CN110348850A - The arbitrage risk checking method and device, electronic equipment of polymerization payment trade company - Google Patents

Abstract

The invention relates to a cash-out risk detection method, device and electronic equipment for aggregated payment merchants. The method includes: using the first rule engine corresponding to the first preset rule corresponding to the business type of the aggregation payment merchant to analyze the transaction data of the aggregation payment merchant acquired in real time to obtain the cash-out risk value; Aggregate transaction data samples of payment merchant samples; modify the cash-out risk value based on the clustering results obtained by cluster analysis of transaction data and transaction data samples to obtain the target cash-out risk value; when the target cash-out risk value reaches the preset risk threshold , issue a warning message and process the account of the aggregated payment merchant. By analyzing the transaction data of the aggregation payment merchants, the present invention can timely identify the cash-out behavior of the aggregation payment merchants, improve the risk control efficiency of the aggregation payment service provider in the aggregation payment service process, and further reduce the cash-out risk of the aggregation payment merchants.

Description

Cashout risk detection method, device, and electronic equipment for aggregation payment merchants

technical field

The present invention relates to the technical field of data analysis, in particular to a cash-out risk detection method, device, and electronic equipment for aggregation payment merchants.

Background technique

With the rapid development of the global economy, people's consumption level is also increasing, and there are more and more various online payment channels, such as WeChat payment, Alipay payment, etc. Therefore, it is inconvenient for merchants to display the payment QR codes corresponding to various online payment channels for consumers to scan and pay. However, aggregated payment service providers aggregate multiple online payment channels through aggregated payment, and only need a payment QR code for consumers to scan the code to pay with any online payment channel, which can adapt to different scenarios It is convenient for merchants and also precipitates and integrates merchants' transaction data between different channels.

However, in practice, it has been found that aggregate payment also has some security risks. For example, merchants use aggregated payment accounts to provide credit line holders with cash-out channels to earn private profits, thereby promoting credit card or electronic credit consumption cash-out behaviors. Among them, credit card consumer cash-out behavior refers to the behavior of credit card holders not withdrawing cash through normal legal procedures, but withdrawing funds within the credit limit in the card through aggregate payment in cash without paying bank cash withdrawal fees. Merchants using aggregated payment accounts to provide credit line holders with cash-out channels will increase financial risks, do not take advantage of the stability of the financial order, and bring great risks to aggregated payment service providers. However, in the prior art, business personnel are often relied on to manually monitor the cash-out behavior of aggregated payment merchants, which cannot be discovered and processed in a timely manner, resulting in low risk control efficiency.

To sum up, there is an urgent need for a technical means to identify cash-out behaviors of merchants in a timely manner, reduce manual intervention, improve the risk control efficiency of aggregation payment service providers in the process of aggregation payment services, and reduce the risk of merchants’ cash-out.

Contents of the invention

In order to solve the problem in the related art that the aggregation payment service provider cannot timely identify the cash-out behavior of the merchant during the aggregation payment service process, resulting in low risk control efficiency, the present invention provides a cash-out risk detection method and device for the aggregation payment merchant, Electronic equipment.

The first aspect of the embodiment of the present invention discloses a cash-out risk detection method for an aggregated payment merchant, the method comprising:

Obtain a first preset rule corresponding to the business type of the aggregated payment merchant, and the first preset rule is a number of first restrictive conditions used to restrict the account of the aggregated payment merchant;

Obtaining the transaction data of the aggregated payment merchant in real time;

Using the first rule engine corresponding to the first preset rule to analyze the transaction data to obtain the cash-out risk value of the aggregation payment merchant;

Acquiring transaction data samples of aggregated payment merchant samples of the same business type as the aggregated payment merchant from a preset database;

performing cluster analysis according to the transaction data and the transaction data samples to obtain the clustering results of the aggregated payment merchants;

Correcting the cash-at-risk value according to the clustering results to obtain a target cash-at-risk value;

When the target cash-out risk value reaches a preset risk threshold, a warning message is issued, and the account of the aggregation payment merchant is processed; wherein the warning message is used to describe the cash-out behavior of the aggregation payment merchant.

The second aspect of the embodiment of the present invention discloses a cashout risk detection device for an aggregated payment merchant. The device includes:

A rule acquiring unit, configured to acquire a first preset rule corresponding to the business type of the aggregation payment merchant, where the first preset rule includes several first restrictive conditions for restricting the accounts of the aggregation payment merchant;

A data acquisition unit, configured to acquire transaction data of the aggregated payment merchant in real time;

An analysis unit, configured to use the first rule engine corresponding to the first preset rule to analyze the transaction data, and obtain the cash-out risk value of the aggregation payment merchant;

A sample acquisition unit, configured to acquire transaction data samples of aggregated payment merchant samples of the same business type as the aggregated payment merchant from a preset database;

A clustering unit, configured to perform cluster analysis according to the transaction data and the transaction data samples, to obtain the clustering results of the aggregated payment merchants;

A correction unit, configured to correct the cash-out risk value according to the clustering result to obtain a target cash-out risk value;

A processing unit, configured to issue a warning message when the target cash-out risk value reaches a preset risk threshold, and process the account of the aggregation payment merchant; wherein, the warning information is used to describe that the aggregation payment merchant exists cash out behavior.

The third aspect of the embodiment of the present invention discloses an electronic device, and the electronic device includes:

processor;

A memory, where computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, the cash-out risk detection method of the aggregation payment merchant disclosed in the first aspect of the embodiment of the present invention is implemented.

The fourth aspect of the embodiment of the present invention discloses a computer-readable storage medium, which stores a computer program, and the computer program enables the computer to execute the cash-out risk detection method of the aggregation payment merchant disclosed in the first aspect of the embodiment of the present invention.

The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

This technical solution obtains the corresponding first preset rule according to the business type of the aggregation payment merchant, and uses the first rule engine corresponding to the first preset rule to analyze the transaction data of the aggregation payment merchant acquired in real time to obtain The cash-out risk value of the aggregated payment merchants, and the aggregation obtained by obtaining the transaction data samples of the aggregated payment merchant samples with the same business type as the aggregated payment merchants from the preset database, and performing cluster analysis based on the transaction data and transaction data samples Pay the merchant's clustering results, correct the cash-out risk value, and obtain the target cash-out risk value. Based on this, the target cash-out risk value can be detected. When the target cash-out risk value reaches the preset risk threshold, a warning message will be issued and the Processing the accounts of aggregated payment merchants can identify the cash-out behavior of aggregated payment merchants in a timely manner, reduce manual intervention, improve the risk control efficiency of aggregated payment service providers in the aggregated payment service process, and thereby reduce the cash-out risk of aggregated payment merchants.

It is to be understood that both the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention.

Description of drawings

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

Fig. 1 is a schematic structural diagram of a cashout risk detection device for an aggregation payment merchant disclosed in an embodiment of the present invention;

Fig. 2 is a schematic flow diagram of a cash-out risk detection method for an aggregation payment merchant disclosed in an embodiment of the present invention;

Fig. 3 is a schematic flow diagram of another cash-out risk detection method for an aggregation payment merchant disclosed in an embodiment of the present invention;

Fig. 4 is a schematic flow diagram of another cash-out risk detection method for an aggregation payment merchant disclosed in an embodiment of the present invention;

Fig. 5 is a schematic structural diagram of another cash-out risk detection device for an aggregation payment merchant disclosed in an embodiment of the present invention;

Fig. 6 is a schematic structural diagram of another cashout risk detection device for an aggregation payment merchant disclosed in an embodiment of the present invention;

Fig. 7 is a schematic structural diagram of another cashout risk detection device for an aggregated payment merchant disclosed in an embodiment of the present invention.

Detailed ways

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with aspects of the invention as recited in the appended claims.

Embodiment one

The implementation environment of the present invention may be an electronic device, such as a smart phone, a tablet computer, or a desktop computer.

Fig. 1 is a schematic structural diagram of a cashout risk detection device for an aggregated payment merchant disclosed in an embodiment of the present invention. The apparatus 100 may be the above-mentioned electronic equipment. As shown in FIG. 1 , apparatus 100 may include one or more of the following components: processing component 102 , memory 104 , power component 106 , multimedia component 108 , audio component 110 , sensor component 114 , and communication component 116 .

The processing component 102 generally controls the overall operations of the device 100, such as operations associated with display, phone calls, data communications, camera operations, and recording operations, among others. The processing component 102 may include one or more processors 118 to execute instructions to complete all or part of the steps of the methods described below. Additionally, processing component 102 may include one or more modules for facilitating interaction between processing component 102 and other components. For example, processing component 102 may include a multimedia module to facilitate interaction between multimedia component 108 and processing component 102 .

The memory 104 is configured to store various types of data to support operations at the device 100 . Examples of such data include instructions for any application or method operating on device 100 . The memory 104 can be realized by any type of volatile or non-volatile storage device or their combination, such as Static Random Access Memory (Static Random Access Memory, referred to as SRAM), Electrically Erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read Only Memory (EPROM for short), Programmable Red-Only Memory (PROM for short), Read-Only Memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The memory 104 also stores one or more modules configured to be executed by the one or more processors 118 to complete all or part of the steps in the method shown below.

The power supply component 106 provides power to various components of the device 100 . Power components 106 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for device 100 .

The multimedia component 108 includes a screen that provides an output interface between the device 100 and the user. In some embodiments, the screen may include a liquid crystal display (Liquid Crystal Display, LCD for short) and a touch panel. If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may not only sense a boundary of a touch or a swipe action, but also detect duration and pressure associated with the touch or swipe operation. The screen may also include an organic electroluminescence display (Organic Light Emitting Display, OLED for short).

The audio component 110 is configured to output and/or input audio signals. For example, the audio component 110 includes a microphone (Microphone, MIC for short), which is configured to receive external audio signals when the device 100 is in operation modes, such as calling mode, recording mode and voice recognition mode. Received audio signals may be further stored in memory 104 or sent via communication component 116 . In some embodiments, the audio component 110 also includes a speaker for outputting audio signals.

Sensor assembly 114 includes one or more sensors for providing various aspects of status assessment for device 100 . For example, sensor assembly 114 may detect the open/closed state of device 100 , relative positioning of components, sensor assembly 114 may also detect changes in the position of device 100 or a component of device 100 , and temperature changes in device 100 . In some embodiments, the sensor assembly 114 may also include a magnetic sensor, a pressure sensor or a temperature sensor.

The communication component 116 is configured to facilitate wired or wireless communication between the apparatus 100 and other devices. The device 100 may access a wireless network based on a communication standard, such as WiFi (Wireless-Fidelity, wireless fidelity). In the embodiment of the present invention, the communication component 116 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In the embodiment of the present invention, the communication component 116 further includes a near field communication (Near Field Communication, NFC for short) module, configured to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (Radio Frequency Identification, referred to as RFID) technology, infrared data association (Infrared Data Association, referred to as IrDA) technology, ultra wideband (Ultra Wideband, referred to as UWB) technology, Bluetooth technology and other technologies.

In an exemplary embodiment, the apparatus 100 may be controlled by one or more application-specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), digital signal processors, digital signal processing equipment, programmable logic devices, field programmable gate arrays, implemented by a microcontroller, microcontroller, microprocessor or other electronic components for performing the method described below.

Embodiment two

Please refer to FIG. 2 . FIG. 2 is a schematic flow chart of a cashout risk detection method for an aggregated payment merchant disclosed in an embodiment of the present invention. Wherein, the cashout risk detection method of the aggregation payment merchant is applicable to the cashout risk detection device of the aggregation payment merchant. Wherein, the cashout risk detection device of the aggregation payment merchant may be electronic equipment such as smart phones, tablet computers, and desktop computers. For the convenience of examples, the embodiments of the present invention are described by taking an electronic device as an execution subject, and it should be understood that the present invention should not be construed in any way. As shown in Figure 2, the cash-out risk detection method of the aggregation payment merchant may include the following steps:

201. The electronic device acquires a first preset rule corresponding to an operation type of an aggregated payment merchant, where the first preset rule includes several first restrictive conditions for restricting the accounts of the aggregated payment merchant.

It should be noted that, in the embodiment of the present invention, the business type can be specifically divided into individuals, individual industrial and commercial households, or enterprises. Wherein, the electronic device may be pre-configured with different first preset rules according to the business type of the aggregation payment merchant.

For example, assuming that the business type of a certain aggregation payment merchant is an individual industrial and commercial household, then a certain entry restriction condition in the corresponding first preset rule is "the number of affiliated sub-merchants cannot exceed A, and A is a positive integer "; assuming that the business type of a certain aggregation payment merchant is an enterprise, then the corresponding entry restriction condition is "the number of affiliated sub-merchants cannot exceed B, and B is a positive integer". Among them, A is not equal to B, and under normal circumstances, A is less than B, but the special case that A is greater than B is not excluded.

202. The electronic device obtains the transaction data of the aggregate payment merchant in real time.

Among them, the transaction data specifically includes consumption data, regional data, bank data, population attribute data, credit data, social network data, and communication data related to transaction records.

It can be understood that there is no sequence between step 201 and step 202. In the embodiment of the present invention, step 201 can be performed first and then step 202 can be performed; while in other possible embodiments, step 201 and step 202 can be performed simultaneously; Alternatively, step 202 may be performed first, and then step 201 may be performed.

As an optional implementation, an aggregated payment management platform can be built, and the aggregated payment management platform includes a merchant client, a service provider client, and a server. Wherein, the merchant client is used for aggregated payment merchants to log in to the aggregated payment management platform for account registration or setting management, and the service provider client is used for aggregated payment service providers to log in to the aggregated payment management platform to implement the first preset rules. Configuration, the server is used to manage the accounts of the aggregated payment merchants. It can be understood that the server end may specifically be an electronic device.

When the aggregation payment merchant logs in the account on the merchant client, the server performs fingerprint input or face recognition verification on the aggregation payment merchant to obtain the verification result. Based on this, before performing step 202, the electronic device may send a query request to the service device to query the above verification result. If the verification result is used to describe the verification success, execute step 202; otherwise, end this process.

By implementing the above embodiments, before acquiring the transaction data of the aggregated payment merchants in real time, identifying whether the aggregated payment merchants have successfully authenticated when logging in can improve security.

203. The electronic device uses the first rule engine corresponding to the first preset rule to analyze the transaction data, and obtain the cash-out risk value of the aggregation payment merchant.

204. The electronic device obtains, from a preset database, transaction data samples of aggregated payment merchant samples of the same business type as the aggregated payment merchant.

205. The electronic device performs clustering analysis according to the transaction data and transaction data samples, and obtains the clustering result of the aggregated payment merchants.

206. The electronic device corrects the cash-out risk value according to the clustering result to obtain the target cash-out risk value.

207. When the target cash-out risk value reaches the preset risk threshold, the electronic device sends out a warning message, and processes the account of the aggregation payment merchant; wherein, the warning information is used to describe the cash-out behavior of the aggregation payment merchant.

Implement the method described in Figure 2, by configuring different first preset rules for aggregated payment merchants of different business types, and using the first rule engine corresponding to the first preset rule, the transactions of aggregated payment merchants acquired in real time Analyze the data to obtain the cash-out risk value of the aggregation payment merchants, and obtain the transaction data samples of the aggregation payment merchant samples with the same business type as the aggregation payment merchants from the preset database, and perform clustering according to the transaction data and transaction data samples Analyze the clustering results of the aggregated payment merchants obtained, correct the cash-out risk value, and obtain the target cash-out risk value. Based on this, the target cash-out risk value can be detected. When the target cash-out risk value reaches the preset risk threshold, Sending out warning messages and processing the accounts of aggregated payment merchants can identify the cash-out behavior of aggregated payment merchants in a timely manner, reduce manual intervention, improve the risk control efficiency of aggregated payment service providers in the aggregated payment service process, and reduce the cash-out of aggregated payment merchants. risk.

Embodiment three

Please refer to FIG. 3 . FIG. 3 is a schematic flow chart of another cashout risk detection method for an aggregated payment merchant disclosed in an embodiment of the present invention. As shown in Figure 3, the cash-out risk detection method of the aggregation payment merchant may include the following steps:

301. The electronic device retrieves the industrial and commercial credit information of the aggregation payment merchant from the credit data interface.

Wherein, the industrial and commercial credit information may specifically include one or more information of industrial and commercial names, legal person information, business license information, and equity allocation.

302. The electronic device obtains the business type of the aggregated payment merchant according to the industrial and commercial credit information.

303-304. Wherein, steps 303 to 304 are the same as steps 201 to 202 in the second embodiment, and will not be repeated in this embodiment of the present invention.

305. The electronic device inputs the user attribute information of the aggregated payment merchant into the preset risk stratification model, and obtains the credit score value of the aggregated payment merchant.

Wherein, the preset risk stratification model may be a risk stratification model obtained by training according to collected user attribute information samples of multiple aggregated payment merchant samples as training data. Among them, the user attribute information may be one or more information in credit records, repayment habits, overdue records, payment ability and social network data.

Implementing this embodiment can improve the accuracy of the credit scoring value, and can also use social network data to perform credit scoring on aggregated payment merchants, improving the comprehensiveness of evaluating aggregated payment merchants.

306. The electronic device judges whether the credit score value reaches a preset scoring threshold. If not, execute steps 307 and 310; otherwise, execute steps 308-310.

307. The electronic device uses the first rule engine corresponding to the first preset rule to analyze the transaction data, and obtain the cash-out risk value of the aggregation payment merchant.

308. The electronic device optimizes the first preset rule to obtain the second preset rule; wherein, the second preset rule includes several second restriction conditions for restricting the accounts of the aggregation payment merchants, and the second restriction conditions correspond to The restriction strength is smaller than the restriction strength corresponding to the first restriction condition.

In the embodiment of the present invention, the specific implementation of step 308 may be: the electronic device adjusts the values corresponding to the first restriction conditions in the first preset rule to obtain the second preset rule.

It should be noted that each first restriction in the first preset rule corresponds to each second restriction in the second preset rule, and the corresponding first restriction and second restriction have the same content , with different values. For example, assume that a certain first restriction condition in the first default rule corresponding to a certain business type is: "The credit card consumption in the transaction data received by the payment account of the same aggregation payment merchant cannot exceed C%, where C is positive Integer", and if the credit score value of the aggregation payment merchant reaches the preset scoring threshold, after optimizing the first restriction condition, the corresponding second restriction condition is "in the transaction data received by the payment account number of the same aggregation payment merchant Credit card consumption cannot exceed D%, where D is a positive integer", where C is less than D. It can be seen that, for the same aggregate payment merchant, the restrictive strength of the second restrictive condition is less than that of the first restrictive condition, that is to say, the second restrictive condition is lower than the first restrictive condition.

By implementing the above steps 305-308, by performing credit scoring on aggregated payment merchants of the same business type, when their credit scoring value reaches the preset scoring threshold, matching them with more optimized preset rules can improve risk control efficiency.

309. The electronic device uses the second rule engine corresponding to the second preset rule to analyze the transaction data, and obtain the cash-out risk value of the aggregated payment merchant.

310-313. Wherein, steps 310-313 are the same as steps 204-207 described in the second embodiment, and are not repeated in this embodiment of the present invention.

In the embodiment of the present invention, the electronic device processing the aggregate payment merchant's account in step 313 may include the following steps: the electronic device judges whether the difference between the target cash-out risk value and the preset risk threshold reaches the preset difference threshold. If so, the electronic device closes the account of the aggregated payment merchant; if not, the electronic device freezes the funds of the aggregated payment merchant's account.

Among them, it can be understood that if the difference reaches the preset difference threshold, it can be determined that the target cash-out risk value exceeds the preset risk threshold to a greater extent, and the cash-out risk level of the aggregation payment merchant is relatively high, so it is more strict to take account closure. treatment measures. In the same way, if it is not achieved, take more lenient measures such as freezing funds. Wherein, after the electronic device freezes the funds of the aggregated payment merchant's account, the electronic device can also issue a prompt message, which is used to remind the user to check the transaction journal of the aggregated payment merchant's account within the preset time; Receive the unfreeze funds instruction for the aggregated payment merchant’s account input by the user within the specified time, and unfreeze the funds for the aggregated payment merchant’s account; Account closing instruction, to close the account of the aggregated payment merchant's account.

Implementing this embodiment, when it is detected that the target cash-out risk value reaches the preset risk threshold, by judging whether the difference between the target cash-out risk value and the preset risk threshold reaches the preset difference threshold, it can be identified that the target cash-out risk value exceeds the preset risk threshold. Set the degree of risk threshold to identify the cash-out risk level of the aggregation payment merchants, and according to different cash-out risk levels, carry out different treatments on the accounts of the aggregation payment merchants, which can improve the risk control of the aggregation payment service provider in the aggregation payment service process Efficiency, thereby reducing the risk of cashing out of aggregated payment merchants.

It can be seen that the implementation of the method described in Figure 3 can timely identify the cash-out behavior of the aggregation payment merchants, reduce manual intervention, improve the risk control efficiency of the aggregation payment service provider in the aggregation payment service process, and thereby reduce the cash-out risk of the aggregation payment merchants.

In addition, by performing credit scoring on aggregated payment merchants of the same business type, when their credit score reaches the preset scoring threshold, matching them with more optimized preset rules can improve the efficiency of risk control.

In addition, when it is detected that the target cash-out risk value reaches the preset risk threshold, by judging whether the difference between the target cash-out risk value and the preset risk threshold reaches the preset difference threshold, it can be identified that the target cash-out risk value exceeds the preset risk The extent of the threshold is to identify the cash-out risk level of the aggregation payment merchant, and according to the different cash-out risk levels, the accounts of the aggregation payment merchants are treated differently, which can improve the risk control efficiency of the aggregation payment service provider in the aggregation payment service process. Thereby reducing the risk of merchants cashing out of aggregated payment.

Embodiment Four

Please refer to FIG. 4 . FIG. 4 is a schematic flow chart of another cashout risk detection method for an aggregated payment merchant disclosed in an embodiment of the present invention. As shown in Figure 4, the cash-out risk detection method of the aggregation payment merchant may include the following steps:

401~402. Wherein, steps 401-402 are the same as steps 201-202 in the second embodiment, and will not be repeated in this embodiment of the present invention.

403. The electronic device caches the transaction data locally on the rule engine server for viewing by the user.

It can be understood that the rule engine used to analyze the transaction data of the aggregated payment merchants can be modified and edited at any time to optimize the rules.

404. The electronic device receives a rule modification instruction sent by the user.

405. The electronic device sends the call request corresponding to the rule modification instruction to the rule engine server, so that the rule engine server obtains the editable parameters of the first rule engine corresponding to the first preset rule according to the call request. Wherein, the editable parameters may include one or more parameters.

406. The electronic device receives the first numerical value corresponding to the editable parameter input by the user.

In the embodiment of the present invention, step 406 may specifically include: the electronic device receives the first numerical value corresponding to the editable parameter input by the user through the editable parameter file; wherein the editable parameter file is used for the user to directly modify the editable parameter and Editing; wherein, optionally, if the editable parameter includes multiple parameters, the editable parameter file is displayed in a list form. Implementing this implementation manner can make the editing interface of the rule engine more intuitive and the editing operation easier.

Optionally, after performing step 406, the electronic device may determine whether the first value is consistent with the original value of the editable parameter; if yes, perform step 407; otherwise, end the process.

407. The electronic device queries the position information of the editable parameter from the code of the first rule engine.

408. The electronic device replaces the original value at the position corresponding to the position information with the first value, so as to generate a third rule engine.

By implementing the above steps 403-408, it is convenient for the user to modify and edit the rule engine used for analyzing the transaction data of the aggregated payment merchants at any time, so as to optimize the rules. Moreover, there is no need to redevelop when editing the rule engine, and the editable parameter file is used for the user to directly modify and edit the editable parameters, the editing operation is simple, the efficiency is high, and time and effort are saved.

409. The electronic device uses the third rule engine to analyze the transaction data, and obtain the cash-out risk value of the aggregation payment merchant.

410-413. Wherein, steps 410-413 are the same as steps 204-207 described in the second embodiment, and are not repeated in this embodiment of the present invention.

It can be seen that the implementation of the method described in Figure 4 can timely identify the cash-out behavior of the aggregation payment merchants, reduce manual intervention, improve the risk control efficiency of the aggregation payment service provider in the aggregation payment service process, and thereby reduce the cash-out risk of the aggregation payment merchants.

In addition, it is convenient for users to modify and edit the rule engine used to analyze the transaction data of the aggregated payment merchants at any time to optimize the rules. Moreover, there is no need to redevelop when editing the rule engine, and the editable parameter file is used for the user to directly modify and edit the editable parameters, the editing operation is simple, the efficiency is high, and time and effort are saved.

In addition, the editable parameter files can also be displayed in a list form, making the editing interface of the rule engine more intuitive and the editing operation easier.

Embodiment five

Please refer to FIG. 5 . FIG. 5 is a schematic structural diagram of another cashout risk detection device for an aggregated payment merchant disclosed in an embodiment of the present invention. As shown in Figure 5, the cashout risk detection device of the aggregated payment merchant may include: a rule acquisition unit 501, a data acquisition unit 502, an analysis unit 503, and a processing unit 504, wherein,

The rule acquiring unit 501 is configured to acquire a first preset rule corresponding to the business type of the aggregated payment merchant, and the first preset rule includes several first restrictive conditions for restricting the accounts of the aggregated payment merchant.

The data acquisition unit 502 is configured to acquire the transaction data of the aggregated payment merchants in real time.

The analysis unit 503 is configured to use the first rule engine corresponding to the first preset rule to analyze the transaction data, and obtain the cash-out risk value of the aggregated payment merchant.

The sample acquiring unit 504 is configured to acquire transaction data samples of aggregated payment merchant samples of the same business type as the aggregated payment merchant from a preset database.

The clustering unit 505 is configured to perform cluster analysis according to the transaction data and transaction data samples, and obtain clustering results of aggregated payment merchants.

The correction unit 506 is configured to correct the cash-out risk value according to the clustering result to obtain the target cash-out risk value.

The processing unit 507 is configured to issue a warning message when the target cash-out risk value reaches a preset risk threshold, and process the aggregated payment merchant's account. Among them, the warning information is used to describe the cash-out behavior of the aggregated payment merchants.

As an optional implementation, the cashout risk detection device of the aggregated payment merchant shown in FIG. 5 may also include an unillustrated query unit, which is used to send to The service device sends a query request to query the verification result; the verification result is used to describe whether the fingerprint input or face recognition verification performed by the aggregated payment merchant when logging in to the account is successful.

Correspondingly, the above-mentioned data acquisition unit 502 is also used to acquire the transaction data of the aggregation payment merchant in real time when the verification result is used to describe the verification success.

By implementing the above embodiments, before acquiring the transaction data of the aggregated payment merchants in real time, identifying whether the aggregated payment merchants have successfully authenticated when logging in can improve security.

Implement the device shown in Figure 5, by configuring different first preset rules for aggregated payment merchants of different business types, and using the first rule engine corresponding to the first preset rule, the transactions of aggregated payment merchants acquired in real time Analyze the data to obtain the cash-out risk value of the aggregation payment merchants, and obtain the transaction data samples of the aggregation payment merchant samples with the same business type as the aggregation payment merchants from the preset database, and perform clustering according to the transaction data and transaction data samples Analyze the clustering results of the aggregated payment merchants obtained, correct the cash-out risk value, and obtain the target cash-out risk value. Based on this, the target cash-out risk value can be detected. When the target cash-out risk value reaches the preset risk threshold, Sending out warning messages and processing the accounts of aggregated payment merchants can identify the cash-out behavior of aggregated payment merchants in a timely manner, reduce manual intervention, improve the risk control efficiency of aggregated payment service providers in the aggregated payment service process, and reduce the cash-out of aggregated payment merchants. risk.

Embodiment six

Please refer to FIG. 6 . FIG. 6 is a schematic structural diagram of another cashout risk detection device for an aggregated payment merchant disclosed in an embodiment of the present invention. The cashout risk detection device of the aggregated payment merchant shown in FIG. 6 is obtained by optimizing the cashout risk detection device of the aggregated payment merchant shown in FIG. 5 . Compared with the cashout risk detection device of the aggregated payment merchant shown in Figure 5, the cashout risk detection device of the aggregated payment merchant shown in Figure 6 may further include: a call unit 508, a first acquisition unit 509, and a second acquisition unit 510 and hive 511, where,

The calling unit 508 is used to call the industrial and commercial credit information of the aggregate payment merchant from the credit data interface before the rule acquisition unit 501 acquires the first preset rule corresponding to the business type of the aggregate payment merchant. The industrial and commercial credit information includes the business name, One or more of legal person information, business license information, and equity allocation.

The first obtaining unit 509 is configured to obtain the business type of the aggregation payment merchant according to the business credit information, and the business type includes individual, individual business or enterprise.

The second acquiring unit 510 is configured to input the user attribute information of the aggregated payment merchant into the preset risk stratification model after the rule acquiring unit 501 acquires the first preset rule corresponding to the business type of the aggregated payment merchant, and obtain the aggregated payment The merchant's credit score value, user attribute information includes one or more information in credit records, repayment habits, overdue records, payment ability and social network data.

The configuration unit 511 is configured to optimize the first preset rule and obtain the second preset rule when the credit score value reaches the preset score threshold; wherein, the second preset rule includes several rules for restricting aggregated payment merchants The second restrictive condition of the account, the restrictive strength corresponding to the second restrictive condition is smaller than the restrictive strength corresponding to the first restrictive condition.

The above analysis unit 503 is used to use the first rule engine corresponding to the first preset rule to analyze the transaction data and obtain the cash-out risk value of the aggregated payment merchant as follows:

The above analysis unit 503 is configured to use the second rule engine corresponding to the second preset rule to analyze the transaction data, and obtain the cash-out risk value of the aggregation payment merchant.

As an optional implementation manner, the manner in which the processing unit 507 processes the accounts of the aggregated payment merchants is specifically:

The processing unit 507 is used to determine whether the difference between the target cash-out risk value and the preset risk threshold reaches the preset difference threshold; Shutdown processing; and, when it is judged that the difference value does not reach the preset difference value threshold, the funds of the aggregated payment merchant's account are frozen.

As an optional implementation, the cash-out risk detection device for aggregated payment merchants shown in FIG. 6 may also include an unillustrated modeling unit, which is used to use the collected user attribute information samples of multiple aggregated payment merchant samples as The training data is used for training to obtain a risk stratification model.

Implementing this embodiment can improve the accuracy of the credit scoring value, and can also use social network data to perform credit scoring on aggregated payment merchants, improving the comprehensiveness of evaluating aggregated payment merchants.

It can be seen that the implementation of the device shown in Figure 6 can timely identify the cash-out behavior of the aggregation payment merchants, reduce manual intervention, improve the risk control efficiency of the aggregation payment service provider in the aggregation payment service process, and thereby reduce the cash-out risk of the aggregation payment merchants.

In addition, by performing credit scoring on aggregated payment merchants of the same business type, when their credit score reaches the preset scoring threshold, matching them with more optimized preset rules can improve the efficiency of risk control.

In addition, when it is detected that the target cash-out risk value reaches the preset risk threshold, by judging whether the difference between the target cash-out risk value and the preset risk threshold reaches the preset difference threshold, it can be identified that the target cash-out risk value exceeds the preset risk The extent of the threshold is to identify the cash-out risk level of the aggregation payment merchant, and according to the different cash-out risk levels, the accounts of the aggregation payment merchants are treated differently, which can improve the risk control efficiency of the aggregation payment service provider in the aggregation payment service process. Thereby reducing the risk of merchants cashing out of aggregated payment.

Embodiment seven

Please refer to FIG. 7 . FIG. 7 is a schematic structural diagram of another cashout risk detection device for an aggregated payment merchant disclosed in an embodiment of the present invention. The cashout risk detection device of the aggregated payment merchant shown in FIG. 7 is obtained by optimizing the cashout risk detection device of the aggregated payment merchant shown in FIG. 6 . Compared with the cashout risk detection device of the aggregated payment merchant shown in Figure 6, the cashout risk detection device of the aggregated payment merchant shown in Figure 7 may further include: a cache unit 512, a receiving unit 513 and an editing unit 514, wherein,

The caching unit 512 is configured to cache the transaction data to the rule engine server locally after the data obtaining unit 502 obtains the transaction data of the aggregated payment merchant in real time for users to view.

The receiving unit 513 is configured to receive the rule modification instruction sent by the user after the cache unit 512 caches the transaction data locally on the rule engine server for the user to view.

The editing unit 514 is configured to edit the first rule engine corresponding to the first preset rule according to the rule modification instruction, so as to generate a third rule engine.

The above analysis unit 503 is used to use the first rule engine corresponding to the first preset rule to analyze the transaction data and obtain the cash-out risk value of the aggregated payment merchant as follows:

The analysis unit 503 mentioned above is used to analyze the transaction data by using the third rule engine, and obtain the cash-out risk value of the aggregated payment merchant.

As an optional implementation manner, the above-mentioned editing unit 514 may include:

The sending subunit 5141 is configured to send the call request corresponding to the rule modification instruction to the rule engine server, so that the rule engine server obtains the editable parameters of the first rule engine corresponding to the first preset rule according to the call request.

The receiving subunit 5142 is configured to receive the first numerical value corresponding to the editable parameter input by the user.

The update subunit 5143 is configured to update the editable parameter according to the first value, so as to generate the third rule engine.

And, the cashout risk detection device of the aggregated payment merchant shown in FIG. 7 may further include a sending unit (not shown), configured to send prompt information describing the processing result of generating the third rule engine to the user.

As an optional implementation manner, the above-mentioned update subunit 5143 may include the following modules not shown in the figure:

The query module is used to query the position information of the editable parameter from the code of the first rule engine.

A replacement module, configured to replace the original value at the position corresponding to the position information with the first value, so as to generate a third rule engine.

As an optional implementation manner, the manner in which the receiving subunit 5142 receives the first numerical value corresponding to the editable parameter input by the user may specifically be:

The receiving subunit 5142 is used to receive the first numerical value corresponding to the editable parameter input by the user through the editable parameter file; wherein the editable parameter file is used for the user to directly modify and edit the editable parameter; wherein, optionally , if the editable parameters include multiple parameters, the editable parameter files will be displayed in a list form.

Implementing this implementation manner can make the editing interface of the rule engine more intuitive and the editing operation easier.

It can be seen that the implementation of the device shown in Figure 7 can timely identify the cash-out behavior of the aggregation payment merchants, reduce manual intervention, improve the risk control efficiency of the aggregation payment service provider in the aggregation payment service process, and then reduce the risk of aggregation payment merchants cashing out.

In addition, it is convenient for users to modify and edit the rule engine used to analyze the transaction data of the aggregated payment merchants at any time to optimize the rules. Moreover, there is no need to redevelop when editing the rule engine, and the editable parameter file is used for the user to directly modify and edit the editable parameters, the editing operation is simple, the efficiency is high, and time and effort are saved.

In addition, the editable parameter files can also be displayed in a list form, making the editing interface of the rule engine more intuitive and the editing operation easier.

The present invention also provides an electronic device, which includes:

processor;

A memory, where computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the cash-out risk detection method of the aggregation payment merchant as shown above is realized.

The electronic device may be the apparatus 100 shown in FIG. 1 .

In an exemplary embodiment, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the cash-out risk detection method of the aggregation payment merchant as shown above is implemented. .

It should be understood that the present invention is not limited to the precise constructions which have been described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims (10)

1. a kind of arbitrage risk checking method of polymerization payment trade company, which is characterized in that the described method includes:

The first preset rules corresponding to the working group of polymerization payment trade company are obtained, first preset rules include several For limiting the first restrictive condition of the account of polymerization payment trade company；

The transaction data of polymerization payment trade company is obtained in real time；

Using corresponding first regulation engine of first preset rules, the transaction data is analyzed, is obtained described poly- Close the arbitrage value-at-risk of payment trade company；

The friendship of polymerization payment trade company's sample identical with the working group for polymerizeing payment trade company is obtained from presetting database Easy data sample；

Clustering is carried out according to the transaction data and the transaction data sample, obtains the cluster of polymerization payment trade company As a result；

According to the cluster result, the arbitrage value-at-risk is modified, obtains target arbitrage value-at-risk；

It when the target arbitrage value-at-risk reaches default risk threshold value, sends a warning message, and trade company is paid to the polymerization Account handled；Wherein, there are arbitrage behaviors for describing polymerization payment trade company for the warning message.

2. the method according to claim 1, wherein corresponding to the working group for obtaining polymerization payment trade company The first preset rules before, the method also includes:

The industrial and commercial credit information of polymerization payment trade company is transferred from credit data interface, the industry and commerce credit information includes industrial and commercial surname One of name, corporate message, business license information and Allocation of Equity or much information；

According to the industrial and commercial credit information, obtain the working group of polymerization payment trade company, the working group include it is personal, Self-employed entrepreneur or enterprise.

3. method according to claim 1 or 2, which is characterized in that the working group institute for obtaining polymerization payment trade company After corresponding first preset rules, the method also includes:

The customer attribute information of polymerization payment trade company is inputted into preset risk stratification model, obtains the polymerization payment provider The credit scoring value at family, the customer attribute information include reference record, habit of refunding, overdue record, ability to pay and social activity One of network data or much information；

When the credit scoring value reaches default scoring threshold value, first preset rules are optimized, it is pre- to obtain second If regular；Wherein, second preset rules include several second limits for being used to limit the account of polymerization payment trade company Condition processed, the corresponding limitation dynamics of second restrictive condition are less than the corresponding limitation dynamics of first restrictive condition；

And it is described using corresponding first regulation engine of first preset rules, the transaction data is analyzed, is obtained Obtain the arbitrage value-at-risk of polymerization payment trade company, comprising:

Using the corresponding Second Rule engine of second preset rules, the transaction data is analyzed, is obtained described poly- Close the arbitrage value-at-risk of payment trade company.

4. the method according to claim 1, wherein the number of deals for obtaining polymerization payment trade company in real time According to later, the method also includes:

The transaction data is cached to rules engine service device local, so that user checks；

Receive the rules modification instruction that the user sends；

It is instructed according to the rules modification, corresponding first regulation engine of first preset rules is edited, to generate Third regulation engine；

And it is described using corresponding first regulation engine of first preset rules, the transaction data is analyzed, is obtained Obtain the arbitrage value-at-risk of polymerization payment trade company, comprising:

Using the third regulation engine, the transaction data is analyzed, obtains the arbitrage wind of polymerization payment trade company Danger value.

5. according to the method described in claim 4, it is characterized in that, described instruct according to the rules modification, to described first Corresponding first regulation engine of preset rules is edited, to generate third regulation engine, comprising:

Corresponding call request is instructed to be sent to the rules engine service device rules modification, so that the regulation engine Server obtains the editable parameter of corresponding first regulation engine of first preset rules according to the call request；

Receive corresponding first numerical value of the editable parameter of user's input；

The editable parameter is updated according to first numerical value, to generate third regulation engine；

And the method also includes:

The prompt information of the processing result for describing to generate the third regulation engine is sent to the user.

6. according to the method described in claim 5, it is characterized in that, it is described according to first numerical value to the editable parameter It is updated, to generate third regulation engine, comprising:

The location information of the editable parameter is inquired from the code of first regulation engine；

Former numerical value on the corresponding position of the location information is replaced with into first numerical value, to generate third regulation engine.

7. according to claim 1, method described in 4,5 or 6, which is characterized in that the account to polymerization payment trade company It is handled, comprising:

Judge whether the target arbitrage value-at-risk and the difference of the default risk threshold value reach preset difference value threshold value；

If reaching, account is carried out to the account of polymerization payment trade company and shuts down processing；

If not up to, carrying out freezing of funds processing to the account of polymerization payment trade company.

8. a kind of arbitrage risk supervision device of polymerization payment trade company, which is characterized in that described device includes:

Rule unit polymerize the first preset rules corresponding to the working group of payment trade company for obtaining, and described first Preset rules include the first restrictive condition that several are used to limit the account of polymerization payment trade company；

Data capture unit, for obtaining the transaction data of polymerization payment trade company in real time；

Analytical unit, for dividing the transaction data using corresponding first regulation engine of first preset rules Analysis obtains the arbitrage value-at-risk of polymerization payment trade company；

Sample acquisition unit, for being obtained from presetting database and the identical polymerization of working group for polymerizeing payment trade company Pay the transaction data sample of trade company's sample；

Cluster cell obtains the polymerization for carrying out clustering according to the transaction data and the transaction data sample Pay the cluster result of trade company；

Amending unit obtains target arbitrage risk for being modified to the arbitrage value-at-risk according to the cluster result Value；

Processing unit, for sending a warning message, and to described when the target arbitrage value-at-risk reaches default risk threshold value The account of polymerization payment trade company is handled；Wherein, there are arbitrages for describing polymerization payment trade company for the warning message Behavior.

9. a kind of electronic equipment, including memory and processor, the memory are stored with computer program, which is characterized in that The processor realizes the set of polymerization payment according to any one of claims 1 to 7 trade company when executing the computer program The step of existing risk checking method.

10. a kind of computer readable storage medium, which is characterized in that it stores computer program, and the computer program makes Computer perform claim requires the arbitrage risk checking method of 1~7 described in any item polymerization payments trade company.