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 there is an electric charge transaction, and the second rule is used for identifying whether there is an abnormal behavior; 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 of the invention, the behavior of money laundering through electricity fee payment 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] Criminals' money laundering methods are constantly evolving and emerging. They use normal production and living expenses such as electricity bill payments to conceal and hide illegal proceeds. Money laundering methods are becoming increasingly professional and covert. Therefore, an effective method to identify money laundering through electricity bill payments is urgently needed. Summary of the Invention

[0003] The present application provides a transaction evaluation method, device, electronic device, medium and product for accurately identifying money laundering through electricity bill payment.

[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, a rule matched by the historical transaction data in 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 electricity fee 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 of the matched rules, 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, 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 electricity fee 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 the present application analyze the user's historical transaction data from two dimensions, based on a preset rule set, from whether the user has paid the electricity bill on behalf of the user 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 electricity bill payment. 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] Criminals' money laundering methods are constantly changing and emerging. They use normal production and living expenses such as paying electricity bills to cover up and conceal the proceeds of illegal crimes. The money laundering methods are becoming more professional and covert.

[0024] The specific methods of money laundering through electricity bill payment are as follows: criminals recruit payment agents with high commissions; payment agents use discounts lower than the market policy prices on large Internet platforms, WeChat groups, etc. to attract payers of normal production and life to pay their electricity bills through them; payment agents transfer the electricity bill funds collected from payers into a collection account controlled by criminals, and send the payers' payment information to criminals; based on the payers' payment information provided by the payment agents, criminals use funds illegally obtained from telecommunications fraud, online gambling, etc. to pay bills to power companies in other places on behalf of the payers; criminals further transfer the payment funds obtained from the payment agents from the collection accounts controlled by them to downstream accounts controlled by them to complete the fund laundering.

[0025] Criminals attract customers with discounted electricity bill payment advertisements, effectively laundering illegally obtained funds through this method, disrupting the normal operation of the market economy and trapping actual payers in money laundering without their knowledge. The covert nature of this money laundering method makes it more difficult for financial institutions to detect unusual transactions. Therefore, an effective method for identifying money laundering through electricity bill payment is urgently needed.

[0026] 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.

[0027] 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.

[0028] 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:

[0029] S101. Obtain historical transaction data of a user and, based on a pre-defined rule set, determine a rule that 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 electricity fee transaction, and the second rule being used to identify whether there is abnormal behavior;

[0030] 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.

[0031] In a specific implementation, a rule set can be pre-set, and the multiple rules in the rule set are divided into two categories: the first rule and the second rule. The first rule is used to identify whether the user has electricity bill 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 electricity bill payment through the two dimensions of the first rule and the second rule, thereby realizing feature indexing and indicator modeling. When evaluating a user's transaction, 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 hit rules and / or the assignment of each hit rule, the transaction evaluation result is output as whether money laundering behavior exists or the transaction is normal, thereby accurately identifying whether the user has laundered money through electricity bill payment and issuing a timely warning.

[0032] The transaction evaluation method provided in this embodiment analyzes the user's historical transaction data from two dimensions, based on a preset rule set, from whether the user has paid the electricity bill on behalf of others and whether the user has engaged in abnormal transaction behavior. Based on the number of rules hit by the historical transaction data in the rule set and / or the value assigned to each hit rule, the user's transaction evaluation result is obtained as whether money laundering behavior has occurred or the transaction is normal. This method can accurately identify whether the user has laundered money through electricity bill payment.

[0033] In one possible implementation method, the first rule includes:

[0034] The user's transaction partners are involved in the electricity sales industry;

[0035] And / or, the user's transaction remarks relate to payment of electricity bills.

[0036] In a specific implementation, it is possible to accurately determine whether the user has ever made an electricity bill payment by judging the user's transaction object and whether the user's transaction remarks involve electricity bill payment.

[0037] Exemplarily, the user's transaction object involving electricity bill payment may include, within the ninth time, the name of the user's transaction object includes electricity sales related fields, including but not limited to power grid, power supply, electricity, etc.

[0038] Furthermore, in order to avoid identifying the user's normal electricity bill payment behavior as money laundering behavior, the user's transaction object involving electricity bill payment can specifically include: within the ninth time, the name of the user's transaction object involves electricity bill payment, and the number of transactions between the user and the transaction object is not less than the ninth number.

[0039] Exemplarily, the user's transaction remarks concerning electricity bill payment may include that within the tenth time period, the transaction summary / transaction remarks / source and purpose of funds in the user's transaction flow sheet include at least one of the relevant fields such as electricity bill, payment on behalf of, payment on behalf of, recharge, charging fee, payment, and payment of electricity bill.

[0040] In a possible implementation method, the second rule includes:

[0041] Funds are transferred in centrally and out dispersedly, or transferred in dispersedly and out centrally;

[0042] and / or, rapid inflow and outflow of funds on the same day;

[0043] and / or, the account is activated with test transactions;

[0044] and / or, abnormal cross-regional account transactions;

[0045] and / or, there is a multiple relationship between the amounts of multiple transactions;

[0046] And / or, multiple users have the same contact number.

[0047] In specific implementation, through the analysis of money laundering cases, we can know that: (1) Concentrated transfer of funds and dispersed transfer out, or dispersed transfer of funds and concentrated transfer out, or fast in and fast out, are common characteristics of money laundering. (2) Criminals will make several small trial transactions before the account is used in large quantities. The main purpose is to test whether the account can be used normally, so as to ensure the timeliness of the subsequent transfer of large amounts of funds. (3) Since electricity bill payment involves all parts of the country, the transaction objects often involve multiple regions. (4) In money laundering, there are often multiple transactions with the same amount or multiple transactions with integer multiples of the amount. (5) The main reason is that criminals usually ask depositors to leave new phone numbers or non-personal phone numbers, or leave some business addresses that do not match the actual ones, in order to make it easier to use the account after acquisition, so as to increase the difficulty of the financial institution's on-site investigation and thus conceal themselves. Therefore, we can analyze whether the user has abnormal transaction behavior from the above dimensions.

[0048] Exemplarily, funds are transferred in in a concentrated manner and transferred out in a dispersed manner, or transferred in in a dispersed manner and transferred out in a concentrated manner, including: within the first time, the ratio of the user's cumulative credit transaction amount to the cumulative debit transaction amount is not less than a first ratio, or the ratio of the user's cumulative debit transaction amount to the cumulative credit transaction amount is not less than the first ratio, and the ratio of the user's cumulative credit transaction amount to the cumulative debit transaction amount minus one is not greater than a first threshold.

[0049] Exemplarily, the fast inflow and outflow of funds on the same day includes that the ratio of the user's cumulative credit transaction amount to the cumulative debit transaction amount on the same day minus one is not greater than the second threshold, and the unilateral cumulative amount is not less than the second amount.

[0050] Exemplarily, when an account is activated, test transactions are performed, including: a single transaction by the user on that day with an amount not less than a third amount, and the number of debit transactions with an amount not greater than the third amount before the single transaction is not less than a third amount.

[0051] Exemplarily, the abnormal cross-regional account transactions include: within a fourth time period, the number of the user's non-local transaction objects is not less than a fourth number, and the cumulative transaction amount is not less than a fourth amount.

[0052] Exemplarily, there is a multiple relationship between the amounts of multiple transactions, including: within the fifth time period, the user's cumulative number of transactions is not less than a first threshold, and the number of occurrences of the same transaction amount is not less than a second threshold; and / or, the number of transactions with transaction amounts that are integer multiples of the high-frequency transaction amount is not less than a third threshold, and the high-frequency transaction amount is the fifth transaction amount with the highest frequency among the user's transaction amounts.

[0053] In some embodiments, different user types correspond to different rule sets, and the user types include corporate customers or private customers.

[0054] In practice, user types can be categorized as corporate and personal. Corporate customers typically refer to legal entities such as enterprises, companies, and institutions, while personal customers refer to 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 identify the rules that match the rule set. This allows accurate identification of money laundering activities involving electricity bill payments.

[0055] In a possible implementation, when the user type is a corporate customer, the first rule further includes:

[0056] The user's industry type is not electricity sales industry.

[0057] In a specific implementation, when the industry type of a corporate customer is the electricity sales industry, the user's electricity bill-related transactions fall within the normal business scope, so only the electricity bill transactions of users in non-electricity sales industries need to be identified.

[0058] Illustratively, the user's industry type is not the electricity sales industry and may include that the user's business scope does not include fields such as electricity sales, electricity sales, power supply, power generation, power transmission, power supply, power distribution, and power supply (distribution).

[0059] In a possible implementation, when the user type is a corporate customer, the second rule further includes:

[0060] Unusual transactions with individual customers;

[0061] and / or the user's legal representative, beneficial owner, agent, or shareholder holds multiple positions;

[0062] and / or, the transaction size is inconsistent with the registered capital;

[0063] When the user type is a private customer, the second rule also includes:

[0064] The registered addresses or actual residential addresses of multiple users are similar or the same.

[0065] 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.

[0066] 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, 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 / amounts transferred from the user to the individual customer to the cumulative number of transactions / amounts on the user's debit side, or the ratio of the cumulative number of transactions / amounts transferred from the individual customer to the user to the cumulative number of transactions / amounts on the user's credit side, is not less than the sixth ratio;

[0067] 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.

[0068] Exemplarily, the registered addresses or actual residence addresses of multiple users are similar or identical, including: within the eleventh time period, the number of private customers whose registered addresses or actual residence addresses are the same or similar to the registered addresses or actual residence addresses of the users is not less than the eleventh number.

[0069] If the length of the household registration address or actual residential address, excluding the province, city, and district, is at least 9 bytes, a warning will be issued if a private customer with the same household registration address or actual residential address is found, and the household registration address or actual residential address needs to be cross-verified. If the length of the household registration address or actual residential address, excluding the province, city, and district, is at least 9 bytes, a warning will be issued if a private customer with a similar household registration address or actual residential address is found, and the household registration address or actual residential address needs to be cross-verified.

[0070] 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:

[0071] 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.

[0072] 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 electricity bill payment is high, the transaction evaluation result is output as the existence of money laundering, and an early warning is issued; or, each rule in the rule set can be assigned a value, and the size of the assigned value of each rule is positively correlated with the importance of the rule. When 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 electricity bill payment 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 electricity bill payment.

[0073] The transaction evaluation method provided in this embodiment analyzes the user's historical transaction data from two dimensions, based on a preset rule set, from whether the user has paid the electricity bill on behalf of others and whether the user has engaged in abnormal transaction behavior. Based on the number of rules hit by the historical transaction data in the rule set and / or the value assigned to each hit rule, the user's transaction evaluation result is obtained as whether money laundering behavior has occurred or the transaction is normal. This method can accurately identify whether the user has laundered money through electricity bill payment.

[0074] 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 method includes:

[0075] 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 there is an electricity transaction, and the second rule being configured to identify whether there is abnormal behavior;

[0076] 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.

[0077] 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.

[0078] 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:

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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, a rule matched by the historical transaction data in 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 electricity fee 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's transaction partners are involved in the electricity sales industry; And / or, the user's transaction remarks relate to payment of electricity bills.

3. The method according to claim 2, characterized in that The second rule includes: Funds are transferred in centrally and out dispersedly, or transferred in dispersedly and out centrally; and / or, rapid inflow and outflow of funds on the same day; and / or, the account is activated with test transactions; and / or, abnormal cross-regional account transactions; and / or, there is a multiple relationship between the amounts of multiple transactions; And / or, multiple users have the same contact number.

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 first rule further includes: The user's industry type is not the electricity sales 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 legal representative, beneficial owner, agent, or shareholder of the user holds overlapping positions; and / or, the transaction size is inconsistent with the registered capital; When the user type is a private customer, the second rule further includes: The registered addresses or actual residential addresses of multiple users are similar or the same.

7. The method according to claim 3, characterized in that The centralized transfer-in and decentralized transfer-out or decentralized transfer-in and centralized transfer-out includes: within a first period of time, the ratio of the user's cumulative credit transaction amount to the cumulative debit transaction amount is not less than a first ratio, or the ratio of the user's cumulative debit transaction amount to the cumulative credit transaction amount is not less than the first ratio, and the ratio of the user's cumulative credit transaction amount to the cumulative debit transaction amount minus one is not greater than a first threshold; The said fast inflow and outflow of funds on the same day includes that the ratio of the user's cumulative credit transaction amount to the cumulative debit transaction amount on the same day minus one is not greater than the second threshold, and the unilateral cumulative amount is not less than the second amount; The account activation is accompanied by test transactions, including: a single transaction by the user on that day with an amount not less than a third amount, and the number of debit transactions with an amount not greater than the third amount prior to the single transaction being not less than a third amount; The cross-regional account transaction anomaly includes: within a fourth time period, the number of non-local transaction partners of the user is not less than a fourth number, and the cumulative transaction amount is not less than a fourth amount; There is a multiple relationship between the amounts of the multiple transactions, including: within a fifth time period, the cumulative number of transactions of the user is not less than a first threshold, and the number of occurrences of the same transaction amount is not less than a second threshold; and / or the number of transactions with an amount that is an integer multiple of the high-frequency transaction amount is not less than a third threshold, and the high-frequency transaction amount is the fifth number of transaction amounts with the highest frequency among the transaction amounts of the user.

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, 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.

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 matched by the historical transaction data within the rule set; wherein the rule set includes a first rule for identifying whether an electricity transaction exists and a second rule for identifying whether abnormal behavior exists; 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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