Transaction risk monitoring method and device for direct payment claim settlement service, electronic equipment, storage medium and program product

Abstract

The invention provides a transaction risk monitoring method and device for a direct payment claim settlement business, electronic equipment and a storage medium, and the method comprises the steps: obtaining target business data which comprises the attribute data, transaction consumption data, action track data and operation behavior data of a risk recognition main body; according to the target business data, calculating at least one risk field corresponding to the risk identification subject, the risk field comprising a risk index corresponding to the risk identification subject; for each risk field, judging whether the risk identification subject hits a preset risk control rule corresponding to the risk field according to the risk field, and obtaining a corresponding hit judgment result; and according to the hit judgment result, calculating whether the risk identification subject hits a risk control strategy, the risk control strategy comprising at least one preset risk control rule. According to the invention, the influence of manual intervention and subjective judgment can be reduced, the individual and cluster risks are monitored in time, and the reliability and efficiency of direct payment claim settlement transaction risk monitoring are improved.

Description

Technical Field

[0001] The present application relates to the field of risk management, and more specifically, to a transaction risk monitoring method, device, electronic device, storage medium and program product for direct payment claims business. Background Art

[0002] Enterprises recharge users' supplementary medical funds into their cards, and users swipe their cards to make purchases, which involves the purchase of personal medical care and medicines. There are transaction risks and claims risks such as theft, illegal cash withdrawal, and unreasonable swiping of cards other than for medical treatment. Therefore, it is particularly important to effectively monitor direct payment claim card transactions. Through in-depth analysis of massive transaction information, an effective monitoring system can help detect individual abnormal transactions and judge the occurrence of clustered abnormal transaction behaviors in groups, which can better protect customers' fund security and compliance management, and ensure the safe, continuous and stable development of the business.

[0003] However, in the direct payment claims business, direct payment claims for personal business have long relied on manual review and simple static rule verification, and there is no targeted systematic method to monitor risks on the policy side (corporate groups) and consumer institution side (stores). Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a transaction risk monitoring method, device, electronic device, storage medium and program product for direct payment claims business, so as to reduce the impact of human intervention and subjective judgment, timely monitor individual and cluster risks, and improve the reliability and efficiency of risk monitoring of direct payment claims business.

[0005] In a first aspect, the present application provides a transaction risk monitoring method for direct payment claims business, comprising: obtaining target business data, the target business data including attribute data, transaction consumption data, action trajectory data and operation behavior data of a risk identification subject; calculating at least one risk field corresponding to the risk identification subject based on the target business data, the risk field including a risk indicator corresponding to the risk identification subject; for each risk field, judging whether the risk identification subject hits a preset risk control rule corresponding to the risk field based on the risk field, and obtaining a corresponding hit judgment result; calculating whether the risk identification subject hits a risk control strategy based on the hit judgment result, wherein the risk control strategy includes at least one preset risk control rule.

[0006] In the above scheme, by comprehensively analyzing the target business data, including the attribute data of the risk identification subject, transaction consumption data, action trajectory data and operation behavior data, the potential risks of the risk identification subject can be fully identified. In addition, the automated risk field calculation and preset risk control rule matching can accurately determine whether the risk identification subject has triggered the risk control strategy, thereby achieving effective risk monitoring, reducing the impact of manual intervention and subjective judgment, and improving the reliability and efficiency of risk monitoring of direct payment claims business.

[0007] As an optional method, the risk field also includes the inherent attributes corresponding to the risk identification subject. In the above scheme, the risk indicators of the risk identification subject can be combined or not combined with their own inherent attributes to perform hit judgment of the preset risk control rules, reducing misjudgment and missed judgment, which is conducive to supporting the steady development of the business.

[0008] As an optional method, the risk control strategy also includes a weight corresponding to each preset risk control rule, and the calculation of whether the risk identification subject hits the risk control strategy based on the hit judgment result includes: calculating the product between each hit preset risk control rule and its corresponding weight, and calculating the sum of multiple products; determining whether the risk identification subject hits the risk control strategy based on the sum. In the above scheme, by introducing a weight mechanism to evaluate the contribution of each preset risk control rule in the overall risk control strategy, risk assessment is no longer a simple match of preset risk control rules, but weighted according to the importance of each rule, thereby optimizing the risk control strategy and improving the accuracy of risk assessment.

[0009] As an optional method, after calculating whether the risk identification subject hits the risk control strategy based on the hit judgment result, it also includes: outputting the basic information of the risk identification subject, risk subject indicators, risk analysis results of the risk identification subject and risk details of the risk analysis results; wherein the risk subject indicators are visual indicators generated based on the risk fields for administrators to use.

[0010] In the above scheme, by providing basic information of risk identification subjects, risk subject indicators, risk analysis results and risk details, users and decision makers can more clearly understand the basis and results of risk assessment and use data to support business decisions and risk management.

[0011] As an optional way, before obtaining the target business data, the method further includes: cleaning the underlying data from different sources to obtain the target business data. In the above scheme, cleaning the data can identify and correct errors or inconsistencies in the data set, thereby improving data quality, reducing operational risks caused by data errors or inconsistencies, and improving overall business process efficiency.

[0012] As an optional manner, the calculating, based on the target business data, at least one risk field corresponding to the risk identification subject includes: calculating at least one risk field corresponding to the risk identification subject within a time node. In the above scheme, by calculating the risk field within a specific time node, the risk assessment can be linked to the historical business situation, thereby improving the timeliness and relevance of the risk assessment and enhancing the dynamic risk monitoring capability.

[0013] In the second aspect, the present application provides a transaction risk monitoring device for direct payment claims business, including: an acquisition module, used to acquire target business data, the target business data including attribute data, transaction consumption data, action trajectory data and operation behavior data of the risk identification subject; a risk field calculation module, used to calculate at least one risk field corresponding to the risk identification subject according to the target business data, the risk field including the risk indicator corresponding to the risk identification subject; a judgment module, used to judge whether the risk identification subject hits the preset risk control rule corresponding to the risk field for each risk field according to the risk field, and obtain the corresponding hit judgment result; a risk control strategy calculation module, used to calculate whether the risk identification subject hits the risk control strategy according to the hit judgment result, wherein the risk control strategy includes at least one preset risk control rule.

[0014] In the above scheme, by comprehensively analyzing the target business data, including the attribute data of the risk identification subject, transaction consumption data, action trajectory data and operation behavior data, the potential risks of the risk identification subject can be fully identified. In addition, the automated risk field calculation and preset risk control rule matching can accurately determine whether the risk identification subject has triggered the risk control strategy, thereby achieving effective risk monitoring, reducing the impact of manual intervention and subjective judgment, and improving the reliability and efficiency of risk monitoring of direct payment claims business.

[0015] As an optional method, the risk control strategy also includes a weight corresponding to each preset risk control rule, and the risk control strategy calculation module is used to calculate the product between each hit preset risk control rule and its corresponding weight, and calculate the sum of multiple products; determine whether the risk identification subject hits the risk control strategy based on the sum.

[0016] As an optional method, the above-mentioned device also includes: an output module, which is used to output the basic information of the risk identification subject, risk subject indicators, risk analysis results of the risk identification subject and risk details of the risk analysis results; wherein, the risk subject indicators are visual indicators generated according to the risk fields for use by administrators.

[0017] As an optional method, the device also includes: a data cleaning module, which is used to clean the underlying data from different sources to obtain target business data.

[0018] As an optional manner, the risk field calculation module is specifically used to calculate at least one risk field corresponding to the risk identification subject within a time node.

[0019] In a third aspect, the present application provides an electronic device comprising: a processor, a memory and a bus, wherein the processor and the memory communicate with each other through the bus; the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method steps of the first aspect are executed.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method steps of the first aspect are executed.

[0021] In a fifth aspect, the present application provides a computer program product, comprising: a computer program, wherein the computer program executes the method steps of the first aspect when executed by a processor.

[0022] Other features and advantages of the present application will be described in the subsequent description, and in part will become apparent from the description, or will be understood by implementing the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A flowchart of a transaction risk monitoring method for direct payment claims business provided in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of the structure of a transaction risk monitoring device for direct payment claims business provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following embodiments of the technical solution of the present application will be described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0028] It should be noted that all technical and scientific terms used in this document have the same meanings as those generally understood by technicians in the technical field to which this application belongs; the terms used in this document are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0029] It can be understood that the transaction risk monitoring method for direct payment claims business provided in the embodiment of the present application can be applied to a terminal device (also referred to as an electronic device) or a server; the terminal device can specifically be a smart phone, a tablet computer, a computer, or a personal digital assistant (PDA), etc.; the server can specifically be an application server or a Web server.

[0030] To facilitate understanding of the technical solution provided in the embodiment of the present application, the application scenario of the transaction risk monitoring method for the direct payment claims business provided in the embodiment of the present application is introduced below by taking the server as the execution entity as an example.

[0031] Reference Figure 1 , Figure 1 A flowchart of a transaction risk monitoring method for direct payment claims business provided in an embodiment of the present application. The method comprises the following steps:

[0032] Step S10: Acquire target business data, which includes attribute data of the risk identification subject, transaction consumption data, action trajectory data, and operation behavior data.

[0033] Target business data is used to assess and monitor transaction risks. By analyzing this data, the system can identify possible risky behaviors, such as credit card fraud, illegal cash withdrawal, etc. Target business data includes attribute data of risk identification subjects, transaction consumption data, action trajectory data, and operation behavior data.

[0034] The risk identification subject refers to the individual or entity that may be involved in the risk in the direct payment claim business. For example, the risk identification subjects in the direct payment claim business transaction may include the user side, the policy side, and the consumer institution side. It should be noted that the user side refers to the individual who can conduct direct payment claim business transactions, the policy side refers to the cluster group associated with the same policy, and the consumer institution side refers to the transaction store that provides goods or services.

[0035] Taking the supplementary medical fund recharge business as an example, the supplementary medical fund can be recharged into the direct payment claim card of the corresponding user, and the user can make purchases by swiping the card; among them, in this direct payment claim business, the consumer institution end is the transaction store that provides medical services or medicines, the user end is the individual who uses the direct payment claim card to make purchases at these consumer institutions, and the policy end is the cluster group associated with the policy.

[0036] The attribute data of the risk identification subject may include user information, account information, card information, policy information, consumer institution information, etc. The transaction consumption data of the risk identification subject may include transaction log data, item details data, consumption type data, refund and cash payment module data, online shopping related data, etc. The operation behavior data of the risk identification subject may include card operation data, application (Application, APP) operation data, hardware device data used for transaction, etc.

[0037] As an implementation method, the target business data may be from various insurance companies.

[0038] Step S20: Calculate at least one risk field corresponding to a risk identification subject according to the target business data, wherein the risk field includes a risk indicator corresponding to the risk identification subject.

[0039] Risk indicators refer to a series of quantitative indicators used to assess the risks that risk identification entities may face in direct claims business, which are obtained by pre-calculating the target business data of each risk identification entity. For example, risk fields can be transaction amount, number of transactions, maximum number of consecutive transaction days, number of transactions during abnormal time, number of transactions in different places, etc. In addition, transactions can be graded and different levels of transactions can be set according to transaction amounts. Then data integration is performed, such as the longest consecutive transaction days, number of times, maximum consecutive transaction amount, etc. The calculation of the above risk fields can be achieved through the logical execution of SQL statements.

[0040] Step S30: For each risk field, determine whether the risk identification subject hits the preset risk control rule corresponding to the risk field according to the risk field, and obtain a corresponding hit judgment result.

[0041] The preset risk control rules are a series of predefined standards or conditions used in the risk monitoring process to identify, evaluate and respond to potential risks. It should be noted that the embodiments of the present application do not specifically limit the specific implementation methods for determining the preset risk control rules, and those skilled in the art can make appropriate adjustments based on actual conditions. For example, the preset risk control rules can be determined based on statistical analysis of historical data and machine learning algorithm training; or, the preset risk control rules can be formulated based on the experience and business logic of those skilled in the art; or, the preset risk control rules can also be dynamically adjusted according to new data and market changes. For example, a preset risk control rule formulated based on the attribute data of the risk identification subject can be: determine whether the age of the cardholder is greater than 90 years old.

[0042] In addition, the embodiment of the present application does not specifically limit the number of preset risk control rules corresponding to each risk field, and those skilled in the art can also make appropriate adjustments according to actual conditions. For example, the risk field can correspond to one preset risk control rule; or, the risk field can correspond to two preset risk control rules, etc.

[0043] There are two outputs of the hit judgment result, namely, hit or miss. Optionally, a hit can be represented by output 1, and a miss can be represented by output 0.

[0044] Among them, if the risk identification subject hits a preset risk control rule, it means that the risk identification subject meets the preset risk control rule; correspondingly, if the risk identification subject does not hit a preset risk control rule, it means that the risk identification subject does not meet the preset risk control rule. For example: when the preset risk control rule determines whether the cardholder is older than 90 years old, the user age is greater than 90 years old, which means that the risk identification subject hits the preset risk control rule.

[0045] Step S40: Calculate whether the risk identification subject hits the risk control strategy based on the hit judgment result, wherein the risk control strategy includes at least one preset risk control rule.

[0046] A risk control strategy is a collection of at least one preset risk control rule in the risk monitoring process, which is used to capture risks, protect the financial security of enterprises and users, and ensure the compliance and stability of the business. In the risk monitoring process, hitting only a single preset risk control rule does not mean that the user is actually at risk, because the preset risk control rules may also offset each other. Therefore, risk control strategies are required to preset risk types according to actual scenarios and business needs, and to capture risks in different situations by configuring different risk control strategies.

[0047] As an implementation method, the configuration of the risk control strategy can be static; once the risk control strategy is determined in advance, the risk control strategy will not change. As another implementation method, the configuration of the risk control strategy can also be dynamic, and it is constantly optimized and adjusted based on new data, machine learning algorithms, market conditions and business needs.

[0048] Taking the cross-region transaction business as an example, the risk field may include the transaction location, transaction amount, transaction store, and card attributes. Therefore, the cross-region transaction risk control strategy may select relevant preset risk control rules involving the transaction location, transaction amount, transaction store, and card attributes to form a rule set. Among them, within a period of time, the above risk control strategy may include relevant preset risk control rules involving the transaction location and transaction amount; within the next period of time, the above risk control strategy may be updated to include relevant preset risk control rules involving the transaction location, transaction amount, and card attributes.

[0049] Taking the risk identification subject as an example, the risk control strategy includes two preset risk control rules, the first one is whether the user is over 55 years old; the second one is whether the user has made three or more consecutive transactions in the past week. If the user hits the first preset risk control rule but misses the second preset risk control rule, as a first implementation method, when all preset risk control rules in the risk control strategy are hit, it is considered that the risk control strategy is hit. At this time, the user does not hit the risk control strategy and is judged as a low-risk user; as a second implementation method, the first preset risk control rule sets a weight coefficient of 0.3, and the second preset risk control rule sets a weight coefficient of 0.7. When the calculated output value of the risk control strategy is greater than or equal to 0.5, it is considered that the risk control strategy is hit. At this time, the output value of the risk control strategy is 1×0.3+0×0.7=0.3<0.5, so the user does not hit the risk control strategy and is judged as a low-risk user.

[0050] In the above scheme, by comprehensively analyzing the target business data, including the attribute data of the risk identification subject, transaction consumption data and operation behavior data, the potential risks of the risk identification subject can be fully identified. In addition, the automated risk field calculation and preset risk control rule matching can accurately determine whether the risk identification subject has triggered the risk control strategy, thereby achieving effective risk monitoring, reducing the impact of manual intervention and subjective judgment, timely monitoring individual and cluster risks, and improving the reliability and efficiency of risk monitoring of direct payment claims business.

[0051] In some embodiments, the risk field may also include inherent attributes corresponding to the risk identification subject.

[0052] The inherent attributes of the risk identification subject refer to the basic characteristics that are directly related to the risk identification subject and usually do not change with time or behavior. For example, assuming that the risk identification subject is the user end, its inherent attributes may include the card user's gender, place of origin, mobile phone number, policy type, whether one person has multiple cards, whether it is real-name, policy ownership, etc.; assuming that the risk identification subject is the policy end, its inherent attributes may include the insurance company on the policy end, claim limit claim ratio, face recognition rate, etc.; assuming that the risk identification subject is the consumer institution end, its inherent attributes may include the store category, consumption limit, place of origin, etc.

[0053] When judging whether the risk identification subject hits the preset risk control rules, the judgment can be made only based on the risk indicators in the risk field, or it can be combined with inherent attributes to judge whether the preset risk control rules are hit based on both the risk indicators and the inherent attributes.

[0054] Taking the risk identification subject as the user end as an example, the risk indicator of the card held by the user end is the transaction amount, and an inherent attribute of the card is the cardholder's place of origin. The preset risk control rule corresponding to the risk field combining these two can be whether the user end has had any transaction behavior with a transaction amount greater than 1,000 yuan in Sichuan Province.

[0055] In the above scheme, the risk indicators of the risk identification subject can be combined or not combined with their own inherent attributes to make hit judgments for preset risk control rules, thereby reducing misjudgments and missed judgments, which is conducive to supporting the stable development of the business.

[0056] In some embodiments, the risk control strategy also includes a weight corresponding to each preset risk control rule. In this case, step S40 includes: calculating the product between each hit preset risk control rule and its corresponding weight, and calculating the sum of multiple products; determining whether the risk identification subject hits the risk control strategy based on the sum.

[0057] In this embodiment, a weight is assigned to each preset risk control rule to help identify which preset risk control rules are more critical, and the weight ratio of the more critical preset risk control rules is set higher, and vice versa, which can reduce unnecessary risk control intervention and reduce false alarm rate. For example, if a preset risk control rule is triggered, but its weight is low, it may not immediately cause the risk identification subject to be judged as high risk, thereby reducing unnecessary interference with low-risk users.

[0058] In the above scheme, a weight mechanism is introduced to evaluate the contribution of each preset risk control rule in the overall risk control strategy, so that risk assessment is no longer a simple matching of preset risk control rules, but weighting according to the importance of each rule, thereby optimizing the risk control strategy and improving the accuracy of risk assessment.

[0059] In some embodiments, after step S40, the transaction risk monitoring method for direct payment claims business provided by the embodiment of the present application further includes:

[0060] Output the basic information of the risk identification subject, risk subject indicators, risk analysis results of the risk identification subject, and risk details of the risk analysis results; among them, the risk subject indicators are visual indicators generated based on risk fields for administrators to use.

[0061] The present application embodiment does not specifically limit the specific implementation method of outputting each data in the above steps, and those skilled in the art can make appropriate adjustments according to actual conditions. For example, each data can be output in the form of text, pictures, tables, etc.; or each data can be output in the form of voice, etc.

[0062] When outputting the basic information of the risk identification subject, all situations corresponding to the current risk identification subject can be displayed. For example, when the risk identification subject is a card, the user situation and the cards under the name, related user relationships, etc. will be displayed; when the risk identification subject is the policy end or the consumer institution end, the corresponding subject information will also be displayed.

[0063] When outputting risk subject indicators, different risk subject indicator modules can be configured for display according to different risk identification subjects. The risk subject indicators are intuitively displayed after reading the risk fields, which are easy for administrators to understand.

[0064] When outputting the risk analysis results of the risk identification subject, the risk points of the risk identification subject can be further revealed and displayed in categories according to different subjects. For example, the card subject will display the daily consumption trend, consumption type, consumption store, consumption location and distribution, abnormal timeline, etc. The display on the policy side and the consumption institution side is relatively more complicated.

[0065] When outputting the risk details of the risk analysis results, the corresponding risk details are provided in more depth and detail on the basis of the output of the risk analysis results, which is convenient for business processing docking. For example, in the output of the risk analysis results, the card consumption path can be seen. The user made consumption in two provinces on a certain day, but only the consumption province and amount are output. In the output of the risk details, it is also possible to view the store city area where the consumption was made on a certain day, the specific consumption time, the consumption amount and the consumed items. The risk details can include the risk transaction items of the risk identification subject, the risk list involved in the insurance policy, the risk consumption users at the consumer institution end, abnormal transaction items, etc.

[0066] In the above scheme, by providing basic information of risk identification subjects, risk subject indicators, risk analysis results and risk details, users and decision makers can more clearly understand the basis and results of risk assessment and use data to support business decisions and risk management.

[0067] In some embodiments, before acquiring the target business data, the method further includes: cleaning underlying data from different sources to obtain the target business data.

[0068] The underlying data from different insurance companies can be provided in different ways, for example, through mini-programs, or through data sharing platforms, or through cloud services, etc.

[0069] Data cleaning can correct or delete errors, duplications, anomalies or missing values ​​in the data. It can also improve the quality and accuracy of the data. In the process of data cleaning, the data can also be standardized and normalized to convert the data into a unified format.

[0070] In the above solution, data cleaning can identify and correct errors or inconsistencies in the data set, thereby improving data quality, reducing operational risks caused by data errors or inconsistencies, and improving overall business process efficiency.

[0071] In some embodiments, step S20 includes: calculating at least one risk field corresponding to the risk identification subject within the time node.

[0072] The risk field includes a time attribute. For example, the risk field is calculated within the time node using a day, week, month, or year as a time node. Specifically, the risk field may be the longest number of consecutive trading days in a month.

[0073] In the above solution, by calculating the risk field within a specific time node, the risk assessment can be linked to the historical business situation, thereby improving the timeliness and relevance of the risk assessment and enhancing the dynamic risk monitoring capability.

[0074] Reference Figure 2 , Figure 2 A schematic diagram of the structure of a transaction risk monitoring device for a direct payment claims settlement business provided in an embodiment of the present application includes:

[0075] The acquisition module 100 is used to acquire target business data, wherein the target business data includes attribute data of the risk identification subject, transaction consumption data, action trajectory data and operation behavior data.

[0076] The risk field calculation module 200 is used to calculate at least one risk field corresponding to the risk identification subject according to the target business data, and the risk field includes a risk indicator corresponding to the risk identification subject.

[0077] The judgment module 300 is used to judge, for each risk field, whether the risk identification subject hits the preset risk control rule corresponding to the risk field according to the risk field, and obtain the corresponding hit judgment result.

[0078] The risk control strategy calculation module 400 is used to calculate whether the risk identification subject hits the risk control strategy based on the hit judgment result, wherein the risk control strategy includes at least one preset risk control rule.

[0079] It should be understood that the device corresponds to the above-mentioned transaction risk monitoring method embodiment of the direct payment claim business, and can execute the various steps involved in the above-mentioned method embodiment. The specific functions of the device can be found in the description above. To avoid repetition, the detailed description is appropriately omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the device.

[0080] As an optional method, the risk control strategy also includes a weight corresponding to each preset risk control rule, and the risk control strategy calculation module is used to calculate the product between each hit preset risk control rule and its corresponding weight, and calculate the sum of multiple products; determine whether the risk identification subject hits the risk control strategy based on the sum.

[0081] In some embodiments, the above-mentioned device also includes: an output module, which is used to output the basic information of the risk identification subject, risk subject indicators, risk analysis results of the risk identification subject and risk details of the risk analysis results; wherein, the risk subject indicators are visual indicators generated according to the risk fields for use by administrators.

[0082] In some embodiments, the device further includes: a data cleaning module, which is used to clean the underlying data from different sources to obtain target business data.

[0083] In some embodiments, the risk field calculation module is specifically used to calculate at least one risk field corresponding to the risk identification subject within a time node.

[0084] Reference Figure 3 , Figure 3 A schematic diagram of the structure of an electronic device provided for an embodiment of the present application includes: a processor 301, a processor 302 and a bus 303, wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 302 stores machine-readable instructions executable by the processor 301, and when the machine-readable instructions are executed by the processor 301, the methods provided by the above-mentioned method embodiments are executed.

[0085] The processor 301 includes one or more (only one is shown in the figure), which can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 301 can be a general-purpose processor, including a central processing unit (CPU), a micro control unit (MCU), a network processor (NP) or other conventional processors; it can also be a dedicated processor, including a neural network processor (NPU), a graphics processor (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. In addition, when there are multiple processors 301, some of them can be general-purpose processors and the other part can be dedicated processors.

[0086] The processor 302 includes one or more (only one is shown in the figure), which can be, but not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The processor 301 and other possible components can access the processor 302 and read and / or write data therein.

[0087] In particular, one or more computer program instructions may be stored in the processor 302, and the processor 301 may read and execute these computer program instructions to implement the weak password scanning behavior identification method provided in the embodiment of the present application.

[0088] The bus 303 includes one or more (only one is shown in the figure), which can be used to communicate directly or indirectly with other devices to interact with data. The bus 303 may include devices for wired wireless communication, such as optical fiber, serial peripheral interface (Serial Peripheral Interface, referred to as SPI) module, integrated circuit bus (Inter-Integrated Circuit, referred to as I2C), etc., and may also include devices for wireless communication, such as Bluetooth module, Wi-Fi module, mobile communication module (such as 4G, 5G module), etc.

[0089] Understandably, Figure 3 The structure shown is for illustration only. The electronic device may also include Figure 3 More or fewer components as shown, or with Figure 3 Different structures are shown. Figure 3 The components shown in the figure can be implemented by hardware, software or a combination thereof. The electronic device may be a physical device, such as a switch, a router, a server, a PC, etc., or a virtual device, such as a virtual machine, a virtualized container, etc. Moreover, the electronic device is not limited to a single device, but may also be a combination of multiple devices or an integrated environment composed of a large number of devices.

[0090] The present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the methods provided by the above-mentioned method embodiments are executed.

[0091] The present application provides a computer program product, including: a computer program, wherein when the computer program is executed by a processor, the method provided by the above-mentioned method embodiments is executed.

[0092] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0093] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0095] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A transaction risk monitoring method for direct payment claims business, characterized in that: include: Acquire target business data, which includes attribute data of the risk identification subject, transaction consumption data, action trajectory data, and operation behavior data; Calculating at least one risk field corresponding to the risk identification subject according to the target business data, wherein the risk field includes a risk indicator corresponding to the risk identification subject; For each risk field, judging whether the risk identification subject hits the preset risk control rule corresponding to the risk field according to the risk field, and obtaining a corresponding hit judgment result; According to the hit judgment result, it is calculated whether the risk identification subject hits the risk control strategy, wherein the risk control strategy includes at least one preset risk control rule.

2. The method according to claim 1, characterized in that The risk field also includes inherent attributes corresponding to the risk identification subject.

3. The method according to claim 1, characterized in that The risk control strategy also includes a weight corresponding to each preset risk control rule. The calculation of whether the risk identification subject hits the risk control strategy according to the hit judgment result includes: Calculate the product of each hit’s preset risk control rule and its corresponding weight, and calculate the sum of multiple products; Determine whether the risk identification subject hits the risk control strategy based on the sum.

4. The method according to claim 1, characterized in that: After calculating whether the risk identification subject hits the risk control strategy according to the hit judgment result, the method further includes: Output the basic information of the risk identification subject, risk subject indicators, risk analysis results of the risk identification subject, and risk details of the risk analysis results; The risk subject indicator is a visual indicator generated according to the risk field for use by the administrator.

5. The method according to claim 1, characterized in that Before acquiring the target business data, the method further includes: Clean the underlying data from different sources to obtain the target business data.

6. The method according to claim 1, characterized in that The calculating, according to the target business data, at least one risk field corresponding to the risk identification subject comprises: At least one risk field corresponding to the risk identification subject is calculated within a time node.

7. A transaction risk monitoring device for direct payment claims business, characterized in that: include: An acquisition module is used to acquire target business data, wherein the target business data includes attribute data of the risk identification subject, transaction consumption data, action trajectory data, and operation behavior data; A risk field calculation module, used to calculate at least one risk field corresponding to the risk identification subject according to the target business data; A judgment module is used to judge, for each risk field, whether the risk identification subject hits the preset risk control rule corresponding to the risk field according to the risk field, and obtain a corresponding hit judgment result; The risk control strategy calculation module is used to calculate whether the risk identification subject hits the risk control strategy based on the hit judgment result, wherein the risk control strategy includes at least one preset risk control rule.

8. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the processor and the memory communicate with each other via the bus; The memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is performed.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is executed.

10. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is executed.

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