WO2020114110A1

WO2020114110A1 - Risk prevention and control method and apparatus for merchant

Info

Publication number: WO2020114110A1
Application number: PCT/CN2019/112126
Authority: WO
Inventors: 郑霖; 陈帅; 程羽; 陈弢; 聂茜倩; 朱江
Original assignee: 阿里巴巴集团控股有限公司
Priority date: 2018-12-04
Filing date: 2019-10-21
Publication date: 2020-06-11
Also published as: TWI714262B; TW202022749A; CN110046781A; CN110046781B

Abstract

Disclosed is a risk prevention and control method for a merchant, the method comprising: where an abnormality of target index data of a merchant is detected, determining a target abnormal parameter corresponding to the target index data (S101); based on correlations between an index, a category and a dimension, acquiring a target category to which a target index corresponding to the target index data belongs, and a target dimension to which the target category belongs (S103); based on the target dimension and the target category, determining at least one linkage category, wherein the at least one linkage category comprises an index that causes the abnormality of the target index data (S105); and based on the target abnormal parameter and the at least one linkage category, carrying out merchant risk prevention and control (S107). By carrying out abnormality detection on indexes related to merchant risk prevention and control, and each time an index of which index data is abnormal is detected, timely carrying out merchant risk prevention and control based on an index abnormality scenario, the method recognizes in advance a risk before merchant risk confirmation, thus improving timeliness of merchant risk prevention and control.

Description

Merchant risk prevention and control method and device

Technical field

This application relates to the field of computer technology, in particular to a method and device for merchant risk prevention and control.

Background technique

With the rapid development of Internet technology, mobile payment is becoming more and more popular, and consumers are more and more accustomed to using third-party payment platforms for convenient mobile payment.

For mobile payment, users and merchants who choose to use third-party payment platforms are the two most important subjects. With the rapid growth of merchants' orders of magnitude, the probability of merchants' risks also increasing. How to prevent and control tens of thousands of merchants in a timely and effective manner has become an urgent problem to be solved.

Therefore, a method of merchant risk prevention and control is urgently needed to improve the timeliness of merchant risk prevention and control.

Summary of the invention

The embodiments of the present application provide a method and device for merchant risk prevention and control, aiming to perform abnormal detection on indicators related to merchant risk prevention and control, and whenever an abnormal index of the indicator data is detected, based on the abnormal situation of the index Merchant risk prevention and control, to realize early identification of risks before merchant risk confirmation, and improve the timeliness of merchant risk prevention and control.

The embodiments of the present application adopt the following technical solutions:

In the first aspect, the embodiments of the present application provide a method for merchant risk prevention and control, including:

In the case that the target indicator data of the merchant is abnormal, determine the target abnormal parameter corresponding to the target indicator data;

Acquiring the target category to which the target indicator corresponding to the target indicator data belongs, and the target dimension to which the target category belongs based on the correspondence between the indicators, categories, and dimensions;

Determine at least one linkage category based on the target dimension and the target category, and the at least one linkage category includes an indicator that causes abnormality of the target indicator data;

Based on the target abnormal parameter and the at least one linkage category, merchant risk prevention and control is performed.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application determines at least one linkage category based on the target dimension and the target category, including:

Acquiring at least one linked dimension associated with the target dimension;

Under the at least one linkage dimension, the at least one linkage category associated with the target category is obtained.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on the target abnormal parameter and the at least one linkage category, performing merchant risk prevention and control includes:

Under the at least one linkage category, obtain a first set of indexes corresponding to the abnormal index data;

Based on the target abnormal parameter, the at least one linkage category, and the first indicator set, merchant risk prevention and control is performed.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on the target abnormal parameter, the at least one linkage category, and the first indicator set, performing merchant risk prevention and control includes:

Based on the at least one linkage category, determine an abnormal cause that causes abnormality in the target indicator data;

Determining a first parameter set corresponding to the first index set, each abnormal parameter in the first parameter set corresponds one-to-one with index data of each index in the first index set;

Based on the target abnormal parameter, the first index set and the first parameter set, a second index set and a second parameter set are determined, each abnormal parameter in the second parameter set and the second index set The indicator data of each indicator corresponds to each other;

Based on the abnormal reason, the second indicator set and the second parameter set, merchant risk prevention and control is performed.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on the at least one linkage category, determines an abnormal cause that causes abnormality in the target indicator data, including:

Receive category selection instructions;

In the at least one linkage category, determine a target linkage category corresponding to the category selection instruction;

The cause of the abnormality is determined according to the target linkage category.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on the target abnormal parameter, the first indicator set and the first parameter set, determining the second indicator set and the second parameter set includes: :

Determine the difference between the target abnormal parameter and each abnormal parameter in the first parameter set;

When the difference is within a preset threshold range, constructing the second parameter set based on the abnormal parameter corresponding to the difference;

The second indicator set is constructed based on indicators corresponding to each abnormal parameter in the second parameter set.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on the abnormal reason, the second indicator set, and the second parameter set, performing merchant risk prevention and control includes:

In the second set of indicators, obtain a risk prevention and control indicator that matches the cause of the abnormality;

In the second parameter set, obtain risk prevention and control parameters, which correspond to the risk prevention and control indicators in one-to-one correspondence;

Based on the risk prevention and control indicators and the risk prevention and control parameters, carry out merchant risk prevention and control.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on the risk prevention and control indicators and the risk prevention and control parameters, carries out merchant risk prevention and control, including:

Acquiring the risk prevention and control strategy corresponding to the risk prevention and control index when the risk prevention and control parameter is greater than the abnormal parameter threshold;

Perform merchant risk prevention and control based on the risk prevention and control strategy.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, before performing merchant risk prevention and control, the method further includes:

Obtain historical risk indicators and corresponding historical abnormal parameters.

In the case where the risk prevention and control indicator has a missing indicator compared with the historical risk indicator, the initial model is trained based on the first training sample to obtain the first merchant risk identification model;

Deploy the first merchant risk identification model for merchant risk identification and output the risk identification results;

Obtain a risk prevention and control strategy corresponding to the risk identification result to carry out risk prevention and control based on the risk prevention and control strategy;

Wherein, the missing indicator is included in the historical risk indicator, and the first training sample includes the risk prevention and control indicator, the risk prevention and control parameter, the missing indicator and the missing abnormal parameter corresponding to the missing indicator .

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on the first training sample, trains the initial model to obtain the first merchant risk identification model, including:

Determine a risk identification threshold based on the risk prevention and control parameters and the missing anomaly parameters;

Generating an input vector of the initial model based on the risk prevention and control indicator and the missing indicator;

Input the input vector into the initial model to obtain the output of the initial model;

Adjust the parameters of the initial model according to the gap between the output of the initial model and the risk identification threshold;

Repeat the above steps until the gap meets the preset condition to obtain the first merchant risk identification model.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on the risk prevention and control indicators and the risk prevention and control parameters, performing risk prevention and control on the merchant includes:

When there is no missing indicator compared with the historical risk indicator, based on the second training sample, the initial model is trained to obtain a second merchant risk identification model;

Deploy the second merchant risk identification model for merchant risk identification and output the risk identification results;

Wherein, the missing indicator is included in the historical risk indicator, and the second training sample includes the second risk prevention and control indicator and the second risk prevention and control parameter.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on the second training sample, trains the initial model to obtain a second merchant risk identification model, including:

Determine the risk identification threshold based on the risk prevention and control parameters;

Generating an input vector of the initial model based on the risk prevention and control index;

Repeat the above steps until the gap meets the preset condition to obtain the second merchant risk identification model.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, the method further includes:

Based on the time-series anomaly detection model, detecting whether the merchant's target index data is abnormal;

When the detection result indicates that the target index data is abnormal, it is determined that the target index data is abnormal.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, based on a time series anomaly detection model, detects whether the merchant's target indicator data is abnormal, including:

Determine a plurality of time-series measured values corresponding to the target indicator data in the current period;

Based on the time-series prediction value and the plurality of time-series measured values, calculating a plurality of time-series abnormal values corresponding to the plurality of time-series measured values;

When the average value of the plurality of time-series abnormal values is greater than or equal to a preset value, it is determined that the detection result indicates that the target index data is abnormal;

In the case where the average value of the plurality of time-series abnormal values is less than the preset value, it is determined that the detection result indicates that the target index data is normal.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application, the step of determining the timing outliers includes:

Determining the time difference between the time-series measured value and the time-series prediction value;

Calculate the ratio between the timing difference and the timing prediction value to obtain the timing outlier.

Optionally, the merchant risk prevention and control method provided in the first aspect of the present application includes at least one of the following:

The dimension includes at least one of a geographic dimension, a merchant type dimension, and a payment interface dimension;

The category includes at least one of a merchant registration category and a merchant operation category.

The merchant registration category includes at least one of the following indicators: the nature of the merchant, maintenance records, business content, and industry;

The merchant operation category includes at least one of the following indicators: transaction amount, number of complaints, product quality, service quality, and payment interface usage.

In a second aspect, an embodiment of the present application further provides a merchant risk prevention and control device, including:

The first determining module is used to determine the target abnormal parameter corresponding to the target index data when the target index data of the merchant is abnormal;

An obtaining module, configured to obtain the target category to which the target indicator corresponding to the target indicator data belongs and the target dimension to which the target category belongs based on the correspondence between the indicators, categories and dimensions;

A second determination module, configured to determine at least one linkage category based on the target dimension and the target category, and the at least one linkage category includes an indicator that causes abnormality of the target indicator data;

The processing module is configured to perform merchant risk prevention and control based on the target abnormal parameter and the at least one linkage category.

In a third aspect, an embodiment of the present application further provides an electronic device, including:

Processor; and

A memory arranged to store computer-executable instructions, which when executed, causes the processor to perform the following operations:

According to a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are electronically including a plurality of application programs When the device is executed, the electronic device is caused to perform the following operations:

The at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects:

In the embodiments of the present application, indicators for merchant risk prevention and control are comprehensively set from different dimensions, and the indicators are classified to obtain corresponding categories, and the correspondence between indicators, categories, and dimensions is established to detect the existence of indicator data. For abnormal target indicators, based on the corresponding relationship, all linkage categories to which the index that caused the abnormal target data belonged are obtained, and then merchant risk prevention and control are carried out based on the obtained linkage categories and target abnormal parameters that characterize the abnormality of the target index data. Therefore, by carrying out anomaly detection on the indicators related to merchant risk prevention and control, and whenever an abnormal indicator is detected in the indicator data, the merchant risk prevention and control will be carried out in time based on the anomaly of the indicator, so that the risk of the merchant before the risk confirmation is identified in advance and improved Timeliness of merchant risk prevention and control.

BRIEF DESCRIPTION

The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The schematic embodiments and descriptions of the present application are used to explain the present application and do not constitute an undue limitation on the present application. In the drawings:

FIG. 1 is a schematic flowchart of a merchant risk prevention and control method provided in an embodiment of this application;

2 is a schematic diagram of the flow of merchant risk prevention and control information provided in an embodiment of the present application;

3 is a schematic structural diagram of a merchant risk prevention and control device provided in an embodiment of this application;

4 is a schematic structural diagram of an electronic device provided in an embodiment of this application.

detailed description

In order to make the purpose, technical solutions and advantages of the present application more clear, the technical solutions of the present application will be described clearly and completely in conjunction with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

With the popularity of mobile payment methods, people are more and more accustomed to using third-party payment platforms for convenient mobile payment. For mobile payment, users and merchants who choose to use third-party payment platforms are the two most important subjects, and these two groups have a positive influence on each other: the more merchants provide the third-party payment platform The scenarios and services of mobile payment will actively encourage more users to use the third-party payment platform. On the contrary, the continuous increase in the number of users will also drive the continuous increase in the number of merchants.

However, as the number of merchants continues to grow, the proportion of problematic merchants will also increase. When the problematic merchants engage in fraud, gambling, cashing, and other illegal and prohibited activities, they will bring corresponding risks. The interests of the merchants and the normal operation of the merchants cause harm. Therefore, how to timely and effectively identify and prevent and control tens of thousands of merchants has become an urgent problem to be solved. Moreover, considering that merchants' risk often involves large amounts of funds, it is easy to attract the attention of public opinion and regulatory authorities. Therefore, the timeliness of merchants' risk prevention and control is particularly important.

In order to achieve effective prevention and control of merchant risks, the following prevention and control schemes can be used:

(1) The prevention and control scheme based on the merchant strategy requires the strategy operator to extract the risk characteristics through manual trial and analysis of the case, and then configure the online prevention and control strategy based on the extracted risk characteristics to allow the merchant to access beforehand and in the event Conduct layer-by-layer prevention and control in transactions and ex-post management.

(2) The prevention and control scheme based on the risk detection model needs to analyze and design the corresponding risk characteristics for specific merchant risks, such as fraud, gambling, cash out and other illegal and prohibited behaviors, and then establish a risk detection model based on the risk characteristics. Furthermore, based on the risk value output by the risk detection model, risk prevention and control can be carried out in combination with corresponding prevention and control strategies.

(3) The prevention and control plan based on user complaints, when implementing the prevention and control plan, mainly review and determine the merchants with user complaints, punish the corresponding risk merchants after verifying the risks, and punish the risk merchants together Of other merchants.

However, the above-mentioned merchant risk prevention and control schemes generally have the following defects:

(1) In the prevention and control scheme based on merchant strategy, not only does it require more manpower to process and analyze cases to extract risk characteristics, but the cases that are tried and analyzed are generally directed at the risks that have occurred. When risks occur, it is necessary to re-examine and analyze the case and extract risk characteristics to reconfigure the prevention and control strategy. Obviously, the timeliness of risk prevention and control will be reduced. In addition, the prevention and control effects of the new strategy often need to be available after the strategy is launched. Make an evaluation.

(2) In the prevention and control scheme based on the risk detection model, when establishing the risk detection model, it is necessary to use the black and white samples in the historical data to determine the variables in the input risk detection model, and then train the online model, the modeling period is relatively long, and When the risk detection model goes online, its ability to identify merchant risks that have not been seen in history is limited, and there will be a decline in model detection capabilities. Therefore, it is necessary to periodically retrain the model, which obviously affects the timeliness of risk prevention and control.

(3) In the prevention and control scheme based on user complaints, since the scheme is an afterthought prevention and control, not only the merchant risk has occurred, but also in the case that the merchant risk hurts the interests of users, users are required to take the initiative to complain, and then Only after qualitative assessment of merchants' risks based on user complaints can the corresponding management of such merchants' risks be controlled, which is not timely.

Therefore, in practical application scenarios, the risk prevention and control results of the above prevention and control solutions often cannot meet the actual needs of merchants' risk prevention and control, and affect the user experience.

In view of this, the embodiments of the present application provide a merchant risk prevention and control solution, which can identify merchant risks in advance, realize the dynamic prevention and control of merchant risks, improve the timeliness of merchant risk prevention and control, and have strong risk dynamics Adversarial ability, suitable for different types of merchant risks, good versatility.

The technical solutions provided by the embodiments of the present application will be described in detail below in conjunction with the drawings.

Referring to FIG. 1, an embodiment of the present application provides a method for merchant risk prevention and control. The method may include the following steps:

S101: In the case that the target indicator data of the merchant is abnormal, determine the target abnormal parameter corresponding to the target indicator data.

S103: Acquire the target category to which the target indicator corresponding to the target indicator data belongs, and the target dimension to which the target category belongs, based on the correspondence between the indexes, categories, and dimensions.

It can be understood that in the embodiment of the present application, it is necessary to perform abnormal detection on the target indicator data of the merchant, and when an abnormality is detected in the target indicator data, on the one hand, it needs to be based on the correspondence relationship between the pre-established indicators, categories and dimensions, Find and acquire the target category to which the target index corresponding to the target index data with an exception belongs and the target dimension to which the target category belongs, on the other hand, it is necessary to determine the target abnormal parameter corresponding to the target index data.

Optionally, the following embodiments can be used to realize anomaly detection of the merchant's target indicator data and determine target anomaly parameters:

Based on the time-series anomaly detection model, it is detected whether the target indicator data of the merchant is abnormal.

Optional, time series anomaly detection models include ARIMA model (Autoregressive Integrated Moving Average model), HOLT-WINTERS model (exponential smoothing model) and LSTM (Long Short-Term Memory, long-short-term memory network) model One of them, of course, can also use other models that can implement anomaly detection.

Optionally, based on the time series anomaly detection model, the step of detecting whether the target indicator data of the merchant is abnormal may include:

Determine the measured values of multiple time series corresponding to the target indicator data in the current period;

Based on the time series predicted value and multiple time series measured values, multiple time series outliers corresponding to the multiple time series measured values are calculated;

In the case where the average value of multiple timing abnormal values is greater than or equal to the preset value, it is determined that the detection result indicates that the target indicator data is abnormal;

In the case where the average value of multiple time series abnormal values is less than the preset value, it is determined that the detection result indicates that the target index data is normal.

Optionally, the step of determining the timing outliers may include:

Determine the time difference between the time series measured value and the time series predicted value;

Calculate the ratio between the time difference and the time prediction value to get the time sequence outlier.

Optionally, the target index data can reflect the value of the target index as a parameter variable, and the target abnormal parameter can reflect the abnormal degree when the target index data is abnormal. Preferably, the target abnormal data corresponding to the target index data can be multiple The average value of time series outliers.

Among them, the time series measured value can reflect the actual detection value of the target indicator data at a detection time point in the current period, the time series prediction value can reflect the target index data according to the normal estimate of the estimated value at the detection time point, while the time series actual measurement The time difference between the value and the time series prediction value can reflect the gap between the actual measurement value and the normal estimated value of the target indicator data at the detection time point, and the time difference can measure the target indicator data in the detection Whether the detection value at the moment changes.

It can be understood that the values of the above current time period and the preset value can be determined based on the experience of the merchant performing the anomaly detection and the actual situation of the target indicator; for example, the average value of multiple time series anomaly values that can be detected within 3 consecutive days is greater than Or equal to the preset value, it is determined that the detection result indicates that the target index data is abnormal.

Further, when the detection result indicates that the target index data is abnormal, it is determined that the target index data is abnormal.

Optionally, when establishing the correspondence between indicators, categories, and dimensions, you can comprehensively set indicators for merchant risk prevention and control from different dimensions, and classify the indicators to get the corresponding categories. When dimensioning, you can consider multi-level settings from coarse to fine and/or from upper to lower angles to make the design of indicators more comprehensive and more specific.

The dimension may include at least one of a geographic dimension, a merchant type dimension, and a payment interface dimension.

For example, the regional dimension from thick to thin can include autonomous regions, provinces, cities, flag counties, and streets. For another example, the merchant type dimension from superior to inferior may include: service providers, business development specialists, offline merchants, merchant transactions, etc. For another example, the payment interface dimension may include whether to sign a third-party payment interface, whether the third-party payment interface is Alipay, and so on.

It can be understood that, in the embodiments of the present application, considering that the merchants are distributed in different regions, their operations and other aspects may be affected by certain regional characteristics, and the different types of merchants’ respective operations and other aspects will also be different. , And different payment interfaces are adopted, their fund transfer authority, operation process and other aspects will also be different, so there may be differences in the risks of merchants. Therefore, you can set up different dimensions for merchants to detect anomalies to achieve More comprehensive merchant risk monitoring.

In addition, the category under the dimension may include at least one of a merchant registration category and a merchant operation category.

Optionally, the merchant registration category may include at least one of the following indicators: the nature of the merchant, maintenance records, business content, and industry; the merchant operation category may include at least one of the following indicators: transaction amount, number of complaints, product quality, service quality and Payment interface usage.

Among them, the nature of merchants can include individual merchants and corporate merchants; dimension records include business development specialists (BD, Business Development) to regularly record the merchant's dimensions, and the maintenance content can include monitoring the merchant's tax payment and monitoring whether the merchant's business license expires Whether the due date is updated, monitor whether the merchant handles customer complaints in a timely manner, etc. BD generates corresponding maintenance records after each dimension of the merchant; the business content can refer to the merchandise operated by the merchant when the merchant is registered, for example, the fruit shop’s business content can be It is fruit, and the business content of the vegetable shop can be vegetables; the industry to which the merchant belongs can refer to the industry filled in when the merchant is registered, or can refer to the industry derived from the business content of the merchant; the transaction amount can include the merchant's transaction within the set time The total amount; the number of complaints may include the number of complaints by the merchant in the most recent cycle; the usage of the payment interface may include whether the merchant's third-party payment interface is used by other merchants, and the geographic location information of the third-party payment interface to complete the transaction.

Of course, the setting of dimensions, categories, indicators, etc. for risk prevention and control of merchants is not limited to the above, and can be adjusted accordingly according to specific and actual prevention and control requirements.

In the embodiment of the present application, anomaly detection can be performed on specific indicators under a specified category in a specified dimension to achieve the capture of target indicator data that has changed in time series during the risk prevention and control process of the merchant; all indicators designed in advance can also be captured Anomaly detection is carried out to capture the target indicator data that has changed in time series during the risk prevention and control process of merchants. Among them, the process of anomaly detection may be performed periodically or continuously, and in the case of periodic anomaly detection, the polling period for different indicators may be the same or different.

S105: Determine at least one linkage category based on the target dimension and the target category, and the at least one linkage category includes indicators that cause abnormal target indicator data.

It can be understood that after locking the corresponding target dimension and target category based on the target indicator that detects an abnormality, at least one linkage category is further obtained based on the target dimension and target category, and the at least one linkage category includes data that can cause the target indicator to be abnormal Indicators, so as to find out related abnormal indicators more comprehensively.

Optionally, the step of determining at least one linkage category based on the target dimension and the target category may include:

Obtain at least one linked dimension associated with the target dimension;

Under at least one linkage dimension, obtain at least one linkage category associated with the target category.

It can be understood that, in the embodiments of the present application, at least one linkage category can be scrolled up and down to search for the correlation between the dimensions and the associations between the categories and the categories to more comprehensively find relevant abnormal indicators. Optional The relationship between dimensions and dimensions, categories and categories can be recorded and maintained in the correspondence between indicators, categories and dimensions, or the association between dimensions and dimensions, categories and categories can be established and maintained separately.

Understandably, the linkage dimension may refer to a dimension that has an association relationship with the target dimension to which the target index corresponding to the abnormal target index data belongs, and the linkage category may refer to the association category to the target category to which the target index corresponding to the abnormal target index data belongs to Category, each linkage category is subordinate to the corresponding linkage dimension, and all indicators under each linkage category have indicators that can cause the target indicator data to be abnormal, in other words, each indicator data corresponding to each indicator under each linkage category can cause Indicator data with abnormal target indicator data.

After determining the target abnormal parameter corresponding to the target indicator data and obtaining at least one linkage category, merchant risk prevention and control can be performed accordingly, that is, the following steps are performed:

S107: Perform merchant risk prevention and control based on target abnormal parameters and at least one linkage category.

Optionally, this step S107 may include:

Under at least one linkage category, obtain the first index set corresponding to the abnormal index data;

Based on the target abnormal parameters, at least one linkage category and the first indicator set, carry out merchant risk prevention and control.

It can be understood that based on at least one linkage category associated with the target index, anomaly detection can be performed on the index data corresponding to the index under the at least one linkage category, and optionally, anomaly detection can be performed on the index data within a specified time period to Improve the degree of association with abnormal target index data, improve the accuracy and reliability of index acquisition in the first index set, and use the above-mentioned similar detection scheme for detecting target index data for abnormality to at least one linkage category Anomaly detection is performed on the index data under, that is, whether the index data corresponding to the index under at least one linkage category is abnormal based on the time-series anomaly detection model, and when the detection result indicates abnormality, it is determined that the index under at least one linkage category corresponds to There is an abnormality in the indicator data of, and at least one indicator corresponding to the at least one indicator data that has an anomaly can be formed into a first indicator set for use in merchant risk prevention and control together with the target anomaly parameter and at least one linkage category.

Optionally, based on the target abnormal parameter, at least one linkage category, and the first indicator set, merchant risk prevention and control may specifically include the following steps:

Based on at least one linkage category, determine the abnormal cause of the abnormal target data.

It can be understood that in the embodiment of the present application, the cause of the abnormality of the target indicator data may be determined according to at least one linkage category. Optionally, based on the user's selection, the linkage category that the user is more concerned about in the at least one linkage category is determined as the cause of abnormality , Which can include the following steps:

Receive category selection instructions;

In at least one linkage category, determine the target linkage category corresponding to the category selection instruction;

According to the target linkage category, determine the cause of the abnormality.

Of course, in addition to the user's selection, the cause of the abnormality can be determined based on at least one linkage category in other ways, such as setting a category screening rule to automatically filter out categories that meet the requirements of the rule in at least one linkage category based on the category screening rule Abnormal causes, etc.

Further, a first parameter set corresponding to the first index set is determined, and each abnormal parameter in the first parameter set has a one-to-one correspondence with the index data of each index in the first index set.

It can be understood that, for at least one index in which the index data is abnormal under the linkage category, the abnormal parameter of each index data can be determined accordingly, so that the first parameter set corresponding to the first index set can be obtained, wherein the determination of each abnormal parameter can be Refer to the above embodiment for determining the target abnormal parameter, which will not be repeated here.

Further, based on the target abnormal parameter, the first index set and the first parameter set, the second index set and the second parameter set are determined, and each abnormal parameter in the second parameter set is one-to-one with the index data of each index in the second index set correspond.

In the embodiment of the present application, the first indicator set and the first parameter set may be converted into the second indicator set and the second parameter set based on the target abnormal parameter, so as to achieve reasonable control of the strength of merchant risk prevention and control, where The second indicator set includes at least one indicator in the first indicator set, and the second parameter set includes at least one abnormal parameter in the first parameter set.

Optionally, the process of determining the second indicator set and the second parameter set may include the following steps:

When the difference is within a preset threshold range, construct a second parameter set based on the abnormal parameter corresponding to the difference;

The second index set is constructed based on the indexes corresponding to the abnormal parameters in the second parameter set.

In this embodiment, the abnormal parameters whose abnormality level indicated in the first parameter set is above the abnormality level corresponding to the target index data (ie, target abnormal parameter) can be classified into the second parameter set, that is, when the first parameter set When the difference between the abnormal parameter and the target abnormal parameter is within a certain specified range, the abnormal parameter is classified into the second parameter set, and then the indicators in the first index set corresponding to each abnormal parameter in the second parameter set are classified as The second set of indicators.

Further, the steps of merchant risk prevention and control based on the cause of abnormality, the second indicator set and the second parameter set may be performed. Optional, may include:

In the second set of indicators, obtain the risk prevention and control indicators, which match the abnormality causes;

In the second parameter set, the risk prevention and control parameters are obtained, and the risk prevention and control parameters correspond to the risk prevention and control indicators one-to-one;

Based on risk prevention and control indicators and risk prevention and control parameters, carry out merchant risk prevention and control.

It can be understood that, after determining the cause of the abnormality based on the distribution of the category (that is, at least one linkage category) in which the indicator data has abnormality, the risk prevention and control index that matches the cause of the abnormality can be obtained in the second index set, and the Obtain the risk prevention and control parameters corresponding to the aforementioned risk prevention and control indicators that match the cause of the abnormality in the two parameter sets, so as to carry out more reasonable merchant risk prevention and control.

Based on the risk prevention and control indicators and risk prevention and control parameters described above, merchant risk prevention and control can be achieved through the following specific embodiments:

In a specific embodiment 1, a solution for merchant risk prevention and control may include:

When the risk prevention and control parameters are greater than the abnormal parameter threshold, obtain a risk prevention and control strategy corresponding to the risk prevention and control indicators;

Carry out merchant risk prevention and control based on risk prevention and control strategies.

It can be understood that when the value of the risk prevention and control parameter obtained based on the abnormal parameter and the cause of the abnormality in the second parameter set is greater than the abnormal parameter threshold, it means that the abnormality of the indicator is sufficiently large. In this case, you can directly deploy the corresponding risk prevention and control The strategic way to carry out merchant risk prevention and control in a timely manner is to improve the timeliness of merchant risk prevention and control while detecting abnormal indicators in a timely manner.

Optionally, risk prevention and control strategies include fines for merchants, return of fraud gains, order to suspend business operations for rectification, and order to improve operational qualifications, etc., to prohibit merchants from performing any risk-causing actions.

In the second specific embodiment, the merchant risk identification model can be trained to update the merchant risk identification model based on the latest indicator abnormality, while detecting the indicator abnormality in a timely manner, and improving the timeliness of merchant risk prevention and control.

Optionally, before carrying out merchant risk prevention and control, in the embodiment of the present application, a step of obtaining historical risk indicators and corresponding historical abnormal parameters needs to be performed, and then the historical risk indicators are compared with the current risk prevention and control indicators To implement corresponding merchant risk prevention and control schemes based on the results of different comparisons, to avoid situations where historical risks are missed, and to achieve more comprehensive merchant risk prevention and control coverage.

Optionally, based on the risk prevention and control indicators and risk prevention and control parameters, the merchant risk prevention and control scheme may include the following two aspects:

In the first aspect, when the risk prevention and control indicators are missing indicators compared with the historical risk indicators, the initial model is trained based on the first training sample to obtain the first merchant risk identification model;

Among them, the missing indicators are included in the historical risk indicators, and the first training sample includes risk prevention and control indicators, risk prevention and control parameters, missing indicators, and missing abnormal parameters corresponding to the missing indicators.

It can be understood that when the risk prevention and control indicators determined based on the current abnormal situation of the indicators are compared with historical risk indicators, there is a situation where the coverage of the indicators to be prevented and controlled is not complete. For the timeliness of merchant risk identification, that is, to cover the possible new Risk, and to ensure that historical risk indicators that have been confirmed are not missed during risk identification, you can consider as the first merchant based on the latest risk prevention and control indicators and their corresponding risk prevention and control parameters, missing indicators and their corresponding missing abnormal parameters The training samples of the risk identification model, so that the latest first merchant risk identification model trained based on this can identify historical and latest merchant risks, and has good timeliness.

Optionally, the above initial model may be an untrained original model constructed using the isolated forest iForest algorithm or the symbolic regression (Symbolic Regression) algorithm, or a merchant risk identification model that is being trained based on historical risk indicators and other data. .

Optionally, based on the first training sample, the process of training the initial model to obtain the first merchant risk identification model may include:

Determine risk identification threshold based on risk prevention and control parameters and missing abnormal parameters;

Based on the risk prevention and control indicators and missing indicators, the input vector of the initial model is generated;

Input the input vector into the initial model to get the output of the initial model;

Adjust the parameters of the initial model according to the gap between the output of the initial model and the threshold of risk identification;

Repeat the above steps until the gap meets the preset conditions to obtain the first merchant risk identification model.

It can be understood that, considering that the risk prevention and control parameters and the missing abnormal parameters reflect the abnormal degree of the corresponding index data, the risk identification threshold can be determined based on this, so that the risk identification standard is reliable and accurate.

Further, deploy the first merchant risk identification model for merchant risk identification, and output the risk identification results;

Obtain a risk prevention and control strategy corresponding to the risk identification results to carry out risk prevention and control based on the risk prevention and control strategy.

It can be understood that when the current risk prevention and control indicators have missing indicators compared with the historical risk indicators, when the merchant risk identification model is trained, it is necessary to take into account some of the missing historical indicators and their corresponding abnormal parameters.

It should be noted that, based on the above training samples, the training set and the verification set during model training can be determined. Optionally, the number of samples in the training set and the verification set can be flexibly allocated. For example, 70% of the training samples are used as the training set to train the merchant risk identification model, and the remaining 30% of the training samples are used as the verification set to verify the merchant risk identification. Whether the output of the model meets the requirements in order to realize the model effect evaluation. Optionally, risk prevention and control strategies include fines for merchants, return of fraud gains, order to suspend business operations for rectification, and order to improve operational qualifications, etc., to prohibit merchants from performing any risk-causing actions.

In the second aspect, when there is no missing indicator compared with the historical risk indicator, based on the second training sample, the initial model is trained to obtain the second merchant risk identification model;

Among them, the missing indicator is included in the historical risk indicator, and the second training sample includes the second risk prevention and control indicator and the second risk prevention and control parameter.

It can be understood that when the risk prevention and control indicators determined based on the current abnormal situation of the indicators are compared with historical risk indicators, and there is no incomplete coverage of the indicators to be controlled, the latest and comprehensive risk prevention and control indicators may be considered. The corresponding risk prevention and control parameters are used as training samples for the second merchant risk identification model, so that the latest second merchant risk identification model based on this training can identify historical and latest merchant risks, and has good timeliness.

Optionally, based on the second training sample, the process of training the initial model to obtain the second merchant risk identification model may include:

Determine risk identification threshold based on risk prevention and control parameters;

Based on the risk prevention and control indicators, generate the input vector of the initial model;

Repeat the above steps until the gap meets the preset conditions to obtain the second merchant risk identification model.

It should be noted that, based on the above-mentioned second training sample, the training set and the verification set during model training can be determined. Optionally, the number of samples in the training set and the verification set can be flexibly allocated. For example, 70% of the training samples are used as the training set to train the second merchant risk identification model, and the remaining 30% of the training samples are used as the verification set. Whether the output of the two merchants' risk identification model meets the requirements in order to realize the model effect evaluation

Further, deploy a second merchant risk identification model for merchant risk identification, and output the risk identification results;

It can be understood that when the current risk prevention and control indicators are not missing from the historical risk indicators, the merchant risk identification model training can be directly performed based on the risk prevention and control indicators and their corresponding risk prevention and control parameters.

In summary, in the embodiments of the present application, indicators for merchant risk prevention and control are comprehensively set from different dimensions, and the indicators are classified to obtain corresponding categories, and the correspondence between indicators, categories, and dimensions is established to When an abnormal target indicator of the indicator data is detected, based on the corresponding relationship, all linkage categories to which the indicator that caused the abnormal target indicator data belongs to are obtained, and then the merchant is conducted based on the obtained linkage category and the target abnormal parameter characterizing the abnormality of the target indicator data. Risk prevention and control. Therefore, by carrying out anomaly detection on the indicators related to merchant risk prevention and control, and whenever an abnormal indicator is detected in the indicator data, the merchant risk prevention and control will be carried out in time based on the anomaly of the indicator, so that the risk of the merchant before the risk confirmation is identified in advance and improved Timeliness of merchant risk prevention and control.

The merchant risk prevention and control method described in the embodiments of the present application can also be displayed through the information flow shown in FIG. 2.

(1) Abnormality detection (that is, abnormality detection), that is, monitoring where abnormality occurs.

Reasonable dimension division and indicator design can be adopted, such as dividing dimensions according to contract information, merchant type, payment institution, etc., and separately designing indicators in the account opening, transaction, operation and other aspects of the merchant, and classifying the indicators, and testing over time The time series change of the indicator in each dimension or specific dimension, according to the difference between the detected time series actual value and the time series forecast value to determine whether the indicator has changed, if there is a change, the target change category to which the target change indicator belongs is output The abnormality dimension and the target abnormality degree corresponding to the target abnormality index (that is, the target abnormality parameter).

Among them, the degree of abnormality of the indicator can be calculated as follows:

Degree of change = (time series actual value-time series predicted value) / time series predicted value.

(2) Moving down and scrolling up, that is, looking for the reason and related index of the transaction.

Based on the above output target transaction category and target transaction dimension, drill down and/or scroll up the related index data and detailed index data under the related dimension, and determine the appropriate prevention and control strength according to the above output target transaction degree. The stricter the prevention and control measures are, the detailed index data under related categories and related dimensions are filtered to find out all related transaction indicators, and the abnormal degree of indicators is obtained based on the same method as above, and at the same time, based on each transaction indicator The distribution of the category can determine the cause of the transaction (ie, the cause of the abnormality) to output the reason for the transaction, the list of transaction indicators that match the reason for the transaction, and the degree of the corresponding transaction.

Optionally, when outputting the index list and the corresponding transaction degree, the identified historical risk indicators and the transaction degree can be considered to avoid missing risks.

(3) Transaction response

The list of transaction indicators based on the above output and the corresponding index transaction degree, and the use of isolated forest iForest algorithm, symbolic regression algorithm, etc. to quickly build a model, and on the basis of the above transaction detail data model evaluation, to obtain the model's estimated effect, output risk Identify the model and recommend risk prevention and control strategies.

(4) Risk prevention and control

The above-mentioned risk identification model is output to the risk prevention and control platform for model deployment to realize the assessment of the true results of the model, and the risk prevention and control strategy can be matched with the results to quickly prevent and control new risks.

For example, using the city dimension as the main dimension to detect transaction abnormalities or anomaly detection for merchants, when the data corresponding to the indicator B of city A is perceived to be abnormal, compared with the normal time-series forecast value, the transaction has occurred for many consecutive days, and the average transaction When the degree reaches a certain value, the indicator category C to which the indicator B belongs, the average transaction degree corresponding to the data of the indicator B, and the city dimension A are output.

Further, based on the pre-established relationships among indicators, categories, and dimensions, find and determine other city dimensions, flag county dimensions, and town dimensions that are associated with city dimension A. For example, in city A, it is perceived that there is a chain of merchants in the region. , Branches or service providers, etc., and find other categories associated with category C, such as the corresponding category of the merchant corresponding to other regions, etc., you can use the index data of the indicators under each category to determine the transaction Excavate, find all relevant transaction indicators, and determine the reason for the transaction based on the determined category, that is, the distribution of the relevant transaction indicators, and then carry out model training and construction, model evaluation, and prevention and control according to the indicators that match the reason for the transaction and the corresponding degree of transaction Strategy recommendation, etc., to achieve merchant risk prevention and control.

From the perspective of the whole process, abnormalities can be quickly and timely discovered through transactional sensing, and the reason for rapid positioning can be quickly located through transactional down-drilling and scrolling, output effective transactional indicators, model training and risk prevention and control, to ensure the prevention and control Timeliness, in addition, the transfer of risks will cause changes in other indicators, which can dynamically capture risks and enhance dynamic confrontation. It can be seen that the entire system does not pay attention to specific risks, only focuses on changes. Any risk in any situation can be detected and timely as long as it causes changes. Prevention and control, specific and very versatile.

It can be seen that the merchant risk prevention and control method of the embodiments of the present application has the following advantages:

(1) Better timeliness: Based on real-time risk index calculation and monitoring, risks can be discovered and located in a more timely manner;

(2) Stronger dynamic resistance: the transfer and mutation of merchant risks can be discovered in time, and the rapid assembly of strategies and models can be carried out to complete the prevention and control of new risks;

(3) Better versatility: The invention does not target any merchant's figurative risk. Through comprehensive index design, it can detect merchant anomalies caused by different risks.

Referring to FIG. 3, an embodiment of the present application further provides a merchant risk prevention and control device, which may include:

The first determining module 301 is configured to determine target abnormal parameters corresponding to the target index data when the target index data of the merchant is abnormal;

The obtaining module 303 is used to obtain the target category to which the target indicator corresponding to the target indicator data belongs and the target dimension to which the target category belongs based on the correspondence between the indicators, categories and dimensions;

The second determining module 305 is configured to determine at least one linkage category based on the target dimension and the target category, and the at least one linkage category includes indicators that cause abnormal target indicator data;

The processing module 307 is configured to perform merchant risk prevention and control based on the target abnormal parameter and at least one linkage category.

It can be understood that the merchant risk prevention and control device shown in FIG. 3 can implement various steps of the merchant risk prevention and control method described in FIG. 1, and the relevant explanations on the merchant risk prevention and control methods in the foregoing embodiments are applicable to merchant risk prevention and control The device will not be repeated here.

4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Please refer to FIG. 4. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), or may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. Of course, the electronic device may also include hardware required for other services.

The processor, network interface and memory can be connected to each other through an internal bus, which can be an ISA (Industry Standard Architecture, Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry, Standard Architecture, extended industry standard structure) bus, etc. The bus can be divided into an address bus, a data bus, and a control bus. For ease of representation, only one bidirectional arrow is used in FIG. 4, but it does not mean that there is only one bus or one type of bus.

Memory, used to store programs. Specifically, the program may include program code, and the program code includes a computer operation instruction. The memory may include memory and non-volatile memory, and provide instructions and data to the processor.

The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to form a merchant risk prevention and control device at a logical level. The processor executes the programs stored in the memory and is specifically used to perform the following operations:

Based on the correspondence between indicators, categories, and dimensions, obtain the target category to which the target indicator corresponding to the target indicator data belongs, and the target dimension to which the target category belongs;

Based on the target dimension and the target category, determine at least one linkage category, at least one linkage category includes indicators that cause abnormal target indicator data;

Based on target abnormal parameters and at least one linkage category, carry out merchant risk prevention and control.

The above method performed by the merchant risk prevention and control device disclosed in the foregoing corresponding embodiments of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method may be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The aforementioned processor may be a general-purpose processor, including a central processor (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; it may also be a digital signal processor (Digital Signal Processor, DSP), dedicated integration Circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps, and logical block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in conjunction with the embodiments of the present application may be directly embodied and executed by a hardware decoding processor, or may be executed and completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, and registers. The storage medium is located in the memory. The processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

The electronic device may also execute the method performed by the corresponding merchant risk prevention and control device, and implement the functions of the merchant risk prevention and control device in the aforementioned corresponding embodiments, and the embodiments of the present application will not be repeated here.

An embodiment of the present application also provides a computer-readable storage medium that stores one or more programs, and the one or more programs include instructions, which are executed by an electronic device that includes multiple application programs At this time, it can enable the electronic device to execute the method executed by the merchant risk prevention and control device in the embodiment shown in FIG. 1, and is specifically used to execute:

Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

The present invention is described with reference to flowcharts and/or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. 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 An apparatus 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 an instruction device, the instructions The device implements the functions specified in one block or multiple blocks of 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.

In a typical configuration, the computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

Memory may include non-permanent memory, random access memory (RAM) and/or non-volatile memory in computer-readable media, such as read only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer-readable media, including permanent and non-permanent, removable and non-removable media, can store information by any method or technology. The information may be computer readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices. As defined in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

It should also be noted that the terms "include", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device that includes a series of elements includes not only those elements, but also includes Other elements not explicitly listed, or include elements inherent to this process, method, commodity, or equipment. Without more restrictions, the element defined by the sentence "include one..." does not exclude that there are other identical elements in the process, method, commodity, or equipment that includes the element.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

The above are only examples of the present application, and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included in the scope of the claims of this application.

Claims

A merchant risk prevention and control method, including:

In the case that the target indicator data of the merchant is abnormal, determine the target abnormal parameter corresponding to the target indicator data;

Acquiring the target category to which the target indicator corresponding to the target indicator data belongs, and the target dimension to which the target category belongs based on the correspondence between the indicators, categories, and dimensions;

Determine at least one linkage category based on the target dimension and the target category, and the at least one linkage category includes an indicator that causes abnormality of the target indicator data;

Based on the target abnormal parameter and the at least one linkage category, merchant risk prevention and control is performed.
The method according to claim 1, determining at least one linkage category based on the target dimension and the target category, comprising:

Acquiring at least one linked dimension associated with the target dimension;

Under the at least one linkage dimension, the at least one linkage category associated with the target category is obtained.
The method according to claim 2, performing merchant risk prevention and control based on the target abnormal parameter and the at least one linkage category includes:

Under the at least one linkage category, obtain a first set of indexes corresponding to the abnormal index data;

Based on the target abnormal parameter, the at least one linkage category, and the first indicator set, merchant risk prevention and control is performed.
The method according to claim 3, based on the target abnormal parameter, the at least one linkage category and the first indicator set, performing merchant risk prevention and control, including:

Based on the at least one linkage category, determine an abnormal cause that causes abnormality in the target indicator data;

Determining a first parameter set corresponding to the first index set, each abnormal parameter in the first parameter set corresponds one-to-one with index data of each index in the first index set;

Based on the target abnormal parameter, the first index set and the first parameter set, a second index set and a second parameter set are determined, each abnormal parameter in the second parameter set and the second index set The indicator data of each indicator corresponds to each other;

Based on the abnormal reason, the second indicator set and the second parameter set, merchant risk prevention and control is performed.
The method according to claim 4, based on the at least one linkage category, determining an abnormal cause that causes abnormality in the target indicator data includes:

Receive category selection instructions;

In the at least one linkage category, determine a target linkage category corresponding to the category selection instruction;

The cause of the abnormality is determined according to the target linkage category.
The method according to claim 4, determining the second indicator set and the second parameter set based on the target abnormal parameter, the first indicator set and the first parameter set includes:

Determine the difference between the target abnormal parameter and each abnormal parameter in the first parameter set;

When the difference is within a preset threshold range, constructing the second parameter set based on the abnormal parameter corresponding to the difference;

The second indicator set is constructed based on indicators corresponding to each abnormal parameter in the second parameter set.
The method according to claim 6, performing merchant risk prevention and control based on the abnormal cause, the second indicator set, and the second parameter set includes:

In the second set of indicators, obtain a risk prevention and control indicator that matches the cause of the abnormality;

In the second parameter set, obtain risk prevention and control parameters, which correspond to the risk prevention and control indicators in one-to-one correspondence;

Based on the risk prevention and control indicators and the risk prevention and control parameters, carry out merchant risk prevention and control.
The method according to claim 7, performing merchant risk prevention and control based on the risk prevention index and the risk prevention and control parameters, including:

Acquiring the risk prevention and control strategy corresponding to the risk prevention and control index when the risk prevention and control parameter is greater than the abnormal parameter threshold;

Perform merchant risk prevention and control based on the risk prevention and control strategy.
According to the method of claim 7, before carrying out merchant risk prevention and control, the method further comprises:

Obtain historical risk indicators and corresponding historical abnormal parameters.
The method according to claim 9, performing merchant risk prevention and control based on the risk prevention and control indicators and the risk prevention and control parameters, including:

In the case where the risk prevention and control indicator has a missing indicator compared with the historical risk indicator, the initial model is trained based on the first training sample to obtain the first merchant risk identification model;

Deploy the first merchant risk identification model for merchant risk identification and output the risk identification results;

Obtain a risk prevention and control strategy corresponding to the risk identification result to carry out risk prevention and control based on the risk prevention and control strategy;

Wherein, the missing indicator is included in the historical risk indicator, and the first training sample includes the risk prevention and control indicator, the risk prevention and control parameter, the missing indicator and the missing abnormal parameter corresponding to the missing indicator .
According to the method of claim 10, training the initial model based on the first training sample to obtain the first merchant risk identification model includes:

Determine a risk identification threshold based on the risk prevention and control parameters and the missing anomaly parameters;

Generating an input vector of the initial model based on the risk prevention and control indicator and the missing indicator;

Input the input vector into the initial model to obtain the output of the initial model;

Adjust the parameters of the initial model according to the gap between the output of the initial model and the risk identification threshold;

Repeat the above steps until the gap meets the preset condition to obtain the first merchant risk identification model.
The method according to claim 9, performing risk prevention and control on the merchant based on the risk prevention and control indicators and the risk prevention and control parameters, including:

When there is no missing indicator compared with the historical risk indicator, based on the second training sample, the initial model is trained to obtain a second merchant risk identification model;

Deploy the second merchant risk identification model for merchant risk identification and output the risk identification results;

Obtain a risk prevention and control strategy corresponding to the risk identification result to carry out risk prevention and control based on the risk prevention and control strategy;

Wherein, the missing indicator is included in the historical risk indicator, and the second training sample includes the second risk prevention and control indicator and the second risk prevention and control parameter.
The method according to claim 12, based on the second training sample, training the initial model to obtain a second merchant risk identification model, comprising:

Determine the risk identification threshold based on the risk prevention and control parameters;

Generating an input vector of the initial model based on the risk prevention and control index;

Input the input vector into the initial model to obtain the output of the initial model;

Adjust the parameters of the initial model according to the gap between the output of the initial model and the risk identification threshold;

Repeat the above steps until the gap meets the preset condition to obtain the second merchant risk identification model.
The method according to any one of claims 1 to 13, further comprising:

Based on the time-series anomaly detection model, detecting whether the merchant's target index data is abnormal;

When the detection result indicates that the target index data is abnormal, it is determined that the target index data is abnormal.
The method according to claim 14, based on the time-series anomaly detection model, detecting whether there is an anomaly in the target indicator data of the merchant, including:

Determine a plurality of time-series measured values corresponding to the target indicator data in the current period;

Based on the time-series prediction value and the plurality of time-series actual measured values, calculating multiple time-series outliers corresponding to the plurality of time-series actual measurement values;

When the average value of the plurality of time-series abnormal values is greater than or equal to a preset value, it is determined that the detection result indicates that the target index data is abnormal;

In the case where the average value of the plurality of time-series abnormal values is less than the preset value, it is determined that the detection result indicates that the target index data is normal.
The method of claim 15, the step of determining the timing outliers includes:

Determining the time difference between the time-series measured value and the time-series prediction value;

Calculate the ratio between the timing difference and the timing prediction value to obtain the timing outlier.
The method according to any one of claims 1 to 13, comprising at least one of the following:

The dimension includes at least one of a geographic dimension, a merchant type dimension, and a payment interface dimension;

The category includes at least one of a merchant registration category and a merchant operation category.
The method according to claim 17, comprising at least one of the following:

The merchant registration category includes at least one of the following indicators: the nature of the merchant, maintenance records, business content, and industry;

The merchant operation category includes at least one of the following indicators: transaction amount, number of complaints, product quality, service quality, and payment interface usage.
A merchant risk prevention and control device, including:

The first determining module is used to determine the target abnormal parameter corresponding to the target index data when the target index data of the merchant is abnormal;

An obtaining module, configured to obtain the target category to which the target indicator corresponding to the target indicator data belongs and the target dimension to which the target category belongs based on the correspondence between the indicators, categories and dimensions;

A second determining module, configured to determine at least one linkage category based on the target dimension and the target category, and the at least one linkage category includes an indicator that causes abnormality of the target indicator data;

The processing module is configured to perform merchant risk prevention and control based on the target abnormal parameter and the at least one linkage category.
An electronic device, including:

Processor; and

A memory arranged to store computer-executable instructions, which when executed, causes the processor to perform the following operations:

In the case that the target indicator data of the merchant is abnormal, determine the target abnormal parameter corresponding to the target indicator data;

Acquiring the target category to which the target indicator corresponding to the target indicator data belongs, and the target dimension to which the target category belongs based on the correspondence between the indicators, categories, and dimensions;

Determine at least one linkage category based on the target dimension and the target category, and the at least one linkage category includes an indicator that causes abnormality of the target indicator data;

Based on the target abnormal parameter and the at least one linkage category, merchant risk prevention and control is performed.
A computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, which when executed by an electronic device including a plurality of application programs, cause the electronic device to execute The following operations:

In the case that the target indicator data of the merchant is abnormal, determine the target abnormal parameter corresponding to the target indicator data;

Acquiring the target category to which the target indicator corresponding to the target indicator data belongs, and the target dimension to which the target category belongs based on the correspondence between the indicators, categories, and dimensions;

Determine at least one linkage category based on the target dimension and the target category, and the at least one linkage category includes an indicator that causes abnormality of the target indicator data;

Based on the target abnormal parameter and the at least one linkage category, merchant risk prevention and control is performed.