WO2020103560A1

WO2020103560A1 - Risk control method and apparatus, and server and storage medium

Info

Publication number: WO2020103560A1
Application number: PCT/CN2019/107005
Authority: WO
Inventors: 陈欢乐; 李洁; 冯力国; 杨路燕
Original assignee: 阿里巴巴集团控股有限公司
Priority date: 2018-11-21
Filing date: 2019-09-20
Publication date: 2020-05-28
Also published as: CN110020766A; TWI706422B; TW202020888A

Abstract

A risk control method and apparatus, and a server and a storage medium. The method comprises: obtaining target data of a target merchant (S11); obtaining risk health data of the target merchant according to the target data and a preset risk scoring model (S12); and determining a target data range corresponding to the risk health data, and allocating service resources to the target merchant according to a resource allocation rule corresponding to the target data range (S13). According to the solution, the risk health data of the target merchant is determined by means of the target data, so that the target merchant can perceive the risk more intuitively by means of the risk health data.

Description

Risk control method, device, server and storage medium

Technical field

The invention relates to the field of computer technology, in particular to a risk control method, device, server and storage medium.

Background technique

With the continuous development of Internet technology, more and more services can be carried out on the network, and the interaction and processing volume of network data are also increasing. In the existing technology, there are many risks on the network, such as cash-out risk, misappropriation risk, etc. Therefore, for merchants using network services, how to perceive the risk and control the risk is crucial.

Summary of the invention

Embodiments of this specification provide and provide a signature recording method, verification method, device, and storage medium.

In a first aspect, embodiments of this specification provide a risk control method, including:

Obtain the target data of the target merchant;

Obtain the risk health data of the target merchant according to the target data and the preset risk scoring model;

Determine a target data range corresponding to the risk health data, and allocate business resources to the target merchant according to resource allocation rules corresponding to the target data range.

In a second aspect, an embodiment of this specification provides a risk control device, including:

Data acquisition module for acquiring target data of target merchants;

A risk health data acquisition module, configured to acquire the risk health data of the target merchant according to the target data and the preset risk scoring model;

The resource allocation module is configured to determine a target data range corresponding to the risk health data, and allocate business resources to the target merchant according to a resource allocation rule corresponding to the target data range.

In a third aspect, an embodiment of the present specification provides a server, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, any of the foregoing Method steps.

According to a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps of any of the above-mentioned methods.

The beneficial effects of the embodiments of this specification are as follows:

In the risk control method provided in the embodiment of the present specification, target data of a target merchant is obtained, and according to the target data and a preset risk scoring model, risk health data of the target merchant is obtained; Target data range, and allocate business resources to the target merchant according to resource allocation rules corresponding to the target data range. In the above solution, determining the risk health data of the target merchant through the target data can enable the target merchant to perceive risk more intuitively through the risk health data, and at the same time, allocate business resources to the target merchant based on the risk health data, such as when the risk health data is high Increasing the target merchant's business resources can effectively increase the target merchant's enthusiasm for risk control.

BRIEF DESCRIPTION

By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments, and are not considered to limit the present invention. Furthermore, the same reference numerals are used to denote the same parts throughout the drawings. In the drawings:

FIG. 1 is a flowchart of a risk control method provided by the first aspect of the embodiment of the present specification;

2 is a flowchart of a risk identification method provided by the second aspect of the embodiment of the present specification;

3 is a schematic diagram of a risk assessment display interface provided by an embodiment of this specification;

4 is a schematic diagram of a risk control device provided in a second aspect of an embodiment of this specification;

FIG. 5 is a schematic diagram of a server provided in the third aspect of the embodiments of the present specification.

detailed description

In order to better understand the above technical solutions, the following describes the technical solutions of the embodiments of the present specification in detail through the drawings and specific embodiments. It should be understood that the embodiments of the present specification and the specific features in the embodiments are the The detailed description, rather than the limitation on the technical solutions of this specification, the embodiments of this specification and the technical features in the embodiments can be combined with each other without conflict.

In the first aspect, the embodiment of the present specification provides a risk control method. As shown in FIG. 1, it is a flowchart of the risk control method provided by the embodiment of the present specification.

Step S11: Obtain the target data of the target merchant;

In the embodiment of the present specification, the target merchant may be any merchant that needs to perform risk control, for example, the target merchant may be a bank, service provider, individual merchant, or the like. The target data may be data acquired within a predetermined time, for example, data acquired within one day of the target merchant, or data acquired within 12 decimal places of the target merchant, etc. The target data may be data generated during the operation of the target merchant, or may be the inherent attribute data of the target merchant, such as the target merchant's business license and the nature of the enterprise. The target data can be used for risk assessment of the target merchant. In one embodiment, the target data includes multiple types of data, such as merchant attribute data, transaction data generated by the merchant, etc., which is not limited herein.

Step S12: Obtain the risk health data of the target merchant according to the target data and the preset risk scoring model;

In the embodiment of this specification, the preset risk scoring model can be selected according to actual needs. In one embodiment, the preset risk scoring model is a binary classification model. In another embodiment, the preset risk scoring model is a random forest model. . The preset risk scoring model may be a model constructed in advance using training samples and test samples.

It should be understood that the input of the preset risk scoring model may include multiple modes, for example, the input of the preset risk scoring model may be target data, or may be features extracted according to the target data. The output of the preset risk scoring model is the risk health data of the target merchant. In one embodiment, the risk health data may be a risk health score.

Step S13: Determine the target data range corresponding to the risk health data, and allocate business resources to the target merchant according to the resource allocation rule corresponding to the target data range.

In the embodiment of the present specification, the business resource may be a business resource used by the target merchant to use the business, or a business resource not used by the target merchant but related to the business scope of the target merchant. Depending on the risk health data, the degree of business resource allocation may also be different. In the embodiment of this specification, the risk health data can be divided into different gears. For example, taking the risk health data as the risk health score, for example, the risk health score in the range of 0-60 is classified as the first gear. The risk health score in the range of 60-90 is classified as the second gear, and the risk health score in the range of 90-100 is classified as the third gear. Different resource allocation rules can be set for different gears. Following the above example, the first gear resource allocation rule is to cancel the allocated business resources, and the second gear resource allocation rule is to reduce the allocated business resources. The third-level resource allocation rule is to increase the allocated business resources.

In one embodiment, the target merchant is using the Alipay acquiring service. The business resource of the service may be a rebate resource of the acquiring service. For example, when the target merchant uses the Alipay acquiring service for acquiring, the total amount of acquiring is greater than a threshold At the time, a one-time rebate of 500 yuan to the target merchant. If the target merchant's risk health data is high, you can increase the rebate amount to encourage and affirm the target merchant's current risk health status. If the risk health data of the target merchant is low, the rebate to the target merchant can be cancelled to remind the target merchant to make rectification and control the risk.

In another embodiment, the business related to the target merchant's business scope may be determined based on the business information used by the target merchant, the target merchant's business scope, and other data. For example, the business of the target merchant involves convenience payment, mobile payment, and POS receipt. It can be determined that the business scope of the target merchant is the receipt business. Since the Alipay acquiring business is a business related to the target merchant ’s business scope, when the target merchant does not use the Alipay acquiring business and the target merchant ’s risk health data is high, the target merchant can be provided with a one-month fee-free rate The business resources of Alipay's acquiring business to encourage and affirm the current risk health status of target merchants.

Of course, other business resource allocation methods can also be adopted, such as increasing business resource allocation to target merchants with high risk health data, and not processing business resources of target merchants with low risk health data, which is not limited here.

Optionally, the acquiring target data of the target merchant includes: acquiring data of the target merchant in a target dimension as the target data, and the target dimension includes one or more of the following dimensions: merchant attribute dimension , Merchant value dimension, merchant social behavior dimension and merchant risk dimension.

In the embodiment of the present specification, in order to fully identify the risks of the target merchant, the target data may include data in multiple dimensions. Among them, the data corresponding to the attribute dimension of the merchant may include the merchant qualification, the integrity of the merchant's industrial and commercial information, and whether the merchant is involved in black. The data corresponding to the merchant value dimension may include merchant registration time, merchant transaction data, merchant transaction user data, and so on. The data corresponding to the merchant's social behavior dimension may include the merchant's relationship network data, social evaluation data, and so on. The data corresponding to the merchant risk dimension may include merchant fraud risk transaction data, merchant cash-out risk transaction data, merchant fraudulent transaction risk transaction data, merchant gambling risk transaction data, merchant public opinion risk transaction data, etc.

Optionally, the preset risk scoring model is obtained by acquiring a sample data set, where the sample data set includes black sample data and white sample data, and the black sample data is data corresponding to a merchant who has historically recorded risk records The white sample data is data corresponding to merchants with no historical risk records; according to the sample data set, the preset risk scoring model is obtained.

In the embodiment of the present specification, the sample data set may include training samples and test samples, where the training samples are used for model training, and the test samples are used to verify the reliability of the trained model. The sample data set can be composed of black sample data and white sample data, where the merchants with historical risk records are used as black samples, the historical risk data of the merchants can be used as a set of black sample data, and the merchants with no historical risk records are used as white Sample, the historical data of the merchant can be used as a set of white sample data. It should be noted that the historical record may be a record within a preset time, for example, a record within three months is used as a historical record.

It should be understood that the sample data set may include black sample data corresponding to multiple black sample merchants and white sample data corresponding to multiple white sample merchants. For a set of black sample data or a set of white sample data, it may include the data of the merchant in multiple dimensions, for example, a set of black sample data may include the black sample merchant in the merchant attribute dimension, merchant value dimension, merchant Data under the social behavior dimension and the merchant risk dimension.

After obtaining the sample data set, the model is trained through the sample data set. In one embodiment, after the sample data set is obtained, each group of sample data can be characterized to obtain data features, and then the model training can be performed through the data features. You can also directly use the sample data for model training, which is not limited here. The trained model can be selected according to actual needs. In one embodiment, the model is a binary classification model. The output of the binary classification model is the probability that the input sample is a white sample. This probability value can be used as the risk health of the input sample. data. For example, a set of sample data of a sample is used as the input of a binary classification model. If the output of the binary classification model has a 98% probability that the sample is a white sample, the risk health data of the sample is 98 points. Of course, there are other ways to define risk health data, which is not limited here.

Optionally, the acquiring the risk health data of the target merchant according to the target data and the preset risk scoring model includes: determining the N types of risks currently present in the target merchant according to the target data, N is a positive integer; in the target data, risk data corresponding to each of the N risk types is determined; based on the risk data, a single risk health corresponding to each risk type is determined In total, N single-risk health data are obtained; based on the N single-risk health data and the preset risk scoring model, the risk health data of the target merchant is obtained.

In the embodiment of the present specification, since the target data includes data in multiple dimensions, the target data may include data for evaluating multiple risk types. For example, the target merchant ’s target data includes the merchant ’s relationship network data, social evaluation data, merchant transaction data, and merchant transaction user data. Among them, the merchant ’s relationship network data and social evaluation data can be used to determine the target merchant ’s public opinion risk, merchant transaction Data and merchant transaction user data can be used to determine the capital risk of the target merchant, that is, the target merchant currently has two types of risks, and N is 2.

Further, according to the risk data under each risk type, the single-risk health data for each risk type is determined. The single-risk health data of each risk type can also be calculated according to the model. In one embodiment, for each risk type, a model can be trained correspondingly, and the input of the model can be the risk data under the risk type. The output of is the single-risk health data corresponding to the risk type. Of course, the single-risk health data for each risk type can also be determined in other ways, which is not limited here.

After obtaining single risk health data for each risk type, the risk health data of the target merchant may be obtained according to a preset risk scoring model. In this embodiment, the input of the preset risk scoring model is single risk health data for each risk type, and the output of the preset risk scoring model is the risk health data of the target merchant. Of course, the risk health data of the target merchant can also be obtained by weighting the single risk health data of each risk type, which is not limited here.

Optionally, as shown in FIG. 2, it is a flowchart of a risk identification method provided by an embodiment of the present specification. The method further includes:

Step S21: According to the target data, determine the M types of risk currently existing in the target merchant, M is a positive integer;

Step S22: Determine the risk identification results of each of the M types of risk types, and obtain a total of M types of risk identification results;

Step S23: Send the M types of risk identification results to the target merchant, so that the target merchant takes corresponding risk processing operations according to the M types of risk identification results.

In the embodiment of the present specification, since the target data includes data in multiple dimensions, the target data may include data for evaluating multiple risk types. For example, the target merchant ’s target data includes the merchant ’s relationship network data, social evaluation data, merchant transaction data, and merchant transaction user data. Among them, the merchant ’s relationship network data and social evaluation data can be used to determine the target merchant ’s public opinion risk, merchant transaction The data and merchant transaction user data can be used to determine the capital risk of the target merchant, that is, the target merchant currently has two types of risks, and M is 2.

Further, the risk identification result of each risk type is determined. In one embodiment, the risk identification result may be determined according to the risk data corresponding to each risk type. For example, the financial risk identification results of the target merchant can be determined based on the merchant transaction data and merchant transaction user data. It should be understood that the financial risk identification results can include multiple risk records, such as fraud risk records, gambling risk records, Cheating risk records, etc. Merchant transaction data and merchant transaction user data can carry a risk tag. For example, if the transaction data of the target merchant contains 20 transaction data with a fraud risk tag, then the target merchant ’s financial risk identification result contains 20 fraud risks recording.

After obtaining the risk identification results of various types of risks, the risk identification results can be displayed to the target merchant, as shown in FIG. 3, which is a schematic diagram of the risk assessment display interface shown to the target merchant in this embodiment of the present specification. In this embodiment, the risk assessment display interface may be displayed on the electronic equipment of the target merchant, such as mobile phones, computers, and other devices. The target data is the data collected by the target merchant within a day. Determine the risk health data of the target user according to the target data and display it on the display interface. In this embodiment, the risk health data is the risk health score. As shown in FIG. 3, the target merchant ’s health risk score is 95 points. Based on the target merchant's target data, it is determined that the target merchant has three types of risks, namely, capital risk, account risk, and public opinion risk, that is, M is 3. Among them, the result of capital risk identification is that there are 20 fraud risk records, 15 gambling risk records, and 2 cheating risk records. The result of account risk identification is that there is one record of abnormal fund expenditure. The result of public opinion risk identification is that there is one record of public opinion risk.

In addition, as shown in FIG. 3, for the existing risks, the embodiment of the present specification may also provide one-click risk processing, that is, the immediate processing button displayed after each risk in FIG. When it is detected that the user clicks the button, the risk processing will be automatically executed. Of course, the records that need to be processed or checked by the target merchant will remind the target merchant to execute it by themselves.

Optionally, the allocating business resources to the target merchant based on the risk health data includes: determining the risk health of the target merchant based on the historical risk health data of the target merchant and the risk health data Data curve; when the maximum value of the risk health data curve is smaller than the first target data range, reduce the business resources allocated to the target merchant to remind the target merchant to deal with the risk; in the risk health data When the minimum value of the curve meets the second target data range, increase the service resources allocated to the target merchant.

In the embodiment of the present specification, the risk health data of the target merchant can be calculated and pushed to the target merchant every preset time. Taking the risk health data as the risk health score, for example, the risk health score of the target merchant can be calculated every day, and the risk health score of the day can be pushed to the target merchant. It should be understood that if the risk health scores of the target merchants all maintain a high score within a certain period of time, it indicates that the target merchants have better risk control. If the target merchant's risk health score continues to decrease within a certain period of time, it indicates that the target merchant's risk is very hidden and the target merchant has not been rectified. If the target merchant ’s risk health score starts to be low and then maintains a high score, it indicates that the target merchant has made rectifications to the existing risks and maintained the rectified level.

Therefore, in the embodiment of this specification, the risk health data curve of the target merchant is drawn according to the historical risk health data of the target merchant and the currently acquired risk health data, and then the target merchant's control of the risk is determined according to the trend of the risk health data curve Happening. The historical risk health data can be set according to actual needs. For example, the historical risk health data is the risk health data calculated every day in the previous month, or the risk health data calculated every other day in the previous three months. limited.

In the embodiment of the present specification, the risk change of the target merchant can be distinguished according to the first target data range and the second target data range, and the first target data range and the second target data range can be set according to actual needs. In one embodiment, the first target data range is less than or equal to the first threshold, the second target data range is greater than or equal to the second threshold, and the first threshold is less than or equal to the second threshold. Then, in this embodiment, when the maximum value of the risk health data curve is less than or equal to the first threshold, it corresponds to the condition that the target merchant is at risk and no rectification measures are taken. When the minimum value of the risk health data curve is greater than or equal to the second threshold, it corresponds to the condition that the target merchant has always maintained a lower risk.

Of course, other risk changes can also be included, for example, when the risk health data curve shows an increasing trend, and the risk health data finally maintains a stable state over time, and the risk health data in the non-steady state exists less than or equal to the first When the threshold score and the steady state risk health data are greater than or equal to the second threshold, it corresponds to the condition that the target merchant has a risk and rectifies the risk. The first threshold and the second threshold can be selected according to actual needs, which is not limited here.

In the embodiment of the present specification, in order to encourage the target merchants with higher risk health data to continue to maintain, the business resources allocated to the target merchants can be increased, and to warn the target merchants at risk for rectification, the business resources allocated to the target merchants can be reduced. In addition, when encouraging target merchants, the incentive mechanism can be graded according to the length of time that the higher risk health data is maintained. Similarly, when warning the target merchant, the warning level can also be based on the level of risk health data. Grading.

In one embodiment, the business resource is a rebate resource of the acquiring business. The rebate resource is a one-time rebate of 500 yuan when the total amount of receipts of the target business using the Alipay acquiring business reaches a threshold. When the risk health data curve of the target merchant shows that the target merchant has always maintained a high health score, the rebate resource can be increased to a one-time rebate of 800 yuan to affirm the risk control of the target merchant. When the risk health data curve of the target merchant indicates that the target merchant rectifies the existing risks and keeps the risk health data at a high score after the rectification, the rebate resource can be increased to a one-time rebate of 600 yuan to appropriately target the target Merchants are encouraged, and target merchants are encouraged to continue. When the risk health data curve of the target merchant indicates that the target merchant is at risk and no rectification measures have been taken, it can cancel the rebate resources or close the acquiring business to remind the target merchant to take rectification measures.

It should be understood that, for different situations of different risk health data curves, the allocation rules of business resource allocation can be set in advance, and when the risk health data curve reflects the corresponding situation, the business resource allocation rules in this case can be directly invoked .

In the second aspect, the embodiment of the present specification provides a risk control device, please refer to FIG. 4, including:

The data obtaining module 41 is used to obtain target data of the target merchant;

The risk health data acquisition module 42 is used to acquire the risk health data of the target merchant according to the target data and the preset risk scoring model;

The resource allocation module 43 is configured to adjust the business rules of the target business according to the risk health data, where the target business is a business associated with the target merchant.

In an optional implementation, the data acquisition module 41 is used to:

The data of the target merchant in the target dimension is acquired as the target data, and the target dimension includes one or more of the following dimensions: merchant attribute dimension, merchant value dimension, merchant social behavior dimension, and merchant risk dimension.

In an optional implementation manner, the device further includes:

The sample acquisition module is used to obtain a sample data set, the sample data set includes black sample data and white sample data, the black sample data is data corresponding to a historical risk record merchant, and the white sample data is historical no risk Record the data corresponding to the merchant;

The model obtaining module is used to obtain the preset risk scoring model according to the sample data set.

In an optional implementation manner, the risk health data acquisition module 42 is used to:

According to the target data, determine the N types of risks currently present in the target merchant, where N is a positive integer;

Determining risk data corresponding to each of the N types of risk in the target data;

According to the risk data, determine the single-risk health data corresponding to each of the risk types, and obtain a total of N single-risk health data;

Obtain the risk health data of the target merchant according to the N single risk health data and the preset risk scoring model.

In an optional implementation manner, the device further includes:

The risk type determination module is used to determine the M types of risk currently existing in the target merchant according to the target data, where M is a positive integer;

The risk identification module is used to determine the risk identification result of each of the M types of risk and obtain a total of M types of risk identification results;

The sending module is configured to send the M types of risk identification results to the target merchant, so that the target merchant takes corresponding risk processing operations according to the M types of risk identification results.

In an optional implementation, the resource allocation module 43 is used to:

Determine the risk health data curve of the target merchant according to the historical risk health data of the target merchant and the risk health data;

When the maximum value of the risk health data curve meets the first target data range, reduce the business resources allocated to the target merchant to remind the target merchant to deal with the risk;

When the minimum value of the risk health data curve meets the second target data range, increase the service resources allocated to the target merchant.

Regarding the above device, the specific functions of each module have been described in detail in the embodiments of the risk control method provided by the embodiments of the present invention, and will not be elaborated here.

In the third aspect, based on the same inventive concept as the risk control method in the foregoing embodiment, the present invention also provides a server device, as shown in FIG. 5, including a memory 604, a processor 602, and stored on the memory 604 and capable of processing A computer program running on the processor 602, and when the processor 602 executes the program, the steps of any of the risk control methods described above are implemented.

Among them, in FIG. 5, the bus architecture (represented by the bus 600), the bus 600 may include any number of interconnected buses and bridges, and the bus 600 will include one or more processors and memory 604 represented by the processor 602. The various circuits of the memory are linked together. The bus 600 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art, and therefore, they will not be described further herein. The bus interface 606 provides an interface between the bus 600 and the receiver 601 and the transmitter 603. The receiver 601 and the transmitter 603 may be the same element, that is, a transceiver, providing a unit for communicating with various other devices on a transmission medium. The processor 602 is responsible for managing the bus 600 and general processing, and the memory 604 may be used to store data used by the processor 602 in performing operations.

According to a fourth aspect, based on the inventive concept of the risk control method in the foregoing embodiments, the present invention also provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the risk control method described above Of any method.

This specification is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the specification. It should be understood that each flow and / or block in the flowchart and / or block diagram and a combination of the flow and / or block in the flowchart and / or block diagram may be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processing machine, or other programmable data processing device to produce a machine that enables the generation of instructions executed by the processor of the computer or other programmable data processing device A device for realizing the functions specified in one block or multiple blocks of one flow or multiple flows of a flowchart and / or one block or multiple blocks of a block diagram.

These computer program instructions may also be stored in a computer readable memory that can guide a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including the instruction device, the instructions The device implements the functions specified in one block or multiple blocks in the flowchart one flow or multiple flows and / or block diagrams.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operating steps are performed on the computer or other programmable device to produce computer-implemented processing, which is executed on the computer or other programmable device The instructions provide steps for implementing the functions specified in one block or multiple blocks of the flowchart one flow or multiple flows and / or block diagrams.

Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. In this way, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and equivalent technologies thereof, the present invention is also intended to include these modifications and variations.

Claims

A risk control method, the method includes:

Obtain the target data of the target merchant;

Obtain the risk health data of the target merchant according to the target data and the preset risk scoring model;

Determine a target data range corresponding to the risk health data, and allocate business resources to the target merchant according to resource allocation rules corresponding to the target data range.
According to the risk control method of claim 1, the acquiring target data of a target merchant includes:

The data of the target merchant in the target dimension is acquired as the target data, and the target dimension includes one or more of the following dimensions: merchant attribute dimension, merchant value dimension, merchant social behavior dimension, and merchant risk dimension.
According to the risk control method of claim 2, the preset risk scoring model is obtained by:

Obtain a sample data set, the sample data set includes black sample data and white sample data, the black sample data is data corresponding to a merchant with historical risk records, and the white sample data is data corresponding to a merchant with no historical risk records;

According to the sample data set, the preset risk scoring model is obtained.
The risk control method according to claim 2, wherein obtaining risk health data of the target merchant according to the target data and a preset risk scoring model includes:

According to the target data, determine the N types of risks currently present in the target merchant, where N is a positive integer;

Determining risk data corresponding to each of the N types of risk in the target data;

According to the risk data, determine the single-risk health data corresponding to each of the risk types, and obtain a total of N single-risk health data;

Obtain the risk health data of the target merchant according to the N single risk health data and the preset risk scoring model.
The risk control method according to claim 2, further comprising:

According to the target data, determine the M types of risk currently existing in the target merchant, where M is a positive integer;

Determine the risk identification results of each of the M types of risk types, and obtain a total of M types of risk identification results;

Sending the M types of risk identification results to the target merchant, so that the target merchant takes corresponding risk processing operations according to the M types of risk identification results.
According to the risk control method of claim 1, the determining the target data range corresponding to the risk health data and allocating business resources to the target merchant according to the resource allocation rule corresponding to the target data range includes:

Determine the risk health data curve of the target merchant according to the historical risk health data of the target merchant and the risk health data;

When the maximum value of the risk health data curve meets the first target data range, reduce the business resources allocated to the target merchant to remind the target merchant to deal with the risk;

When the minimum value of the risk health data curve meets the second target data range, increase the service resources allocated to the target merchant.
A risk control device, the device includes:

Data acquisition module for acquiring target data of target merchants;

A risk health data acquisition module, configured to acquire the risk health data of the target merchant according to the target data and the preset risk scoring model;

The resource allocation module is configured to allocate business resources to the target merchant based on the risk health data.
The risk control device according to claim 7, the data acquisition module is configured to:

The data of the target merchant in the target dimension is acquired as the target data, and the target dimension includes one or more of the following dimensions: merchant attribute dimension, merchant value dimension, merchant social behavior dimension, and merchant risk dimension.
The risk control device according to claim 8, the device further comprising:

The sample acquisition module is used to obtain a sample data set, the sample data set includes black sample data and white sample data, the black sample data is data corresponding to a historical risk record merchant, and the white sample data is historical no risk Record the data corresponding to the merchant;

The model obtaining module is used to obtain the preset risk scoring model according to the sample data set.
The risk control device according to claim 8, wherein the risk health data acquisition module is used to:

According to the target data, determine the N types of risks currently present in the target merchant, where N is a positive integer;

Determining risk data corresponding to each of the N types of risk in the target data;

According to the risk data, determine the single-risk health data corresponding to each of the risk types, and obtain a total of N single-risk health data;

Obtain the risk health data of the target merchant according to the N single risk health data and the preset risk scoring model.
The risk control device according to claim 8, the device further comprising:

The risk type determination module is used to determine the M types of risk currently existing in the target merchant according to the target data, where M is a positive integer;

The risk identification module is used to determine the risk identification result of each of the M types of risk and obtain a total of M types of risk identification results;

The sending module is configured to send the M types of risk identification results to the target merchant, so that the target merchant takes corresponding risk processing operations according to the M types of risk identification results.
The risk control device according to claim 7, the resource allocation module is configured to:

Determine the risk health data curve of the target merchant according to the historical risk health data of the target merchant and the risk health data;

When the maximum value of the risk health data curve meets the first target data range, reduce the business resources allocated to the target merchant to remind the target merchant to deal with the risk;

When the minimum value of the risk health data curve meets the second target data range, increase the service resources allocated to the target merchant.
A server includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method according to any one of claims 1-6 are implemented.
A computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps of the method of any of claims 1-6.