Cross-border e-commerce data asset management method, device, computer equipment and medium

Abstract

The present invention discloses a cross-border e-commerce data asset management method, device, computer equipment and medium, and the method implementation includes: constructing a data sandbox in a customs system; the e-commerce platform labels the business data of merchants and sends it to the data sandbox; in the data sandbox, the labeled business data and the customs data corresponding to the e-commerce platform in the customs system are compared and verified to determine the data error value; the labeled business data, customs data and data error value are field-merged to generate a data report, and the data report is sent to a bank risk control system; the bank risk control system selects target merchants that meet the preset credit rules according to the data report, and determines the credibility coefficient according to the data error value, so as to determine the actual credit limit of the target merchant according to the credibility coefficient. The security and reliability of data transmission are improved, the efficiency is high, the security is strong, and it is traceable.

Description

Technical Field

[0001] The present invention relates to the field of cross-border e-commerce technology, and in particular to a cross-border e-commerce data asset management method, device, computer equipment and medium. Background Art

[0002] Cross-border e-commerce has become a highlight and an important driving force for my country's foreign trade growth. As the demand for funds by cross-border e-commerce companies is also growing rapidly, domestic commercial banks are actively developing financial services for cross-border e-commerce companies. At present, cross-border e-commerce companies mainly sell goods to the world through cross-border e-commerce platforms such as Amazon, eBay, Wish, Shopee, etc., which have the characteristics of high transaction frequency, multiple and complex supply chain links, and long fund settlement cycle, so they put forward higher requirements for the risk control technology of financial institutions.

[0003] However, cross-border e-commerce companies will generate extremely rich and complex data in the course of their operations, such as sales, orders, logistics, funds, user reviews, etc. There are also many risk factors, such as returns and refunds, logistics order loss, delivery cycle fluctuations, capital turnover, and changes in e-commerce platform policies.

[0004] Due to the different data protection laws and regulations in different countries, domestic financial institutions cannot directly obtain real-time operating data when providing cross-border e-commerce financial services. Although cross-border e-commerce companies will generate extremely rich and complex data in the course of their operations, such as sales, orders, logistics, funds, user reviews, etc., there are also many risk factors, such as returns and refunds, lost orders in logistics, fluctuations in delivery cycles, capital turnover, changes in e-commerce platform policies, etc. Therefore, it is difficult to establish an effective cross-border e-commerce risk system, resulting in poor data security and low credibility between financial institutions and e-commerce platforms. Summary of the invention

[0005] Based on this, it is necessary to provide a cross-border e-commerce data asset management method, device, computer equipment and storage medium to address the above technical problems, so as to solve the problems of poor data security and low credibility between financial institutions and e-commerce platforms in the prior art.

[0006] In the first aspect, the present application provides a cross-border e-commerce data asset management method, including:

[0007] Building a data sandbox in the customs system;

[0008] The e-commerce platform labels the merchant's business data and sends it to the data sandbox;

[0009] In the data sandbox, the labeled business data and the customs data corresponding to the e-commerce platform in the customs system are compared and verified to determine the data error value;

[0010] Merging the labeled business data, the customs data, and the data error value into fields to generate a data report, and sending the data report to the bank risk control system;

[0011] The bank risk control system screens out target merchants that meet preset credit rules based on the data report, and determines a credibility coefficient based on the data error value, so as to determine the actual credit limit of the target merchant based on the credibility coefficient.

[0012] Building a data sandbox in the customs system;

[0013] The e-commerce platform labels the merchant's business data and sends it to the data sandbox;

[0014] In the data sandbox, the labeled business data and the customs data corresponding to the e-commerce platform in the customs system are compared and verified to determine the data error value;

[0015] Merging the labeled business data, the customs data, and the data error value into fields to generate a data report, and sending the data report to the bank risk control system;

[0016] The bank risk control system screens out target merchants that meet preset credit rules based on the data report, and determines a credibility coefficient based on the data error value, so as to determine the actual credit limit of the target merchant based on the credibility coefficient.

[0017] In one embodiment, the business data includes sales amount, payment amount and inventory value, and the tagging of the business data of the merchant includes:

[0018] Divide the sales amount, payment amount and inventory value by the preset value respectively, and round them up to form new amount data;

[0019] The new amount data is used as the business data label of the merchant.

[0020] In one embodiment, the business data includes order volume data, and the tagging of the business data of the merchant includes:

[0021] The e-commerce platform divides the order volume into multiple intervals according to the order volume data distribution of each merchant, and numbers them;

[0022] The order volume interval to which the merchant belongs is determined according to the order volume data, and the interval number of the order volume interval to which the merchant belongs is used as the business data label of the merchant.

[0023] In one embodiment, comparing and verifying the labeled business data with the customs data in the customs system corresponding to the e-commerce platform to determine the data error value includes:

[0024] Obtaining the customs export list message associated with the merchant, and counting the amount of goods in the customs export list message as the customs data;

[0025] The commodity amount is compared and verified with the operating data to determine the data error value.

[0026] In one embodiment, the obtaining of the customs export list message associated with the merchant and counting the amount of goods in the customs export list message includes:

[0027] Determine the sales time period for the sales amount;

[0028] According to the sales time period, the customs export list messages that are before a preset time period from the sales time are screened out from the customs export list messages, and the amounts of goods in the customs export list messages are counted.

[0029] In one embodiment, screening out target merchants that meet preset credit rules according to the data report includes:

[0030] Determining whether the business data of the merchant reaches a preset business value according to the data report;

[0031] When the result is yes, the credit information of the merchant is obtained;

[0032] Determining whether the merchant's credit information has a credit risk based on the credit information;

[0033] When the judgment result is no, the merchant is taken as the target merchant.

[0034] In one embodiment, after the bank risk control system evaluates the credit status of the merchant based on the data report to determine the actual credit limit of the merchant, it includes:

[0035] After the merchant obtains a loan according to the actual credit limit, the bank risk control system obtains the merchant's post-loan performance data;

[0036] Sample data is formed according to the data report and the post-loan performance data.

[0037] In the second aspect, the present application also provides a cross-border e-commerce data asset management system, including:

[0038] Customs system for building data sandbox;

[0039] The e-commerce platform is used to label the business data of merchants and send it to the data sandbox;

[0040] The customs system is further used to compare and verify the labeled business data and the customs data corresponding to the e-commerce platform in the customs system in the data sandbox to determine a data error value;

[0041] The customs system is further used to merge the labeled business data, the customs data and the data error value in the data sandbox to generate a data report, and send the data report to the bank risk control system;

[0042] The bank risk control system is used to screen out target merchants that meet preset credit rules based on the data report, and determine a credibility coefficient based on the data error value, so as to determine the actual credit limit of the target merchant based on the credibility coefficient.

[0043] In a third aspect, a computer device is provided, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the steps of the cross-border e-commerce data asset management method as described above when executing the computer-readable instructions.

[0044] In a fourth aspect, one or more readable storage media are provided, wherein the readable storage media stores computer-readable instructions, and wherein the computer-readable instructions, when executed by a processor, implement the steps of the above-mentioned cross-border e-commerce data asset management method.

[0045] The above-mentioned cross-border e-commerce data asset management method, device, computer equipment and storage medium, the method implementation includes: building a data sandbox in the customs system; the e-commerce platform labels the merchant's business data and sends it to the data sandbox; in the data sandbox, the labeled business data and the customs data corresponding to the e-commerce platform in the customs system are compared and verified to determine the data error value; the labeled business data, the customs data and the data error value are field merged to generate a data report, and the data report is sent to the bank risk control system; the bank risk control system screens out target merchants that meet the preset credit rules based on the data report, and determines the credibility coefficient based on the data error value, so as to determine the actual credit limit of the target merchant based on the credibility coefficient. This application, by establishing a data sandbox computing environment in the customs data, compares and verifies the labeled business data sent by the e-commerce platform with the customs data through the data sandbox, and forms a data report after confirming the data difference value and sends it to the bank risk control system, thereby improving the credibility of the business data. In addition, the bank risk control system can further determine the merchant's credit limit based on the merchant's credit status and data difference value, thereby improving the security and reliability of data transmission between financial institutions and e-commerce platforms, and can desensitize, interact and quality audit the business data in real time, with high efficiency, strong security, traceability, and a wider range of use. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0047] Figure 1 This is a schematic diagram of an application environment of a cross-border e-commerce data asset management method in one embodiment of the present invention;

[0048] Figure 2 It is a flow chart of a cross-border e-commerce data asset management method in one embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the structure of a cross-border e-commerce data asset management device in one embodiment of the present invention;

[0050] Figure 4 is a schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] The cross-border e-commerce data asset management method provided in this embodiment can be applied in Figure 1 In the application environment, a data sandbox is pre-built in the customs system, the e-commerce platform communicates with the customs system, and the business data of the merchant is labeled and sent to the data sandbox of the customs system. In the data sandbox, the labeled business data is compared and verified with the customs data, the data difference value is determined, and a data report is formed. The customs system communicates with the bank risk control system to send the data report to the bank risk control system to confirm the credit limit of the merchant, realize the data security interaction between the financial institution and the e-commerce platform, and improve the credibility of the data.

[0053] Among them, the e-commerce platform, customs system and bank risk control system can be respectively set up on the server side, and the server side can be implemented with an independent server or a server cluster composed of multiple servers.

[0054] Among them, the above-mentioned e-commerce platforms and the customs system, and the customs system and the bank risk control system can communicate through dedicated lines or public networks.

[0055] In one embodiment, if Figure 2 As shown, a cross-border e-commerce data asset management method is provided, including the following steps:

[0056] In step S110, a data sandbox is constructed in the customs system;

[0057] In the embodiment of the present application, the data sandbox is a computing environment deployed in the customs system, and the data sandbox stores a federal verification program to verify the business data sent by the e-commerce platform. By deploying the data sandbox, the credibility of the data source can be improved.

[0058] In step S120, the e-commerce platform labels the merchant's business data and sends it to the data sandbox;

[0059] In the embodiment of the present application, the operating data includes sales, collection amount, return order data, total order quantity, inventory value and other data.

[0060] In the embodiment of the present application, the business data is classified according to its type, and different labeling rules are used to process different types of business data. For example, the business data can be divided into amount data and quantity data, wherein the amount data may include data related to amount such as sales, payment amount, and inventory value; the quantity data may include data related to quantity such as the total number of orders and the number of return orders. In order to carry out corresponding labeling processing on different types of data in a targeted manner, the business data is blurred through labels to hide the identity of the merchant, comply with the privacy protection policy of the e-commerce platform, and improve the security of the data transmission process.

[0061] In an embodiment of the present application, sensitive fields in the business data can also be desensitized, such as the name, address, contact information and other data of the two parties to the transaction. Specifically, a marking rule shared by the bank, the e-commerce platform and the customs system can be formulated. For example, taking the merchant name as an example, the merchants can be numbered in the order in which they enter the e-commerce platform as a label for the merchant.

[0062] In step S130, in the data sandbox, the labeled business data and the customs data corresponding to the e-commerce platform in the customs system are compared and verified to determine a data error value;

[0063] In the embodiment of the present application, the customs data includes the customs export list of the merchant in the preset time period, which records the goods exported by the merchant and the corresponding amount of the goods. Compared with other air transport, international express logistics and sea transport, it is a more suitable transportation mode with low price, so it has become the mainstream transportation mode currently chosen by cross-border e-commerce merchants, and the authority of customs data is higher. By comparing and verifying the customs data with the business data provided by the e-commerce platform, the credibility of the data can be improved.

[0064] As an implementation method of the present application, after obtaining the labeled business data, in order to facilitate comparison with the customs data, the labeled business data can be de-labeled, or the customs data can be labeled according to the same labeling rules and then compared to improve the accuracy of the data comparison.

[0065] In step S140, the labeled business data, the customs data and the data error value are field-merged to generate a data report, and the data report is sent to the bank risk control system;

[0066] In an embodiment of the present application, the data report may include merchant identification information, operating data, customs data, and data error values, so that the bank's risk control system can directly determine the credibility of the merchant's operating data based on the data report for subsequent processing.

[0067] In one embodiment of the present application, the data sandbox may store the generated data report in a storage database for subsequent backtracking.

[0068] In step S150, the bank risk control system screens out target merchants that meet preset credit rules based on the data report, and determines a credibility coefficient based on the data error value to determine the actual credit limit of the target merchant based on the credibility coefficient.

[0069] In the embodiment of the present application, the preset credit rules may include information such as the annual minimum sales amount, the average monthly payment amount, etc. After obtaining the data report, the annual sales amount, the average monthly payment amount, etc. of the merchant can be determined based on the merchant business data recorded in the data report. When the annual sales amount and the average monthly payment amount of the merchant reach the preset minimum sales amount and the average monthly payment amount, the merchant can be selected as the target merchant.

[0070] In an embodiment of the present application, after a merchant passes the access screening and credit screening, the credibility coefficient can be determined by the error value between the customs data and the operating data, and the actual credit limit can be determined by the credibility coefficient. The actual credit limit = the merchant's average monthly repayment amount × credibility coefficient.

[0071] In an implementation scenario of the present application, when the error value between the customs data and the merchant's operating data is within 20%, the actual credit limit = the merchant's average monthly repayment amount × 1.2; when the error value between the customs data and the merchant's operating data is between 20% and 40%, the actual credit limit = the merchant's average monthly repayment amount × 1.0; when the error value between the customs data and the merchant's operating data is above 40%, the actual credit limit = the merchant's average monthly repayment amount × 0.8.

[0072] The present application provides a cross-border e-commerce data asset management method, including: building a data sandbox in a customs system; the e-commerce platform labels the business data of merchants and sends it to the data sandbox; in the data sandbox, the labeled business data and the customs data corresponding to the e-commerce platform in the customs system are compared and verified to determine a data error value; the labeled business data, the customs data and the data error value are field-merged to generate a data report, and the data report is sent to a bank risk control system; the bank risk control system screens out target merchants that meet preset credit rules based on the data report, and determines a credibility coefficient based on the data error value, so as to determine the actual credit limit of the target merchant based on the credibility coefficient. This application, by establishing a data sandbox computing environment in the customs data, compares and verifies the labeled business data sent by the e-commerce platform with the customs data through the data sandbox, and forms a data report after confirming the data difference value and sends it to the bank risk control system, thereby improving the credibility of the business data. In addition, the bank risk control system can further determine the merchant's credit limit based on the merchant's credit status and data difference value, thereby improving the security and reliability of data transmission between financial institutions and e-commerce platforms, and can desensitize, interact and quality audit the business data in real time, with high efficiency, strong security, traceability, and a wider range of use.

[0073] This application also provides a cross-border e-commerce data asset management method implementation process, including the following steps:

[0074] In step S110, a data sandbox is constructed in the customs system;

[0075] In the embodiment of the present application, the data sandbox is a computing environment deployed in the customs system, and the data sandbox stores a federal verification program to verify the business data sent by the e-commerce platform. By deploying the data sandbox, the credibility of the data source can be improved.

[0076] In step S120, the e-commerce platform labels the merchant's business data and sends it to the data sandbox;

[0077] In the embodiment of the present application, the operating data includes sales, collection amount, return order data, total order quantity, inventory value and other data.

[0078] In an embodiment of the present application, the business data is classified according to its type, and different labeling rules are used to process different types of business data. For example, the above-mentioned business data can be divided into amount data and quantity data, wherein the amount data may include data related to the amount, such as sales, collection amount, and inventory value; the quantity data may include data related to quantity, such as the total number of orders and the number of return orders.

[0079] As an implementation method, the business data includes sales amount, payment amount and inventory value, and the labeling of the business data of the merchant includes:

[0080] Divide the sales amount, payment amount and inventory value by the preset value respectively, and round them up to form new amount data;

[0081] The new amount data is used as the business data label of the merchant.

[0082] Specifically, taking the sales amount as an example, the sales amount is X, and the preset value is Y, then For example, the sales of a merchant in the past month is 325410.00 yuan, and the preset value is 10000. After labeling, After rounding, the value is 32.

[0083] In the implementation of this application, the above-mentioned amount data can be labeled separately to hide the actual transaction amount and reduce the risk of data transmission.

[0084] As an implementation mode, the business data includes order volume data, and the tagging of the business data of the merchant includes:

[0085] The e-commerce platform divides the order volume into multiple intervals according to the order volume data distribution of each merchant, and numbers them;

[0086] The order volume interval to which the merchant belongs is determined according to the order volume data, and the interval number of the order volume interval to which the merchant belongs is used as the business data label of the merchant.

[0087] In the embodiment of the present application, the interval division can be performed according to the sales data distribution of each merchant on the e-commerce platform, so that the number of merchants falling into each interval is basically the same. For example, taking the order volume data as an example, after statistics are performed based on the monthly order volume data distribution of merchants on the e-commerce platform, the order volume micro data can be divided into 10 intervals, as shown below:

[0088] [0,n1),[n1,n2),...,[n8,n9),[n9,+∞)

[0089] Among them, n1 to n9 represent the order volume, where n1 < n2 <... < n9, and the number of merchants falling into each interval is basically the same.

[0090] Number each interval. For example, the number of the interval [0, n1) is 1, the number of the interval [n1, n2) is 2, and so on, numbering the above intervals in sequence. Then, based on the order volume data of the merchant, determine the interval to which the merchant belongs, and further determine the number of the interval to which the merchant belongs. For example, if it is number 3, then the label of the quantity-based business data of this merchant is 3.

[0091] In an embodiment of the present application, the quantity-based data may further include the number of return orders. The number of return orders can also be divided into intervals in the above manner. To avoid confusion between the interval numbers of order volume data and the interval numbers of return order numbers, they can be set to different numbers. For example, the numbers of order numbers can be D1, D2... D10, and the numbers of return order numbers can be T1, T2... T10, etc. Specifically, it can be set according to the actual situation, and the present application does not make any limitations here.

[0092] In step S130, in the data sandbox, compare and verify the labeled business data and the customs data corresponding to the e-commerce platform in the customs system to determine the data error value;

[0093] In an embodiment of the present application, the comparing and verifying the labeled business data and the customs data corresponding to the e-commerce platform in the customs system to determine the data error value includes:

[0094] Obtain the customs export list message associated with the merchant, and count the amount of goods in the customs export list message as the customs data;

[0095] Compare and verify the amount of goods with the business data to determine the data error value.

[0096] In an embodiment of the present application, the customs export list records the goods exported by the merchant and the corresponding amount of the goods. By counting the amount of the goods, the value of the goods exported by the merchant can be obtained.

[0097] In an embodiment of the present application, compare and verify the amount of goods with the business data to determine the data error value. Taking the sales amount as an example, for example, compare the sales amount (S1) of the merchant this month with the value of the goods (C3) in the customs export list to calculate the error value. The specific error value calculation method is as follows:

[0098]

[0099] Among them, when the error value is lower than a preset error value, such as 40%, the operating data can be considered credible.

[0100] In an implementation of the present application, the business data includes sales amount, and the obtaining of the customs export list message associated with the merchant and counting the amount of goods in the customs export list message includes:

[0101] Determine the sales time period for the sales amount;

[0102] According to the sales time period, the customs export list messages that are before a preset time period from the sales time are screened out from the customs export list messages, and the amounts of goods in the customs export list messages are counted.

[0103] Among them, the preset time period can be a specific time value, such as 1 month, 2 months, etc., which can be set according to actual conditions and is not limited in this application.

[0104] In the embodiment of the present application, since the transportation time of the sea transportation mode is relatively long, for example, the sea transportation time from China to the United States is usually 30-40 days, and the sales can be completed on the platform in about one month. Therefore, there is a certain time difference between the same batch of goods from customs declaration to cross-border e-commerce sales. Therefore, the customs export list messages of the previous two months before the merchant’s sales of the current month can be obtained for statistics, which can improve the accuracy of the data.

[0105] In step S140, the labeled business data, the customs data and the data error value are field-merged to generate a data report, and the data report is sent to the bank risk control system;

[0106] In an embodiment of the present application, the data report may include merchant identification information, operating data, customs data, and data error values, so that the bank's risk control system can directly determine the credibility of the merchant's operating data based on the data report for subsequent processing.

[0107] Among them, the customs data, electronic label data and data error value fields are combined and output, as shown in the following table:

[0108]

[0109] It can be seen from the above table that the error value between the merchant's sales and the value of the goods declared by the customs is 6.25%, which means that the business data provided by the merchant is credible. The above is only a display form of the data report, which can also be displayed through bar charts, waveform charts or text descriptions. In addition, the displayed data may also include the amount of repayment, inventory value, order quantity, return quantity and other data. The specific settings can be made according to the actual situation and are not limited in this application.

[0110] In step S150, the bank risk control system screens out target merchants that meet preset credit rules based on the data report, and determines a credibility coefficient based on the data error value to determine the actual credit limit of the target merchant based on the credibility coefficient.

[0111] In an embodiment of the present application, target merchants that meet preset credit rules are screened out according to the data report, including:

[0112] Determining whether the business data of the merchant reaches a preset business value according to the data report;

[0113] When the result is yes, the credit information of the merchant is obtained;

[0114] Determining whether the merchant's credit information has a credit risk based on the credit information;

[0115] When the judgment result is no, the merchant is taken as the target merchant.

[0116] Among them, the preset operating values ​​include the merchant’s minimum annual sales, the merchant’s average monthly repayment amount, the merchant’s minimum monthly order volume, etc.

[0117] Specifically, after obtaining the data report, the bank's risk control system can screen the merchants for access based on the merchants' operating data, for example, whether the merchant's annual sales have reached the preset sales quota, whether there are any abnormalities in the merchant's return orders, such as large-scale returns, to avoid false orders, for example, whether the merchant's inventory value is lower than the preset inventory level, etc.

[0118] Furthermore, after a merchant passes the access screening, the merchant's credit record can be obtained, for example, whether there is any overdue payment, whether there is a large loan record, whether the merchant is a blacklisted user, whether there is any illegal behavior, etc., and the merchant's credit screening can be carried out to determine whether the merchant has a risk of default, so as to eliminate customers with excessive loan risks.

[0119] In an embodiment of the present application, after a merchant passes the access screening and credit screening, the credibility coefficient can be determined by the error value between the customs data and the operating data, and the actual credit limit can be determined by the credibility coefficient. The actual credit limit = the merchant's average monthly repayment amount × credibility coefficient.

[0120] In an implementation scenario of the present application, when the error value between the customs data and the merchant's operating data is within 20%, the actual credit limit = the merchant's average monthly repayment amount × 1.2; when the error value between the customs data and the merchant's operating data is between 20% and 40%, the actual credit limit = the merchant's average monthly repayment amount × 1.0; when the error value between the customs data and the merchant's operating data is above 40%, the actual credit limit = the merchant's average monthly repayment amount × 0.8.

[0121] In an embodiment of the present application, after the bank risk control system evaluates the credit status of the merchant based on the data report to determine the actual credit limit of the merchant, it includes:

[0122] After the merchant obtains a loan according to the actual credit limit, the bank risk control system obtains the merchant's post-loan performance data;

[0123] Sample data is formed according to the data report and the post-loan performance data.

[0124] In one embodiment of the present application, after a merchant successfully obtains a loan, the bank can perform historical data backtracking. Specifically, it will collect the merchant's post-loan performance data and compare the post-loan performance data with the merchant's operating data at the time of credit granting to determine whether the credit service is safe and whether there are risks, so as to optimize the risk control model in a timely manner.

[0125] Among them, post-loan performance data includes whether the merchant repays the loan on time, whether there are any operating risks in the merchant's operating data, etc.

[0126] Furthermore, when performing backtracking, the bank risk control system may send a backtracking request to the data sandbox, which may include a data report identifier or a merchant identifier, so that the data sandbox sends a corresponding data report to the bank risk control system.

[0127] This application, by establishing a data sandbox computing environment in the customs data, compares and verifies the labeled business data sent by the e-commerce platform with the customs data through the data sandbox, and forms a data report after confirming the data difference value and sends it to the bank risk control system, thereby improving the credibility of the business data. In addition, the bank risk control system can further determine the merchant's credit limit based on the merchant's credit status and data difference value, thereby improving the security and reliability of data transmission between financial institutions and e-commerce platforms, and can desensitize, interact and quality audit the business data in real time, with high efficiency, strong security, traceability, and a wider range of use.

[0128] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0129] In one embodiment, a cross-border e-commerce data asset management system is provided, and the cross-border e-commerce data asset management system corresponds one-to-one to the cross-border e-commerce data asset management method in the above embodiment. Figure 3 As shown, the cross-border e-commerce data asset management system includes an e-commerce platform 10, a customs system 20 and a bank risk control system 30. The detailed description is as follows:

[0130] Customs system 20, used to build a data sandbox;

[0131] The e-commerce platform 10 is used to label the business data of the merchants and send it to the data sandbox;

[0132] The customs system 20 is further used to compare and verify the labeled business data and the customs data corresponding to the e-commerce platform in the customs system in the data sandbox to determine the data error value;

[0133] The customs system 20 is further used to merge the labeled business data, the customs data and the data error value in the data sandbox to generate a data report, and send the data report to the bank risk control system;

[0134] The bank risk control system 30 is used to screen out target merchants that meet preset credit rules based on the data report, and determine a credibility coefficient based on the data error value to determine the actual credit limit of the target merchant based on the credibility coefficient.

[0135] In one embodiment, the business data includes sales amount, payment amount and inventory value, and the e-commerce platform 10 is also used to:

[0136] Divide the sales amount, payment amount and inventory value by the preset value respectively, and round them up to form new amount data;

[0137] The new amount data is used as the business data label of the merchant.

[0138] In one embodiment, the business data includes order volume data, and the e-commerce platform is further used to:

[0139] The e-commerce platform 10 divides the order volume intervals into multiple intervals according to the order volume data distribution of each merchant, and numbers them;

[0140] The order volume interval to which the merchant belongs is determined according to the order volume data, and the interval number of the order volume interval to which the merchant belongs is used as the business data label of the merchant.

[0141] In one embodiment of the present application, the customs system 20 is further used for:

[0142] Obtaining the customs export list message associated with the merchant, and counting the amount of goods in the customs export list message as the customs data;

[0143] The commodity amount is compared and verified with the operating data to determine the data error value.

[0144] In one embodiment of the present application, the customs system 20 is further used for:

[0145] Determine the sales time period for the sales amount;

[0146] According to the sales time period, the customs export list messages that are before a preset time period from the sales time are screened out from the customs export list messages, and the amounts of goods in the customs export list messages are counted.

[0147] In one embodiment of the present application, the bank risk control system 30 is also used for:

[0148] Determining whether the business data of the merchant reaches a preset business value according to the data report;

[0149] When the result is yes, the credit information of the merchant is obtained;

[0150] Determining whether the merchant's credit information has a credit risk based on the credit information;

[0151] When the judgment result is no, the merchant is taken as the target merchant.

[0152] In one embodiment of the present application, the bank risk control system 30 is also used for:

[0153] After the merchant obtains a loan according to the actual credit limit, obtaining post-loan performance data of the merchant;

[0154] Sample data is formed according to the data report and the post-loan performance data.

[0155] This application, by establishing a data sandbox computing environment in the customs data, compares and verifies the labeled business data sent by the e-commerce platform with the customs data through the data sandbox, and forms a data report after confirming the data difference value and sends it to the bank risk control system, thereby improving the credibility of the business data. In addition, the bank risk control system can further determine the merchant's credit limit based on the merchant's credit status and data difference value, thereby improving the security and reliability of data transmission between financial institutions and e-commerce platforms, and can desensitize, interact and quality audit the business data in real time, with high efficiency, strong security, traceability, and a wider range of use.

[0156] For the specific limitations of the cross-border e-commerce data asset management system, please refer to the limitations of the cross-border e-commerce data asset management method above, which will not be repeated here. Each module in the above-mentioned cross-border e-commerce data asset management system can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0157] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium. The readable storage medium stores computer-readable instructions. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a cross-border e-commerce data asset management method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0158] A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned cross-border e-commerce data asset management method when executing the computer-readable instructions.

[0159] One or more readable storage media, wherein the readable storage medium stores computer-readable instructions, wherein the computer-readable instructions, when executed by a processor, implement the steps of the cross-border e-commerce data asset management method as described above.

[0160] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0161] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0162] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A cross-border e-commerce data asset management method, characterized in that: The method comprises: Building a data sandbox in the customs system; The e-commerce platform labels the merchant's business data and sends it to the data sandbox; In the data sandbox, the labeled business data and the customs data corresponding to the e-commerce platform in the customs system are compared and verified to determine the data error value, where the business data is the sales amount and the customs data is the goods amount; Merging the labeled business data, the customs data, and the data error value into fields to generate a data report, and sending the data report to the bank risk control system; The bank risk control system screens out target merchants that meet preset credit rules based on the data report, and determines a credibility coefficient based on the data error value, so as to determine the actual credit limit of the target merchant based on the credibility coefficient.

2. The cross-border e-commerce data asset management method according to claim 1, characterized in that: The labeling of the merchant's business data includes: Divide the sales amount by a preset value and round it to form a new amount data; The new amount data is used as the business data label of the merchant.

3. The cross-border e-commerce data asset management method according to claim 1, characterized in that: Comparing and verifying the labeled business data with the customs data in the customs system corresponding to the e-commerce platform to determine the data error value includes: Obtaining the customs export list message associated with the merchant, and counting the amount of goods in the customs export list message as the customs data; The commodity amount is compared and verified with the operating data to determine the data error value.

4. The cross-border e-commerce data asset management method according to claim 3, characterized in that: The obtaining of the customs export list message associated with the merchant and counting the amount of goods in the customs export list message includes: The sales time period to determine the sales amount; According to the sales time period, the customs export list messages that are before a preset time period from the sales time are screened out from the customs export list messages, and the amounts of goods in the customs export list messages are counted.

5. The cross-border e-commerce data asset management method according to claim 1, characterized in that: The step of screening out target merchants that meet preset credit rules according to the data report includes: Determining whether the business data of the merchant reaches a preset business value according to the data report; When the result is yes, the credit information of the merchant is obtained; Determining whether the merchant's credit information has a credit risk based on the credit information; When the judgment result is no, the merchant is taken as the target merchant.

6. The cross-border e-commerce data asset management method according to any one of claims 1 to 5, characterized in that: After the bank risk control system evaluates the credit status of the merchant based on the data report to determine the actual credit limit of the merchant, it includes: After the merchant obtains a loan according to the actual credit limit, the bank risk control system obtains the merchant's post-loan performance data; Sample data is formed according to the data report and the post-loan performance data.

7. A cross-border e-commerce data asset management system, characterized in that: The system comprises Customs system for building data sandbox; The e-commerce platform is used to label the business data of merchants and send it to the data sandbox; The customs system is further used to compare and verify the labeled business data and the customs data corresponding to the e-commerce platform in the customs system in the data sandbox to determine a data error value, wherein the business data is the sales amount and the customs data is the goods amount; The customs system is further used to merge the labeled business data, the customs data and the data error value in the data sandbox to generate a data report, and send the data report to the bank risk control system; The bank risk control system is used to screen out target merchants that meet preset credit rules based on the data report, and determine a credibility coefficient based on the data error value, so as to determine the actual credit limit of the target merchant based on the credibility coefficient.

8. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the steps of the cross-border e-commerce data asset management method as described in any one of claims 1 to 6 are implemented.

9. One or more readable storage media storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by the processor, the steps of the cross-border e-commerce data asset management method as described in any one of claims 1 to 6 are implemented.

Images