some embodiments, different user types correspond to different rule sets, and the user types include corporate customers or private customers.

[0052] In practice, user types can be categorized as corporate and personal. Corporate customers typically refer to legal entities such as businesses, companies, and institutions, while personal customers are individuals. Since different types of users have different business characteristics, corresponding rule sets can be set for corporate and personal customers. When evaluating user transactions, the user type is first matched to the corresponding rule set. The user's historical transaction data is then used to determine which rules match those rules, enabling accurate identification of art money laundering.

[0053] In one possible implementation, when the user type is a corporate customer, the user is involved in the art industry, specifically including:

[0054] The user's name, business scope and / or industry type involves the art industry;

[0055] When the user type is a private customer, the user is involved in the art industry, specifically including:

[0056] The user's occupation type involves the art industry.

[0057] In specific implementation, for corporate customers, it can be determined that the user himself is involved in the art industry when the user's name, business scope and / or industry type involves the art industry; for private customers, it can be determined that the user himself is involved in the art industry when the user's occupation type is in an art-related industry.

[0058] Exemplarily, the user's name related to the art industry may include that the user's name includes at least one of the fields of auction, art, gallery, antique, appraisal, e-commerce, information technology, e-commerce, digital, exhibition, culture, collection, fine arts, etc.

[0059] Exemplarily, the user's business scope involving the art industry may include at least one of auctions, art, galleries, antiques, appraisals, e-commerce, information technology, e-commerce, digital, exhibitions, culture, collections, and fine arts.

[0060] For example, the user's industry type related to the art industry may include that the user's industry includes at least one of "culture and art industry", or "culture, sports, entertainment activities and economic agency services".

[0061] Exemplarily, the user's occupation type related to the art industry may include the user's occupation type being at least one of "literature, art, and sports professionals", "news publishing, and cultural professionals", "cultural, sports, and entertainment service personnel", "cultural, educational, arts and crafts, sports, and entertainment products production personnel", etc.

[0062] In a possible implementation, when the user type is a corporate customer, the second rule further includes:

[0063] Unusual transactions with individual customers;

[0064] and / or, the transaction size is inconsistent with the registered capital;

[0065] When the user type is a private customer, the second rule also includes:

[0066] Abnormal transaction frequency.

[0067] In specific implementation, frequent fund transfers between corporate and personal customers, obvious discrepancies between corporate customer transaction amounts and registered capital, and a large number of concentrated transactions among personal customers can all indicate suspicious transactions.

[0068] Exemplarily, abnormal transactions with individual customers include: the number of transactions between the user and the individual customer is not less than the sixth number, and the cumulative transaction amount between the user and the individual customer is not less than the sixth amount, and the ratio of the cumulative number of transactions / amount transferred from the user to the individual customer to the cumulative number of transactions / amount of the user's debit transactions or the ratio of the cumulative number of transactions / amount transferred from the individual customer to the user to the cumulative number of transactions / amount of the user's credit transactions is not less than the sixth ratio.

[0069] Exemplarily, the transaction scale is inconsistent with the registered capital, including: within the seventh time, the ratio of the user's cumulative transaction amount to the registered capital is not less than the seventh ratio; or, the user has no registered capital and the cumulative transaction amount is not less than the seventh amount.

[0070] Exemplarily, the abnormal transaction frequency includes: within an eighth time period, the number of transactions of the user is not less than an eighth amount, and the cumulative transaction amount is not less than an eighth amount.

[0071] It is understandable that the parameters such as time, amount, quantity, etc. involved in each rule in the rule set can be selected according to actual production needs and are not limited here.

[0072] For example, real transaction data and real transaction evaluation results can be used to run batches and adjust the parameters of each rule in the rule set until the accuracy of the transaction evaluation results output by the rule set relative to the real transaction evaluation results meets the preset requirements.

[0073] In one possible implementation, determining the user's transaction evaluation result based on the number of matched rules and / or the value assigned to each matched rule includes:

[0074] If the number of matched rules is not less than the first threshold, and / or the cumulative value of the matched rules is not less than the second threshold, the transaction evaluation result is determined to be money laundering; otherwise, the transaction evaluation result is determined to be a normal transaction.

[0075] In a specific implementation, when the number of hit rules is not less than a first threshold, it can be determined that the risk of the user laundering money through artworks is high, the transaction evaluation result is output as the existence of money laundering, and an early warning is issued; or, for the amplitudes of each rule in the rule set, the amplitude of each rule is positively correlated with the importance of the rule, and the cumulative score of the hit rules is not less than a second threshold, it can be determined that the risk of the user laundering money through artworks is high, the transaction evaluation result is output as the existence of money laundering, and an early warning is issued; or, when the number of hit rules is not less than the first threshold and the cumulative score of the hit rules is not less than the second threshold, the transaction evaluation result is output as the existence of money laundering, and an early warning is issued, thereby accurately identifying the behavior of money laundering through artworks.

[0076] The transaction evaluation method provided in this embodiment is based on a preset rule set, and analyzes the user's historical transaction data from two dimensions: whether the user has engaged in art transactions and whether the user has abnormal transaction behavior. According to the number of rules hit by the historical transaction data in the rule set and / or the assignment of each hit rule, the user's transaction evaluation result is obtained as whether there is money laundering behavior or the transaction is normal, which can accurately identify whether the user has laundered money through art transactions.

[0077] Figure 2 This is a schematic diagram of the structure of the transaction evaluation device provided in the embodiment of the present application. Figure 2 As shown, the device includes:

[0078] An acquisition module 21 is configured to acquire historical transaction data of a user and, based on a pre-defined rule set, determine which rule in the rule set the historical transaction data matches; wherein the rule set includes a first rule and a second rule, the first rule being configured to identify whether an artwork transaction has occurred, and the second rule being configured to identify whether abnormal behavior has occurred;

[0079] The determination module 22 is configured to determine, based on the number of the matched rules and / or the value assigned to each matched rule, whether the transaction evaluation result of the user involves money laundering or is normal.

[0080] It should be noted that the risk prediction device is used to execute the transaction evaluation method as described above. Its specific implementation method can be found in the method embodiment provided in the embodiments of this application and will not be repeated here.

[0081] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 3 As shown, the electronic device includes:

[0082] The electronic device includes a processor 291 and a memory 292. It may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via bus 294. Communication interface 293 can be used for information transmission. The processor 291 can invoke logic instructions in memory 292 to execute the methods of the above embodiments.

[0083] In addition, the logic instructions in the memory 292 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0084] Memory 292, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of the present application. Processor 291 executes the software programs, instructions, and modules stored in memory 292 to perform functional applications and data processing, thereby implementing the methods in the above-mentioned method embodiments.

[0085] Memory 292 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal device. Memory 292 may also include high-speed random access memory and non-volatile memory.

[0086] An embodiment of the present application provides a non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the method described in the above embodiment.

[0087] An embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the method provided in any of the above embodiments of the present application is implemented.

[0088] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0089] It should be further noted that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0090] It should be understood that the above-described device embodiments are merely illustrative, and the device of the present application may also be implemented in other ways. For example, the division of units / modules in the above-described embodiments is merely a logical functional division, and actual implementations may employ other division methods. For example, multiple units, modules, or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0091] In addition, unless otherwise specified, the functional units / modules in the various embodiments of the present application may be integrated into a single unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The aforementioned integrated units / modules may be implemented in the form of hardware or software program modules.

[0092] If an integrated unit / module is implemented in hardware, the hardware may be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor may be any appropriate hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC. Unless otherwise specified, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0093] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk, or optical disk, etc., various media that can store program code.

[0094] In the above embodiments, the description of each embodiment has its own emphasis. For parts not described in detail in a particular embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined in any way. To keep the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0096] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A transaction evaluation method, characterized in that: include: Obtaining historical transaction data of a user, and determining, based on a pre-defined rule set, which rule in the rule set the historical transaction data matches; wherein the rule set includes a first rule and a second rule, the first rule being used to identify whether there is an artwork transaction, and the second rule being used to identify whether there is abnormal behavior; According to the number of the matched rules and / or the value assigned to each matched rule, it is determined that the transaction evaluation result of the user is that money laundering behavior occurs or the transaction is normal.

2. The method according to claim 1, characterized in that The first rule includes: The user is involved in the art industry; and / or, the user's transaction partner is involved in the art industry; And / or, the user's transaction remarks relate to the art industry.

3. The method according to claim 2, characterized in that The second rule includes: Abnormal cash transaction amounts; and / or, an unusual number of large transactions; and / or, abnormalities in electronic banking transaction channels; and / or, there is a transitional nature of funds in the account; And / or, the transaction object has an association relationship with the user.

4. The method according to claim 3, characterized in that Different user types correspond to different rule sets, and the user types include corporate customers and private customers.

5. The method according to claim 4, characterized in that When the user type is a corporate customer, the user is involved in the art industry, specifically including: The user's name, business scope and / or industry type involves the art industry; When the user type is a private customer, the user is involved in the art industry, specifically including: The user's occupation type is related to the art industry.

6. The method according to claim 4, characterized in that When the user type is a corporate customer, the second rule further includes: Unusual transactions with individual customers; and / or, the transaction size is inconsistent with the registered capital; When the user type is a private customer, the second rule further includes: Abnormal transaction frequency.

7. The method according to claim 3, characterized in that The cash transaction amount is abnormal, including: within a first period of time, the cumulative amount of cash deposited and withdrawn by the user is not less than a first amount; The abnormal number of large-value transactions includes that, within the second time period, the cumulative number of transactions of the user with a single transaction amount greater than or equal to the second amount is greater than or equal to the second number; The electronic banking transaction channel abnormality includes: within a third period of time, the amount of debit transactions conducted by the user through the electronic banking channel is greater than a third amount, and the ratio of the number of debit transactions through the electronic banking channel to the total number of debit transactions is not less than a third ratio; The account funds are transitional, including: within a fourth time period, the ratio of the user's cumulative credit transaction amount to the cumulative debit transaction amount minus one is not greater than a fourth threshold; The transaction object has an associated relationship with the user, including: within a fifth time period, the number of associated transaction objects that are associated persons or associated enterprises among the user's transaction objects reaches a preset number, and the cumulative transaction amount with the associated transaction objects is not less than a fifth amount.

8. The method according to claim 6, characterized in that The abnormal transactions with individual customers include: the number of transactions between the user and individual customers is not less than a sixth number, the cumulative transaction amount between the user and individual customers is not less than a sixth amount, and the ratio of the cumulative number / amount of transactions transferred from the user to individual customers to the cumulative number / amount of debit transactions of the user, or the ratio of the cumulative number / amount of transactions transferred from individual customers to the user to the cumulative number / amount of credit transactions of the user is not less than a sixth ratio; The transaction scale is inconsistent with the registered capital, including: within the seventh time period, the ratio of the user's cumulative transaction amount to the registered capital is not less than the seventh ratio; or the user has no registered capital and the cumulative transaction amount is not less than the seventh amount; The abnormal transaction frequency includes: within the eighth time period, the number of transactions of the user is not less than the eighth amount, and the cumulative transaction amount is not less than the eighth amount.

9. The method according to any one of claims 1 to 8, characterized in that Determining the transaction evaluation result of the user according to the number of the matched rules and / or the value assigned to each of the matched rules includes: If the number of the hit rules is not less than a first threshold, and / or the cumulative assignment of the hit rules is not less than a second threshold, the transaction evaluation result is determined to be money laundering; otherwise, the transaction evaluation result is determined to be normal.

10. A transaction evaluation device, characterized in that: include: an acquisition module for acquiring historical transaction data of a user and, based on a pre-defined rule set, determining a rule within the rule set that the historical transaction data matches; wherein the rule set includes a first rule and a second rule, the first rule being used to identify whether there is an artwork transaction, and the second rule being used to identify whether there is abnormal behavior; The determination module determines, based on the number of the matched rules and / or the value assigned to each matched rule, whether the transaction evaluation result of the user involves money laundering or is normal.

11. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 9 when executed by a processor.

13. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 9 when being executed by a processor.

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