CN117522404A - Cross-border enterprise wind control data intelligent processing method and system - Google Patents
Cross-border enterprise wind control data intelligent processing method and system Download PDFInfo
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Abstract
The invention provides a method and a system for intelligently processing wind control data of a cross-border enterprise, which relate to the technical field of data processing, and the method comprises the following steps: firstly, transaction data of a cross-border transaction platform is extracted based on a cross-border enterprise, then information of a cross-border user is determined, a data analysis network is constructed through cross-border transaction modeling true and false data, transaction risk data are obtained, risk assessment is carried out on the cross-border user information, risk transaction coefficients are obtained, and risk control is carried out on the transaction platform through the risk transaction coefficients. The method and the device mainly solve the risk that funds or information of a user are not protected to cause funds loss due to poor management and management of the platform caused by imperfect technology of the platform when the user conducts transactions. The transaction data is directly extracted from the enterprises of cross-border transactions to further determine user information, risk prediction is carried out on the transaction platform through the transaction data, risk control is carried out on the transaction platform, and the technical effect of data accuracy is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a cross-border enterprise wind control data intelligent processing method and system.
Background
With the advent of the "internet+" age, the international trade model has changed deeply, and traditional off-line trade is gradually yielding on-line trade. Cross-border e-commerce plays an increasingly important role in international commerce as a main carrier of online commerce, and in recent years, cross-border e-commerce plays an increasingly important role in international commerce. At present, remarkable achievement is achieved on the development of cross-border electronic commerce, but supervision is relatively lagged, so that how to perform risk control on cross-border data is worth deeply discussing.
In the prior art, a supervision department considers a logistics enterprise as an access point of a cross-border e-commerce statistical monitoring system by researching a cross-border e-commerce supervision mode and researching related enterprises of the cross-border e-commerce in the field, and performs transaction risk analysis.
The problem that the supervision system is not perfect yet exists in the prior art is that the problem of data loss exists in the current cross-border electronic commerce monitoring statistics, a large amount of trade data which is attributed to the cross-border electronic commerce are classified in other trade modes, the accuracy of the data is lower, the technical problem of inaccurate transaction risk assessment is caused, the risk that the platform is poor in operation and management due to the fact that the technology of the platform is imperfect when a user conducts transactions exists, and funds or information of the user are not protected, so that the funds are lost is caused.
Disclosure of Invention
The method solves the problem that the supervision system is not perfect yet in the prior art, which leads to the problem that data are missing in the current cross-border e-commerce monitoring statistics, a large amount of trade data which are attributed to the cross-border e-commerce are classified in other trade modes, so that the accuracy of the data is lower, the technical problem of inaccurate transaction risk assessment is caused, the technical defect of a platform when a user carries out transaction causes poor management and management of the platform, and funds or information of the user are not protected, so that the risk of funds loss is caused.
In view of the foregoing, an embodiment of the present application provides a method and a system for intelligently processing wind control data of a cross-border enterprise, where in a first aspect, the embodiment of the present application provides a method for intelligently processing wind control data of a cross-border enterprise, and the method includes: extracting cross-border transaction data in the cross-border transaction platform based on a target cross-border enterprise, determining N pieces of cross-border user information, wherein the N pieces of cross-border user information are determined according to transaction order information in the cross-border transaction data, the cross-border transaction data and the N pieces of cross-border user information are in corresponding relation, N is a positive integer greater than or equal to 1, constructing a data analysis network through simulated data of cross-border transaction, conducting risk prediction of transaction on the cross-border transaction platform based on the data analysis network, acquiring multi-stage transaction risk data, conducting risk assessment on the N pieces of cross-border user information based on the multi-stage transaction risk data, obtaining transaction risk coefficients of N cross-border users, and conducting risk control on the cross-border transaction platform according to the transaction risk coefficients of the N cross-border users.
In a second aspect, an embodiment of the present application provides a cross-border enterprise wind control data intelligent processing system, where the system includes: the system comprises a transaction data extraction module, a user information determination module, a multi-stage transaction risk data acquisition module and a cross-border user transaction risk factor acquisition module, wherein the transaction data extraction module is used for extracting cross-border transaction data in the cross-border transaction platform based on a target cross-border enterprise, the user information determination module is used for determining N pieces of cross-border user information, the N pieces of cross-border user information are determined according to transaction order information in the cross-border transaction data, the cross-border transaction data and the N pieces of cross-border user information are in a corresponding relation, N is a positive integer greater than or equal to 1, the data analysis network construction module is used for constructing a data analysis network through simulation and assumption data of cross-border transactions, the multi-stage transaction risk data acquisition module is used for conducting risk prediction on the cross-border transaction platform based on the data analysis network, the multi-stage transaction risk factor acquisition module is used for acquiring the N pieces of cross-border user information, the cross-border user transaction risk factor acquisition module is used for conducting risk assessment on the N pieces of cross-border user information based on the multi-stage transaction risk factor, and the cross-border user transaction risk factor control module is used for conducting risk control on the cross-border transaction platform based on the risk control platform.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
the invention provides a cross-border enterprise wind control data intelligent processing method, which relates to the technical field of data processing, and comprises the following steps: firstly, transaction data of a cross-border transaction platform is extracted based on a cross-border enterprise, then information of a cross-border user is determined, a data analysis network is constructed through cross-border transaction modeling true and false data, transaction risk data is obtained based on the data analysis network, risk assessment is carried out on the cross-border user information, risk transaction coefficients are obtained, and risk control is carried out on the transaction platform through the risk transaction coefficients.
The problem that the supervision system is not perfect yet exists in the prior art is that the problem of data loss exists in the current cross-border electronic commerce monitoring statistics, a large amount of trade data which is attributed to the cross-border electronic commerce are classified in other trade modes, the accuracy of the data is lower, the technical problem of inaccurate transaction risk assessment is caused, and the technical problem that the platform is poor in operation and management due to the fact that the technology of the platform is imperfect when a user conducts transactions, and funds or information of the user are not protected, so that the risk of funds loss is caused. The transaction data is directly extracted from the enterprises of cross-border transactions to further determine user information, risk prediction is carried out on the transaction platform through the transaction data, risk control is carried out on the transaction platform, and the technical effect of data accuracy is improved.
The foregoing description is merely an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
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For a clearer description of the present disclosure or of the prior art, the drawings used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are only exemplary and that other drawings may be obtained, without inventive effort, by a person skilled in the art, from the provided drawings.
Fig. 1 is a schematic flow chart of a method for intelligently processing wind control data of a cross-border enterprise according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for constructing the cross-border transaction platform by performing data association on the cross-border order data, the cross-border product data and the cross-border user data according to the business information association factor in the intelligent processing method for cross-border enterprise wind control data according to the embodiment of the application;
fig. 3 is a schematic flow chart of a method for extracting cross-border user information of the transaction orders and combining the same cross-border user information to determine the N cross-border user information in the intelligent processing method for cross-border enterprise wind control data according to the embodiment of the present application;
fig. 4 is a schematic structural diagram of an intelligent processing system for wind control data of a cross-border enterprise according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a transaction data extraction module 10, a user information determination module 20, a data analysis network construction module 30, a multi-level transaction risk data acquisition module 40, a cross-border user transaction risk coefficient acquisition module 50 and a cross-border platform risk control module 60.
Detailed Description
The problem that the supervision system is not perfect yet exists in the prior art is that the problem of data loss exists in the current cross-border electronic commerce monitoring statistics, a large amount of trade data which is attributed to the cross-border electronic commerce are classified in other trade modes, the accuracy of the data is lower, the technical problem of inaccurate transaction risk assessment is caused, and the technical problem that the platform is poor in operation and management due to the fact that the technology of the platform is imperfect when a user conducts transactions, and funds or information of the user are not protected, so that the risk of funds loss is caused. The transaction data is directly extracted from the enterprises of cross-border transactions to further determine user information, risk prediction is carried out on the transaction platform through the transaction data, risk control is carried out on the transaction platform, and the technical effect of data accuracy is improved.
The technical scheme in the embodiment of the application aims to solve the problems, and the overall solution thinking is as follows:
and calling basic data of the target cross-border enterprise, wherein the basic data comprises cross-border order data, cross-border product data and cross-border user data, calling the basic data of the target cross-border enterprise, wherein the basic data comprises cross-border order data, cross-border product data and cross-border user data, and carrying out data association on the cross-border order data, the cross-border product data and the cross-border user data according to the business information association factors to construct the cross-border transaction platform.
And extracting cross-border transaction data in the cross-border transaction platform based on a target cross-border enterprise, carrying out effective order retrieval according to the cross-border order data in the cross-border transaction data, acquiring effective order data, determining a plurality of transaction order information through cross-border transaction orders in the effective order data, extracting cross-border user information of the plurality of transaction orders, merging the same cross-border user information, and determining the N cross-border user information. The N cross-border user information is determined according to the transaction order information in the cross-border transaction data, wherein the cross-border transaction data and the N cross-border user information are in corresponding relation, and N is a positive integer greater than or equal to 1.
Constructing a data analysis network through simulated and assumed data of cross-border transactions, wherein the data analysis network comprises a data input layer, a hidden layer and an output layer, and performing data annotation on the simulated and assumed data of the cross-border transactions to obtain a constructed data set, wherein the constructed data set comprises a training set and a verification set, and the training set and the verification set are adopted to perform supervision training and verification on the data analysis network until the data analysis network or the accuracy rate reaches a preset requirement, so as to construct the data analysis network.
Setting simulated assumption data of the cross-border transaction according to simulated transaction data, marking the simulated assumption data of the cross-border transaction according to a risk index calculation formula, carrying out risk prediction of the transaction on the cross-border transaction platform based on the data analysis network, obtaining multi-level transaction risk data, carrying out risk assessment on the N pieces of cross-border user information based on the multi-level transaction risk data, obtaining transaction risk coefficients of N cross-border users, and carrying out risk control on the cross-border transaction platform according to the transaction risk coefficients of the N cross-border users.
For a better understanding of the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments of the present invention:
example 1
The method is applied to a cross-border enterprise wind control data intelligent processing system, as shown in fig. 1, and the cross-border enterprise wind control data intelligent processing system is in communication connection with a cross-border transaction platform, and the method comprises the following steps:
extracting cross-border transaction data in the cross-border transaction platform based on a target cross-border enterprise;
specifically, the target cross-border enterprise is an enterprise with cross-border e-commerce industry, wherein the cross-border enterprise has obvious regional characteristics, and the development of the cross-border enterprise is indistinguishable from factors such as regional economy development level, geographic position, traffic and the like. Cross-border e-commerce enterprises mainly distribute e-commerce developed areas. Has stronger policy support, more advanced internet technology, more developed logistics system and stronger market demand, thereby attracting a great deal of cross-border e-commerce enterprises to gather. The cross-border transaction platform is a transaction platform with an international seller and an international buyer, cross-border transaction data in the cross-border transaction platform are extracted from a target cross-border enterprise, wherein the cross-border transaction data are cross-border transactions, such as the current business or consumer in the A country and the B country conduct transactions in the transaction platform, the order of the commodity in the B country, which is purchased by the buyer in the A country, is the cross-border transaction data, the data are stored in a transaction database, transaction information is marked during the transactions, the marked information comprises information such as the transaction time, the transaction quantity, the transaction business, the transaction user, the transaction success rate and the like, and the cross-border transaction data can be extracted through searching the cross-border transaction. Providing convenience for later more convenient determination of customer information.
Determining N pieces of cross-border user information, wherein the N pieces of cross-border user information are determined according to transaction order information in the cross-border transaction data, the cross-border transaction data and the N pieces of cross-border user information are in a corresponding relation, and N is a positive integer greater than or equal to 1;
specifically, N pieces of cross-border user information are determined, the client information includes names, mailboxes, telephones, country information and the like of clients, the N pieces of cross-border user information are a plurality of users transacted by cross country, for example, client a of country a needs to obtain client information of client a by purchasing commodity B of country B in a cross-border transaction platform, transaction orders include information of transaction time, transaction quantity, two parties of transaction and the like, the transaction data include user information, an order of each transaction can be found through cross-border transaction, information of each client can be determined from the order, and accuracy of transaction client information is improved.
Constructing a data analysis network by simulating and assuming data of cross-border transactions;
specifically, a data analysis network is constructed by simulating and assuming data of cross-border transactions, data information can be obtained by the cross-border transactions, information such as whether the transactions are successful or the number of transactions is obtained, the simulated data is data which is supposed to be truly obtained or predicted according to facts, the data can be judged by simulating and assuming that the data comprises the time of the latest consumption of a client or the time interval of the latest purchase, the consumption frequency in a period of time and the consumption amount in the period of time, so that whether the consumption of the client is likely to occur again is judged, the data analysis network is constructed according to the data, the data analysis refers to the process of analyzing a large amount of collected data by using a proper statistical analysis method, extracting useful information and forming conclusions to study and summarize the data in detail, and the data analysis network can be constructed by a structural analysis method or a grouping analysis method. The information in a large group of data which is disordered in view can be concentrated and extracted, so that the purchasing rule of the customer can be found, and people can be helped to make judgment more conveniently.
Performing risk prediction of transaction on the cross-border transaction platform based on the data analysis network to acquire multi-level transaction risk data;
specifically, based on the risk prediction of the transaction performed on the cross-border transaction platform by the data analysis network, many digital token companies are given high-tech flags, the digital asset transaction platform of the digital token companies is announced to the outside, the system architecture of the digital token transaction platform in each country and region is safe and reliable, the risk prevention and control mechanism is perfect, in fact, the system architecture of the digital token transaction platform in each country and region is imperfect, the system architecture is subjected to the attack and the digital token theft event, the risk prediction can be performed on the transaction platform in advance, the occurrence of the theft event can be reduced, or the problems of expenditure and inconsistency of buying parties and obtaining and the like can be solved, the problems include that a buyers use 500 to buy the ticket, but the sellers only receive 450 or do not receive the ticket, or a buyers receive the ticket 500, but the sellers do not deliver the ticket, the problem of a buyers are caused, the transaction risk is predicted by random forest study, and multilevel transaction risk data is obtained. The multi-level transaction risk data is a buyer spending 500, whether the platform can receive 500, receive a part or receive 450, or b the seller delivers goods, the delivery platform can receive goods, whether the buyer receives goods after the platform receives goods, and the like.
Performing risk assessment on the N cross-border user information based on the multi-level transaction risk data to obtain transaction risk coefficients of N cross-border users;
specifically, the multi-level transaction risk data performs risk assessment on the N cross-border user information, and may perform risk assessment by a quantitative analysis method or a qualitative analysis method, where the quantitative analysis method is to issue a questionnaire, and the surveyor evaluates the possibility of occurrence of related risks and the risk influence degree according to experience, and the qualitative analysis method evaluates the level and the level of the related risk of the transaction according to the related quantitative data of the transaction platform. And evaluating the transaction condition of each user to obtain transaction risk coefficients of all cross-border users in the platform. The risk coefficient is a numerical value which measures the possibility of loss and is expressed in numerical value, and the platform can be controlled and adjusted through the risk coefficient.
And performing risk control on the cross-border transaction platform according to the transaction risk coefficients of the N cross-border users.
Specifically, risk control is performed on the cross-border transaction platform according to the transaction risk coefficients of the N cross-border users, where the risk coefficient may be M. M is a number between 0 and 1, if the risk coefficient is too large, the transaction abnormality is more, the failure reasons may include that the merchant has a problem, or the platform, the buyer, before paying, is in the middle of the platform, and the middle of the platform is in the middle, so that the transaction platform receives the gold coins which are inconsistent with the expenditure of the buyer or are in each country, wherein the gold coins in each country circulate at different exchange rates, the risk control can be performed on the transaction platform through the coefficient because the problem of exchange rate of the platform causes the discrepancy received by the seller, the risk control can be more strictly controlled selection on the selection of the merchant, and the calculation of the amount is controlled by a more rigorous method and the like.
Further, as shown in fig. 2, the method of the present application further includes:
invoking basic data of the target cross-border enterprise, wherein the basic data comprises cross-border order data, cross-border product data and cross-border user data;
specifically, basic data of a target cross-border enterprise is called, wherein the basic data comprises cross-border order data, cross-border product data and cross-border user data, the cross-border order data comprises information such as transaction success rate, transaction amount, transaction time interval and the like, the cross-border product data comprises information such as product names, export countries and the like, and the cross-border user data comprises identity information of sellers and buyers and the like.
Acquiring a business information association factor based on the business association relation of the target cross-border enterprise;
and carrying out data association on the cross-border order data, the cross-border product data and the cross-border user data according to the service information association factors to construct the cross-border transaction platform.
Specifically, the service information association factors are obtained, the service association relationships are relationships between product manufacturing and selling parts and the background, for example, a product manufacturer can manufacture a batch of products, the products have marks, the products sold by the selling part can be recorded, for example, 100 products are produced, the products are sold from 0 to 99, and the information of the customers where the products are sold, such as the information of the places where the products are sold, can be recorded in the background for selling 1, 2, 5 and 99 products. And carrying out data association on the cross-border order data, the cross-border product data and the cross-border user data to construct the cross-border transaction platform.
According to these further aspects, as shown in fig. 3, the method of the present application determines N pieces of cross-border user information according to the transaction order information in the cross-border transaction data, and the method further includes:
performing effective order retrieval according to the cross-border order data in the cross-border transaction data to obtain effective order data;
specifically, effective order retrieval is performed according to the cross-border order data in the cross-border transaction data, the transaction platform is connected with the database through JDBC, and the order data are stored in the background database, wherein the effective data are successful transaction data, for example, some users buy things, do not pay or cancel orders later, and the like, and the order data of the effective data are obtained.
Determining a plurality of trade order information by cross-border trade orders in the valid order data;
and extracting cross-border user information of the transaction orders, merging the same cross-border user information, and determining the N cross-border user information.
Specifically, a plurality of transaction order information is determined through the cross-border transaction order in the effective order data, the transaction order information is transaction information of different users, the transaction information is all effective information of a transaction platform, the same user information can be combined, two pointers can be set to start from the head part and to start from the tail part simultaneously through traversing the effective transaction information, and the two pointers are stored in a new database in the same place under the condition of judgment, and deleted in a source database, so that all the information can be divided into different databases according to the transaction places, management can be conveniently carried out, risk differentiation of each place can be further subdivided, and the method is more accurate.
Further, the method constructs a data analysis network through the simulated and assumed data of the cross-border transaction, and the method further comprises the following steps:
the data analysis network comprises a data input layer, an implicit layer and an output layer;
performing data annotation on the simulated and assumed data of the cross-border transaction to obtain a constructed data set, wherein the constructed data set comprises a training set and a verification set;
and performing supervision training and verification on the data analysis network by adopting the training set and the verification set until the data analysis network or the accuracy reaches a preset requirement, and constructing the data analysis network.
Specifically, the data analysis network comprises a data input layer, an implicit layer and an output layer, the input layer comprises data input and data management, the implicit layer processes the data, the output layer outputs the processed data, the data is marked on simulated and assumed data of cross-border transactions, real data and predicted data are marked, different data sets are constructed, the different data sets are a training set and a verification set, the real data are taken as the training set, the predicted data are taken as the verification set, the training set and the verification set are adopted to supervise and train the data analysis network until the data analysis network or the accuracy rate reaches a preset requirement, the accuracy rate can be set to be 95%, if the predicted data and the real data coincidence rate is 96%, the data analysis network is indicated to reach the preset requirement, otherwise, the data analysis network is constructed, the purchase information of users can be predicted in advance, and risk prediction can be performed on the platform in advance, and if the real risk is avoided in time.
Further, the method of the present application performs data annotation on the pseudo-and-assumed data of the cross-border transaction, and the method further includes:
setting simulated assumption data of the cross-border transaction according to the simulated transaction data;
according to a risk index calculation formula, carrying out data marking of a risk index on the simulated and assumed data of the cross-border transaction;
specifically, setting simulated assumption data of cross-border transaction according to simulated assumption transaction data, taking a real value as real data of the simulation value, predicting data according to rules of the real data to serve as assumption data, carrying out data annotation on risk coefficients according to a calculation formula, obtaining the risk coefficients according to the calculation formula, and marking the risk coefficients to more obviously show which group of transaction data has risks.
Further, in the method, the risk index calculation formula is as follows:；
wherein N is the risk index, s is the actual risk loss, y is the expected risk loss, and r is the risk probability.
Example 2
Based on the same inventive concept as the method for intelligently processing cross-border enterprise wind control data in the foregoing embodiment, as shown in fig. 4, the present application provides a cross-border enterprise wind control data intelligent processing system, where the system includes:
the transaction data extraction module is used for extracting cross-border transaction data in the cross-border transaction platform based on a target cross-border enterprise;
the user information determining module is used for determining N pieces of cross-border user information, wherein the N pieces of cross-border user information are determined according to transaction order information in the cross-border transaction data, the cross-border transaction data and the N pieces of cross-border user information are in corresponding relation, and N is a positive integer greater than or equal to 1;
the data analysis network construction module is used for constructing a data analysis network through simulated and assumed data of cross-border transactions;
the multi-level transaction risk data acquisition module is used for carrying out risk prediction of transaction on the cross-border transaction platform based on the data analysis network to acquire multi-level transaction risk data;
the cross-border user transaction risk coefficient acquisition module is used for performing risk assessment on the N cross-border user information based on the multi-level transaction risk data to acquire N cross-border user transaction risk coefficients;
and the cross-border platform risk control module is used for controlling risks of the cross-border transaction platform according to the transaction risk coefficients of the N cross-border users.
Further, the system further comprises:
the basic data calling module is used for calling basic data of the target cross-border enterprise, wherein the basic data comprises cross-border order data, cross-border product data and cross-border user data;
the business association factor acquisition module is used for acquiring business information association factors based on the business association relation of the target cross-border enterprise;
the transaction platform construction module is used for carrying out data association on the cross-border order data, the cross-border product data and the cross-border user data according to the business information association factors to construct the cross-border transaction platform
Further, the system further comprises:
the effective order data acquisition module is used for carrying out effective order retrieval according to the cross-border order data in the cross-border transaction data to acquire effective order data;
an order information determining module for determining a plurality of trade order information through cross-border trade orders in the valid order data;
and the cross-border user information determining module is used for extracting cross-border user information of the transaction orders, combining the same cross-border user information and determining the N cross-border user information.
Further, the system further comprises:
the data analysis network module comprises a data input layer, an implicit layer and an output layer;
the data set construction module is used for carrying out data annotation on the simulated and assumed data of the cross-border transaction to obtain a construction data set, wherein the construction data set comprises a training set and a verification set;
and the data analysis network construction module is used for performing supervision training and verification on the data analysis network by adopting the training set and the verification set until the data analysis network or the accuracy rate reaches a preset requirement, so as to construct the data analysis network.
Further, the system further comprises:
the true assumption data drawing-up module is used for setting the true assumption data of the cross-border transaction according to the simulated transaction data;
the data labeling module of the risk index is used for labeling the data of the risk index for the simulated and assumed data of the cross-border transaction according to a risk index calculation formula;
further, the system further comprises:
the risk index calculation module comprises a risk index calculation formula, which isWherein N is the risk index, s is the actual risk loss, y is the expected risk loss, and r is the risk probability.
Through the foregoing detailed description of a cross-border enterprise wind control data intelligent processing method, those skilled in the art can clearly understand that a cross-border enterprise wind control data intelligent processing system in this embodiment, for the system disclosed in the embodiment, the description is relatively simple because it corresponds to the embodiment disclosure network, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The method is applied to a cross-border enterprise wind control data intelligent processing system which is in communication connection with a cross-border transaction platform, and comprises the following steps:
extracting cross-border transaction data in the cross-border transaction platform based on a target cross-border enterprise;
determining N pieces of cross-border user information, wherein the N pieces of cross-border user information are determined according to transaction order information in the cross-border transaction data, the cross-border transaction data and the N pieces of cross-border user information are in a corresponding relation, and N is a positive integer greater than or equal to 1;
constructing a data analysis network by simulating and assuming data of cross-border transactions;
performing risk prediction of transaction on the cross-border transaction platform based on the data analysis network to acquire multi-level transaction risk data;
performing risk assessment on the N cross-border user information based on the multi-level transaction risk data to obtain transaction risk coefficients of N cross-border users;
and performing risk control on the cross-border transaction platform according to the transaction risk coefficients of the N cross-border users.
2. The method of claim 1, wherein the method further comprises:
invoking basic data of the target cross-border enterprise, wherein the basic data comprises cross-border order data, cross-border product data and cross-border user data;
acquiring a business information association factor based on the business association relation of the target cross-border enterprise;
and carrying out data association on the cross-border order data, the cross-border product data and the cross-border user data according to the service information association factors to construct the cross-border transaction platform.
3. The method of claim 2, wherein the N cross-border user information is determined from transaction order information in cross-border transaction data, the method further comprising:
performing effective order retrieval according to the cross-border order data in the cross-border transaction data to obtain effective order data;
determining a plurality of trade order information by cross-border trade orders in the valid order data;
and extracting cross-border user information of the transaction orders, merging the same cross-border user information, and determining the N cross-border user information.
4. The method of claim 1, wherein the data analysis network is constructed from simulated data of cross-border transactions, the method further comprising:
the data analysis network comprises a data input layer, an implicit layer and an output layer;
performing data annotation on the simulated and assumed data of the cross-border transaction to obtain a constructed data set, wherein the constructed data set comprises a training set and a verification set;
and performing supervision training and verification on the data analysis network by adopting the training set and the verification set until the data analysis network or the accuracy reaches a preset requirement, and constructing the data analysis network.
5. The method of claim 1, wherein the data annotating the simulated data for the cross-border transaction, the method further comprising:
setting simulated assumption data of the cross-border transaction according to the simulated transaction data;
and marking the data of the risk index for the simulated and assumed data of the cross-border transaction according to a risk index calculation formula.
6. The method of claim 1, wherein the risk index calculation formula is:；
wherein N is the risk index, s is the actual risk loss, y is the expected risk loss, and r is the risk probability.
7. An intelligent processing system for cross-border enterprise wind control data is characterized in that the system comprises:
the transaction data extraction module is used for extracting cross-border transaction data in the cross-border transaction platform based on a target cross-border enterprise;
the user information determining module is used for determining N pieces of cross-border user information, wherein the N pieces of cross-border user information are determined according to transaction order information in the cross-border transaction data, the cross-border transaction data and the N pieces of cross-border user information are in corresponding relation, and N is a positive integer greater than or equal to 1;
the data analysis network construction module is used for constructing a data analysis network through simulated and assumed data of cross-border transactions;
the multi-level transaction risk data acquisition module is used for carrying out risk prediction of transaction on the cross-border transaction platform based on the data analysis network to acquire multi-level transaction risk data;
the cross-border user transaction risk coefficient acquisition module is used for performing risk assessment on the N cross-border user information based on the multi-level transaction risk data to acquire N cross-border user transaction risk coefficients;
and the cross-border platform risk control module is used for controlling risks of the cross-border transaction platform according to the transaction risk coefficients of the N cross-border users.
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| CN202311433945.8A CN117522404A (en) | 2023-10-31 | 2023-10-31 | Cross-border enterprise wind control data intelligent processing method and system |
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