KR20230119356A - Method and system for operating pre-settlement service with risk management - Google Patents

Abstract

In a method and system for operating a pre-calculation service capable of risk management, a pre-calculation application signal including business data associated with the seller, shopping mall account data, and pre-calculation application data is received from a seller terminal of the seller, and based on the pre-calculation application signal scrape the pre-sale data associated with the seller, determine the eligibility for the seller's pre-sale service based on the pre-sale data, calculate the seller's pre-calculation usage limit based on the pre-calculation application signal and the pre-sale data, Based on the pre-calculated usage limit and the pre-calculated application amount, the seller's pre-calculated usage amount is calculated, an account transfer request signal requesting the transfer of the pre-calculated usage amount to the seller's selected balance payment account is generated, and the account transfer By transmitting the request signal to the bank server, it is possible to provide a pre-calculation service for paying the pre-calculation usage amount to the seller.

Description

Method and system for operating pre-settlement service with risk management}

It relates to a method and system for operating a pre-settlement service in which a seller of a product registered in an online shopping mall pre-calculates and pays an unsettled price from an online shopping mall. It relates to a method and system for operating a pre-calculation service capable of risk management that can reduce the risk of not recovering the pre-calculation amount by monitoring the abnormality of the seller by scraping and analyzing sales data related to the seller.

With the recent popularization of the Internet, consumers are purchasing products using various types of online sales methods, and sellers are operating after a certain period of time has elapsed after receiving settlements from online vendors for sales of products.

Each online retailer generally uses a method of settling the sales price for the product to the seller when delivery is completed after the seller's product is sold.

In online shopping malls, it usually takes 1 to 2 months after the product is sold for the seller to receive the remittance of the sale price through the above-mentioned settlement method. there is a problem.

Conventionally, there is a pre-calculation method in which a commercial bank or a fund company pre-settles a seller with the sales amount as collateral, and collects the sales amount from the seller at a later point of settlement. In this way, various types of risks of not being able to collect the payment paid to the seller in advance are placed on the settlement payment service implementer. In addition, sellers with low credit ratings cannot apply for loans, and there are sellers who are reluctant to use the existing pre-calculation method because loans affect credit ratings. In addition, since the above-described existing pre-calculation method makes it difficult to proceed with loans outside bank business hours, many online sellers have a problem in that it is difficult to use the existing pre-calculation method.

In addition, the conventional method of pre-calculation has the inconvenience of having to apply to the pre-calculation company on a daily, weekly or monthly basis for the sales price from the seller to the online retailer.

Republic of Korea Patent Publication No. 10-2021-0116887 Republic of Korea Patent Registration No. 10-2153405

It is to provide a method and system for operating a pre-calculation service capable of risk management. In addition, it is not limited to the technical problems as described above, and another technical problem may be derived from the following description.

A method for operating a pre-calculation service according to an embodiment of the present invention includes the step of receiving a pre-calculation application signal including business data associated with the seller, shopping mall account data, and pre-calculation application data from a seller terminal of a seller - the business data includes the number of products, product categories, and business registration numbers that the seller is selling through the online shopping mall, the shopping mall account data includes a list of online shopping malls that the seller is selling, and the pre-calculation application data is the pre-calculation application amount and accounts for payment of pre-determined payments ―; scraping pre-sale data associated with the seller based on the pre-sale request signal, and determining eligibility for using the pre-sale service of the seller based on the pre-sale data; calculating a pre-calculation limit amount of the seller based on the pre-calculation application signal and the pre-sale data, when it is determined that the seller is eligible for the pre-calculation service; Calculating a pre-calculation usage amount of the seller based on the pre-calculation usage limit amount and the pre-calculation application amount; generating an account transfer request signal for requesting a transfer of the pre-calculated amount to a pre-selected balance payment account of the seller; and transmitting the account transfer request signal to a bank server.

The pre-sale data includes order quantity for the previous 6 months from the date of application for the pre-sale service, sales for the previous 6 months, monthly sales for the previous 6 months, and seller ratings. scraping pre-sale data associated with the seller from each online shopping mall server included in the online shopping mall list, based on shopping mall account data; and determining eligibility to use the pre-sale service of the seller based on the scraped pre-sale data.

Based on the scraped pre-sale data, the step of determining the seller's eligibility to use the pre-sales service includes the seller's total pre-order quantity, total pre-sales, total pre-monthly average sales, and An overall seller rating is calculated, and eligibility for using the pre-calculation service of the seller is determined based on the calculated total number of pre-orders, total pre-order sales, average monthly sales total, and total seller ratings.

A method for operating a pre-calculation service according to an embodiment of the present invention includes receiving a pre-calculation payment completion signal from the bank server in response to the account transfer request signal; and scraping follow-up sales data related to the seller when the pre-production payment completion signal is received, and determining whether an abnormal symptom occurs in the seller based on the scraped follow-up sales data and the pre-sale data. include

Determining whether or not the abnormality of the seller has occurred is the step of scraping follow-up sales data of the seller from all online shopping mall servers included in the online shopping mall list after receiving the pre-production payment completion signal - the follow-up sales data includes monthly order quantity, monthly sales, and number of returns per month after payment of pre-calculated usage amount ―; calculating a current status score of the seller based on the subsequent sales data and the pre-sale data; and determining whether or not an abnormal symptom has occurred in the seller based on the current state score of the seller.

The step of calculating the current state score of the seller may include calculating the current state score of the seller based on the subsequent sales data and the pre-sale data by using an artificial neural network previously trained by a training data set. A state score is calculated, and the training data set includes a plurality of pre-sale data for learning, a plurality of follow-up sales data for learning, and a current state score for learning.

In the step of calculating the current state score of the seller, based on the follow-up sales data, the seller's total number of subsequent monthly orders, total subsequent monthly sales, total subsequent monthly returns, and total subsequent monthly product return rates are calculated; A risk variable is calculated based on the number of pre-orders, the average sales per month for all prior months, the number of orders per month for all follow-ups, the number of sales per month for all follow-ups, the number of returns per month for all follow-ups, and the rate of product returns per month for all follow-up, and the risk variable and the total seller Based on the ratings, a current status score for the seller is calculated.

Determining whether the seller has an abnormal symptom based on the current state score of the seller compares the current state score with a preset reference state score, and if the current state score is less than the reference state score as a result of the comparison, the It is determined that an anomaly has occurred with the seller, and the baseline condition score varies depending on the product category.

The operation server 11 that executes the pre-calculation service operation method according to another embodiment of the present invention includes a pre-calculation application signal including business data related to the seller, shopping mall account data, and pre-calculation application data from the seller terminal of the seller. Communication module 1102 for receiving ― the business data includes the number of products sold by the seller through the online shopping mall, product categories, and business registration number, and the shopping mall account data includes a list of online shopping malls sold by the seller and the pre-calculation application data includes the pre-calculation application amount and the pre-calculation payment account; a use eligibility determination module 1104 for scraping pre-sales data associated with the seller based on the pre-sales request signal and determining eligibility for the pre-sales service of the seller based on the pre-sales data; When it is determined that the seller is eligible to use the pre-calculation service, the pre-calculation usage limit amount of the seller is calculated based on the pre-calculation application signal and the pre-sale data, and the pre-calculation usage limit amount and the pre-calculation application amount Based on, the pre-calculated amount calculation module 1105 for calculating the pre-calculated usage amount of the seller; and a pre-calculation execution module 1106 generating an account transfer request signal for requesting a transfer of the pre-calculation usage amount to a selected balance payment account of the seller, wherein the communication module 1102 transmits the account transfer request signal sent to the bank server.

A computer readable recording medium according to another embodiment of the present invention is recorded with a program for performing a method for operating a pre-calculation service according to an embodiment of the present invention.

In the pre-calculation service operation method, a seller who sells through an online shopping mall pays the pre-calculation usage amount to the seller in advance before receiving the settlement from the online shopping mall, so that the seller, who is a small business owner, can quickly provide operating funds.

In addition, the present invention collects various types of data related to the seller, such as the seller's total sales, order quantity, and rating, from each online shopping mall server of the seller in the operation server, and determines eligibility for using the pre-calculation service based on the collected sales data. The risk of not recovering the pre-calculated amount can be minimized by determining and paying the pre-calculated amount only to sellers who are eligible for use.

In addition, the present invention scrapes data related to the seller's product sales after paying the pre-calculated usage amount to the seller, and analyzes the scraped data to monitor whether abnormal symptoms have occurred economically or in business to the seller. . The operation server may monitor whether an abnormal symptom occurs in the seller after the payment of the pre-calculated amount, and if an abnormal symptom occurs, the seller may notify the user of the operation server that the abnormal symptom has occurred. It is possible to prevent the risk of not recovering the amount paid by pre-calculation by continuously monitoring that anomalies have occurred to the seller while using the pre-calculation service, and blocking the use of the pre-calculation service later for the seller with the abnormal symptom. .

1 is a diagram illustrating a pre-calculation service operating system capable of risk management according to an embodiment of the present invention.
FIG. 2 is a configuration diagram of an operating server of the pre-calculation service operating system shown in FIG. 1 .
3 is a flowchart of a method for operating a pre-calculation service capable of risk management performed in the pre-calculation service operating system capable of risk management shown in FIG.
Figure 4 is a detailed flow chart of the step of determining eligibility for the pre-calculation service of the seller shown in Figure 3;
FIG. 5 is a detailed flowchart illustrating the step of scraping the follow-up sales data shown in FIG. 3 and determining whether or not an abnormal symptom has occurred in the seller based on the scraped follow-up sales data and pre-sale data.

Advantages and features of the present disclosure, and methods of achieving them, will become clear with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the technical idea of the present disclosure is not limited to the following embodiments and can be implemented in various different forms, and only the following embodiments complete the technical idea of the present disclosure, and in the technical field to which the present disclosure belongs. It is provided to completely inform those skilled in the art of the scope of the present disclosure, and the technical spirit of the present disclosure is only defined by the scope of the claims.

In adding reference numerals to components of each drawing, it should be noted that the same components have the same numerals as much as possible even if they are displayed on different drawings. In addition, in describing the present disclosure, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the present disclosure, the detailed description will be omitted.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those of ordinary skill in the art to which this disclosure belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined. Terminology used herein is for describing the embodiments and is not intended to limit the present disclosure. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase.

In addition, in describing the components of the present disclosure, terms such as first, second, A, B, (a), and (b) may be used. These terms are only used to distinguish the component from other components, and the nature, order, or order of the corresponding component is not limited by the term. When an element is described as being “connected,” “coupled to,” or “connected” to another element, that element is directly connected or connectable to the other element, but there is another element between the elements. It will be understood that elements may be “connected”, “coupled” or “connected”.

As used in this disclosure, "comprises" and/or "comprising" means that a stated component, step, operation, and/or element is one or more other components, steps, operations, and/or elements. Existence or additions are not excluded.

Elements included in one embodiment and elements having common functions may be described using the same names in other embodiments. Unless otherwise stated, described in any one embodiment The description may also be applied to other embodiments, and detailed descriptions may be omitted to the extent that they overlap or that those skilled in the art can clearly understand.

Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

Since the present invention can make various changes and have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and it should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

The present invention relates to a method for operating a pre-calculation service for pre-calculating the uncalculated sales price of a seller who sells a product through an online shopping mall. More specifically, by monitoring whether abnormal symptoms have occurred in the seller who is currently using the pre-calculation service, by limiting the use of the pre-calculation service later, it is possible to prevent a situation in which it is difficult to recover the pre-calculation amount that may occur in the pre-calculation service. It can be prevented.

Here, the pre-calculation service pays the seller who sells various products through the online shopping mall in advance, instead of the online shopping mall, before receiving the settlement from the online shopping mall, and after receiving the settlement from the online shopping mall. It means a service for which a certain fee is received along with the amount paid in advance. As described above, in the case of small business owners who sell products through online shopping malls, if the sales proceeds are not quickly settled, there is a very high possibility that they will not be able to secure cash liquidity and experience difficulties in business operation.

1 is a diagram illustrating a pre-calculation service operating system capable of risk management according to an embodiment of the present invention. Referring to FIG. 1, a pre-calculation service operating system capable of risk management (1) is composed of an operating server 11, a seller terminal 12, and an online shopping mall server 13. The pre-calculation service operating system 1 may include at least one seller terminal 12 and at least one online shopping mall server 13 .

The operation server 11 provides a pre-calculation service between the seller's terminal 12 and the online shopping mall server 13. The operation server 11 may be composed of one computer or a set of a plurality of computers.

The seller terminal 12 is a terminal of a seller requesting a pre-calculation service in which the sales price is paid in advance before the sales price for selling a product through an online shopping mall is settled from the shopping mall through the pre-calculation service, and the seller is a seller terminal ( 12) You can apply for pre-calculation service. The seller terminal 12 is a personal portable terminal capable of communication, such as a smart phone, tablet PC, PDA (Personal Digital Assistants), and the like.

The online shopping mall server 13 is a server that operates an Internet mediation platform open to both sellers and buyers, and provides an open market service through which individuals and/or sellers can freely sell products to buyers online. Representative examples of providing open market services include Timon ^TM , WeMakePrice ^TM , Interpark ^TM , and Coupang ^TM . The online shopping mall server 11 receives the product sales price from the buyer and settles the sales price excluding a predetermined fee to the seller. The online shopping mall server 13 may be composed of one computer or a set of a plurality of computers.

A method of operating a pre-calculation service capable of risk management performed in the pre-calculation service operating system including the operation server 11, the seller terminal 12, and the online shopping mall server 13 will be described in detail with reference to the drawings below. do it with

FIG. 2 is a configuration diagram of an operating server of the pre-calculation service operating system shown in FIG. 1 . Referring to FIG. 2, the operation server 11 includes a processor 1101, a communication module 1102, a storage 1103, a use eligibility determination module 1104, a predetermined calculation amount calculation module 1105, and a predetermined calculation execution module ( 1106), a seller monitoring module 1107, a danger signal generating module 1108 and an output module 1109. Operation server 11 may further include other components other than the above-described components.

The processor 1101 of the operation server 11 processes general tasks of the operation server 11 as a central processing unit of the operation server 11 . The communication module 1102 of the operating server 11 connects to a mobile communication base station or a Wi-Fi repeater and transmits data to the seller terminal 12, the online shopping mall server 13, and an external bank server (not shown) through a wide area network such as the Internet. and send and receive messages.

The storage 1103 of the operation server 11 stores data necessary for executing a method of operating a pre-calculation service capable of risk management. Storage 1103 contains training data sets for training an artificial neural network. Here, the training data set includes a plurality of pre-sale data for learning, a plurality of follow-up sales data for learning, and a current state score for learning. The artificial neural network described above will be described in detail below.

The use eligibility determination module 1104 of the operation server 11 is a module that determines whether a seller who has applied for the pre-calculation service is entitled to use the pre-calculation service. The use eligibility determination module 1104 determines the eligibility of the seller to use the pre-calculation service based on the pre-calculation request signal received from the seller terminal 12 . A specific method for determining the eligibility to use the pre-calculation service of the seller in the eligibility determination module 1104 will be described in detail below.

The pre-calculation amount calculation module 1105 of the operation server 11 is a module that calculates the pre-calculation usage amount for a seller who has applied for the pre-calculation service. When it is determined that the seller is eligible for the pre-calculation service, the pre-calculation amount calculation module 1105 calculates the seller's pre-calculation usage limit based on pre-sale data and pre-calculation application data associated with the seller. A specific method for calculating the pre-calculated credit card amount will be described in detail below.

The pre-calculation execution module 1106 of the operation server 11 is a module for executing the pre-calculation service. The pre-calculation execution module 1106 generates an account transfer request signal for requesting a transfer of the pre-calculation use amount to an account designated by the seller for a seller who is qualified for the use of the pre-calculation service, and transmits the generated account transfer request signal to a communication module ( 1102) to an external bank server (not shown) to perform pre-calculation. Details of the pre-calculation execution module 1106 will be described in detail below.

The seller monitoring module 1107 of the operation server 11 scrapes data on products sold by the seller from each online shopping mall server after executing the pre-calculation service, that is, after paying the pre-calculation usage amount, The scraped data is analyzed to monitor whether any abnormalities have occurred in the economic situation and/or business status of the seller after the pre-calculation service has been executed. A specific method for determining whether an abnormal symptom has occurred in the seller in the seller monitoring module 1107 will be described in detail below.

When the risk signal generation module 1108 of the operation server 11 determines that an abnormal symptom has occurred to the seller after the pre-calculation usage amount has been paid, the risk signal is used to inform the seller that the abnormal symptom has occurred while using the pre-calculation service. This module generates notification signals. The danger signal generation module 1108 generates a danger notification signal for notifying the user of the operation server 11 (eg, an operator operating a pre-calculation service) that an anomaly has occurred, and the generated danger notification signal. to the output module 1109. A specific method for generating a danger notification signal will be described in detail below.

The output module 1109 of the operation server 11 outputs the danger notification signal generated by the operation server 11 . Examples of the output module 1109 include a monitor, a speaker, a flash, a vibration actuator, and the like. For example, the output module 1109 provides the user of the operation server 11 with the seller's information and information based on a risk notification signal indicating that an abnormality has occurred, such as a change in the seller's economic situation and/or business operation status. It may be a monitor outputting danger information.

In the operation server 11, the use eligibility determination module 1104, the selection calculation amount calculation module 1105, the selection calculation execution module 1106, the seller monitoring module 1107, and the danger signal generation module 1108 are processor 1101 It may be implemented by a separate dedicated processor different from the processor 1101, and may be implemented by a computer program running on the processor 1101.

The operation server 11 may further include other components other than the components described above. For example, the operating server 11 may further include a bus for transmitting data between each component, as shown in FIG. 2 . In addition to these components, the operation server 11 may further include a power supply unit for supplying driving power to each component.

3 is a flowchart of a method for operating a pre-calculation service capable of risk management performed in the pre-calculation service operating system capable of risk management shown in FIG. It is assumed that the pre-calculation service operation method shown in FIG. 3 is implemented on the pre-calculation service operating system shown in FIG.

Referring to FIG. 3 , in step 301 , the operation server 11 receives a pre-calculation request signal from the seller terminal 12 . The seller terminal 12 transmits a pre-calculation request signal to the operation server 11 . More specifically, the seller terminal 12 transmits a pre-calculation request signal including seller business data, shopping mall account data, and pre-calculation application data to the operation server 11 through the communication module 1202 .

Here, the business data of the seller is data related to the seller that the seller sells through the online shopping mall, and includes the number of products on sale, category of products on sale, business registration number, representative name, and the like. The seller's shopping mall account data includes a list of online shopping malls in which the seller is selling, a seller ID of each online shopping mall, and the like. The communication module 1102 of the operation server 11 receives a pre-calculation request signal including operator data and shopping mall account data from the seller terminal 12 . The pre-calculation application data is data representing information about the details of the pre-calculation service requested by the seller, and includes the pre-calculation application amount and the account number for the pre-calculation payment.

A seller selling a product through an online shopping mall may apply for a pre-calculation service using the seller terminal 12 . The seller may input the seller's operator data and shopping mall account data through the input module 1204 (eg, mouse, keyboard, touchpad, etc.) of the seller terminal 12 . The seller terminal 12 transmits a manager mountain application signal including the operator data and shopping mall account data obtained in the above-described manner to the operation server 11 through the communication module 1202 .

In step 302, the operation server 11 scrapes pre-sale data related to the seller based on the pre-calculation request signal transmitted from the seller terminal 12, and determines the eligibility of the seller to use the pre-calculation service based on the pre-sale data. . The use eligibility determination module 1104 of the operation server 11 determines the seller's pre-calculation service eligibility based on the operator data and shopping mall account data included in the pre-calculation request signal. The step of determining eligibility to use the pre-calculation service is a step of determining whether the seller has the eligibility to use the pre-calculation service.

The operation server 11 minimizes the risk of not being able to collect the pre-paid sales price by pre-paying the unsettled sales price from the online shopping mall only to the seller determined to be eligible for the pre-calculation service.

According to an embodiment of the present invention, the use eligibility determination module 1104 determines whether the business registration number included in the seller's business data and the business registration number associated with the seller's ID of the online shopping mall match the seller's pre-calculation service. determine eligibility. The use eligibility determination module 1104 determines that the seller is eligible for the pre-calculation service when the business registration number included in the business data and the business registration number associated with the seller ID of the online shopping mall match. The use eligibility determination module 1104 determines otherwise, that is, when the business registration number included in the business data and the business registration number associated with the seller ID of the online shopping mall do not match, the seller is not eligible to use the pre-calculation service.

In this regard, FIG. 4 is a detailed flowchart of the step of determining eligibility for using the pre-calculation service of the seller shown in FIG. 3 . Referring to FIG. 4 , in step 3021, the usage eligibility determination module 1104 scrapes pre-sale data associated with the seller from the online shopping mall server 13 based on the shopping mall account data. More specifically, the usage eligibility determination module 1104 scrapes pre-sale data associated with the seller from each online shopping mall server 13 in the online shopping mall list included in the seller's shopping mall account data.

Here, the pre-sale data is data related to products sold by the seller through each online shopping mall, including total order quantity for the previous 6 months from the date of application, sales during the previous 6 months from the date of application, monthly sales for the previous 6 months from the date of application, and seller. including ratings, etc. The seller rating is a score evaluated by a plurality of buyers who have purchased the product.

For example, the operation server 11 transmits a signal requesting pre-sale data to each online shopping mall server 13 and receives sales data associated with a seller from each online shopping mall server 13 . The use eligibility determination module 1104 of the operating server 11 may scrape sales data from pre-sale data received from each online shopping mall server 13 .

In step 3022, the operation server 11 determines the eligibility of the seller to use the pre-sale service based on the scrapped seller's pre-sale data. In step 3021, the eligibility determination module 1104 of the operation server 11 determines eligibility for the pre-sale service of the seller based on the scrapped pre-sale data of the seller.

More specifically, the usage eligibility determination module 1104 determines the total pre-order quantity, total pre-sales, and total pre-order months for the preceding 6 months from the date of application, based on the seller's pre-sale data received from each online shopping mall server 13. Calculate average sales and overall seller ratings.

Here, the total pre-order quantity is the total order quantity obtained by adding up the order quantity ordered through the seller ID in each online shopping mall during the previous 6 months from the date of application for the pre-calculation service. In other words, the total pre-order quantity means the total order quantity of the seller, not the order volume of each online shopping mall.

Total advance sales is the total sales of all sales of seller IDs in each online shopping mall for the six months prior to the application date for pre-calculation service. In other words, the total advance sales mean the total sales of the seller, not the sales in each online shopping mall.

The total advance monthly average sales is the seller's total monthly average sales, which is the sum of all average monthly sales at each online shopping mall for the six months prior to the application date for the pre-calculation service. In other words, the total monthly average sales mean the total monthly average sales of sellers, not the average monthly sales in each online shopping mall.

The overall seller rating is the average value of the seller ratings associated with the seller ID in each online shopping mall, converted to a scale of 100, and the seller ratings of the seller IDs of all online shopping malls included in the online shopping mall list included in the seller's shopping mall account data. . In other words, the overall seller rating is the average value of seller ratings in all online shopping malls where the seller sells products.

The usage eligibility determination module 1104 determines the total number of pre-orders, total pre-order sales, total monthly average sales, and total pre-orders for the previous 6 months from the date of application calculated based on the seller's pre-sale data scraped from each online shopping mall server 13. Based on the seller's rating, the seller's eligibility for pre-calculation service is determined.

For example, the use eligibility determination module 1104 determines if the total pre-order quantity for the previous 6 months from the application date is less than 100, if the total pre-order sales for the previous 6 months from the application date is less than 10 million won, and for the entire prior 6 months from the application date. If the average monthly sales in advance is less than KRW 1 million and the overall seller rating is less than 85 points out of 100 points, if any of the sellers is applicable, it is determined that the seller is not eligible to use the selection service.

The use eligibility determination module 1104 is used when the total number of orders for the previous 6 months from the date of application is less than 100, when the total sales during the 6 months prior to the application date is less than 10 million won, and when the monthly sales during the 6 months prior to the date of application are less than 1 million won If the seller does not fall under any of the above, and the seller's rating is less than 85 points out of 100, it is determined that the seller's use of the selection service is sniper. If the seller who applied for the pre-calculation service is recognized as eligible for the pre-calculation service, proceed to step 303.

If the seller's eligibility to use the pre-calculation service is not recognized, the method of operating the pre-calculation service that allows risk management is terminated.

In step 303, if it is determined that the seller is eligible to use the pre-calculation service, the operation server 11 calculates the pre-calculation limit amount of the seller based on the pre-sale data associated with the seller and the pre-calculation application data of the seller. The pre-calculation amount calculation module 1105 of the operation server 11 calculates the pre-calculation limit amount of the seller based on the entire preliminary monthly average sales and overall seller ratings for the previous 6 months from the application date included in the sales data associated with the seller. yield

According to one embodiment of the present invention, the pre-calculated amount calculation module 1105 calculates the average monthly sales for the entire six months prior to the application date and calculates a weight based on the seller's rating. The pre-calculation amount calculation module 1105 multiplies the calculated monthly average sales in advance by a weight to calculate the pre-calculation limit amount of the seller. If the total sales for 6 months is 66 million won and the seller's rating is 90 points out of 100 points, the pre-calculated amount calculation module 1105 calculates the total pre-month average sales as 11 million won and calculates the weight as 0.9. The pre-calculation amount calculation module 1105 multiplies the calculated 11 million won by 0.9 to calculate 9.9 million won as the pre-calculation usage limit of the seller.

In step 304, the operation server 11 calculates the pre-calculation usage amount based on the calculated pre-calculation usage limit and the seller's pre-calculation application amount. The pre-calculation calculation module 1105 of the operation server 11 compares the pre-calculation application amount included in the pre-calculation application data transmitted from the seller with the pre-calculation usage limit calculated in step 303, and uses the calculated pre-calculation amount. Among the plating amounts, the smaller amount among the applied amounts for pre-calculation is calculated as the pre-calculation usage amount. The pre-calculation amount calculation module 1105 inputs the calculated pre-calculation usage amount of the seller to the pre-calculation execution module 1106 .

In step 305, the operation server 11 generates an account transfer request signal requesting a transfer of the determined pre-calculation usage amount to the seller's pre-calculation payment account. The account transfer request signal includes the seller's pre-calculated use amount determined in step 304 and the seller's pre-calculated payment account number. The pre-calculation execution module 1106 of the operation server 11 generates an account transfer request signal requesting a transfer of the determined pre-calculation usage amount to the seller's pre-calculation payment account. The pre-calculation execution module 1106 inputs the generated account transfer request signal to the communication module 1102.

In step 306, the operation server 11 transmits an account transfer request signal to a bank server (not shown). The communication module 1102 of the operating server 11 transmits an account transfer request signal to an external bank server. The account transfer request signal may further include an account number of a pre-calculation service provider (eg, an operator of the operation server 11). The operation server 11 transmits an account transfer request signal including the pre-calculation amount and the seller's pre-calculation payment account number to the bank server, thereby transferring the pre-calculation use amount from the pre-calculation service provider's account to the seller's account. request to be transferred to Based on the account transfer request signal, the bank server transfers the pre-calculation usage amount from the pre-calculation service provider's account to the seller's account. The operation server 11 may provide a pre-calculation service to the seller by transmitting an account transfer request signal to the bank server.

In step 307, the operation server 11 receives a pre-calculation payment completion signal from the bank server in response to the account transfer request signal. After the transfer of the pre-calculated amount to the seller's pre-calculated payment account is completed, the bank server transmits a pre-calculated payment completion signal to the operation server 11 indicating that the transfer of the pre-calculated amount has been completed. The communication module 1102 of the operation server 11 receives a pre-calculation payment completion signal from the bank server. The communication module 1102 inputs the received pre-calculation payment completion signal to the seller monitoring module 1107.

In step 308, when receiving the pre-production and payment completion signal, the operation server 11 scrapes subsequent sales data related to the seller after receiving the pre-production and payment completion signal, and sells the seller based on the scraped subsequent sales data and pre-sale data. determine whether or not abnormal symptoms of The seller monitoring module 1107 of the operation server 11 scrapes follow-up sales data related to the seller after receiving the pre-production payment completion signal from the bank server, analyzes the scraped follow-up sales data and pre-sale data, and informs the seller Determine whether an anomaly has occurred.

The seller monitoring module 1107 of the operation server 11 scrapes follow-up sales data related to products sold by the seller through the online shopping mall after the seller pays the pre-calculation fee. Subsequent sales data includes monthly order quantity, monthly sales, monthly return number, and monthly product return rate after the date of payment of the pre-calculation fee. Here, the product return rate is the percentage of products returned to the seller out of the total products ordered by the buyer.

The seller monitoring module 1107 of the operation server 11 determines whether a remarketing has occurred to the seller based on the scrapped seller's pre-sale data in step 3021 and the scrapped seller's follow-up sales data in step 308.

The operation server 11 monitors whether a change occurs in the situation of the seller who applied for the pre-calculation service after the payment of the pre-calculation service amount. The operation server 11 continuously monitors whether a situation occurs in which the pre-calculated usage money paid to the seller in advance cannot be collected by checking changes in the seller's sales performance and product sales status. The operation server 11 continuously monitors the sales status of the seller's products, so that when the seller using the pre-calculation service reaches a situation where he or she cannot repay the amount of pre-calculation, the use of the pre-calculation service is stopped and additional pre-calculation services are stopped. You can block payment of the usage amount.

In this regard, FIG. 5 is a detailed flowchart illustrating the step of scraping the follow-up sales data shown in FIG. 3 and determining whether or not an abnormal symptom has occurred in the seller based on the scraped follow-up sales data and pre-sale data.

Referring to FIG. 5 , in step 3081, the seller monitoring module 1107 scrapes follow-up sales data of the seller from each online shopping mall server 13 after receiving the pre-production payment completion signal. The seller monitoring module 1107 scrapes follow-up sales data associated with the seller ID from the online shopping mall server 13 of all online shopping malls included in the online shopping mall list included in the seller's shopping mall account data.

For example, the operation server 11 scrapes the sales details of products sold by the seller in each online shopping mall server 13 after receiving the pre-production payment completion signal from the bank server. After the seller monitoring module 1107 of the operation server 11 receives the pre-production payment completion signal, the monthly order quantity, monthly sales, monthly returns, and monthly products sold through the seller ID associated with the seller from each online shopping mall server. Scrape follow-up sales data, including return rates.

In step 3082, the seller monitoring module 1107 calculates the current status score of the seller based on the scrapped post-sale data and pre-sale data.

The seller monitoring module 1107 according to an embodiment of the present invention calculates the seller's current status score based on the pre-sale data and the follow-up sale data. The seller monitoring module 1107 further includes a pre-trained artificial neural network to calculate the current state score of the seller based on the pre-sale data and the post-sale data.

Here, the artificial neural network is pre-trained by a training data set including a plurality of pre-sale data for learning, a plurality of follow-up sales data for training, and a current state score for learning of each pre-sale data for learning and each follow-up sales data for learning. am. The artificial neural network calculates and outputs the seller's current state score using the input pre-sale data and follow-up sales data. Here, the artificial neural network outputs the current state score of the seller as a predetermined natural number between 0 and 100. The closer the seller's current condition score output by the artificial neural network is to 0, the higher the likelihood that an anomaly has occurred, and the closer it is to 100, the higher the likelihood that no abnormality has occurred.

The seller monitoring module 1107 according to another embodiment of the present invention calculates the total number of pre-orders, the average monthly sales amount of all pre-orders, the number of orders per month of all follow-up orders, the total sales volume per month of follow-up, the number of returns per month after all follow-ups, and the total number of monthly products after follow-up. Calculate the seller's presence score based on the return rate.

More specifically, the seller monitoring module 1107 is based on the seller's monthly order quantity, monthly sales, monthly number of returns, and monthly product return rate in each online shopping mall included in the follow-up sales data scraped from each online shopping mall server 13. to calculate the seller's total follow-up monthly order quantity, total follow-up monthly sales, total follow-up monthly number of returns, and total follow-up monthly product return rate.

Here, the total subsequent monthly order quantity of the seller is the monthly order quantity obtained by adding the monthly order quantity ordered through the seller ID in each online shopping mall after receiving the pre-payment completion signal. In other words, the total follow-up monthly order quantity means the seller's total monthly order quantity, not the monthly order quantity of each online shopping mall after pre-calculation.

The total follow-up monthly sales is the total monthly sales, which is the sum of the monthly sales ordered through the seller ID in each online shopping mall after receiving the pre-settlement payment completion signal. In other words, the total follow-up monthly sales refer to the seller's total monthly sales, not the monthly sales of each online shopping mall after pre-calculation.

The total number of subsequent monthly returns is the total number of returns per month, which is the sum of the total number of returns per month of each online shopping mall after the pre-payment completion signal is received. In other words, the total number of subsequent monthly returns refers to the total number of monthly returns of the seller, not the number of monthly returns of each online shopping mall after pre-order.

The total follow-up monthly product return rate is the ratio of the total number of returns per month to the total monthly order quantity after receiving the pre-production payment completion signal. In other words, the total monthly product return rate means the seller's total monthly product return rate, not the monthly product return rate of each online shopping mall after pre-order.

The seller monitoring module 1107 calculates the seller's current status score according to the total number of subsequent monthly orders, the total number of subsequent monthly sales, the total number of subsequent monthly returns, and the total subsequent monthly product return rate of the seller.

For example, the seller monitoring module 1107 compares the total pre-order quantity with the total subsequent monthly order quantity, compares the total prior monthly average sales with the total subsequent monthly sales, compares the total subsequent monthly returns with the monthly number, , and the risk variable is calculated by comparing the change in the return rate for all subsequent months.

As a result of comparing the total pre-order quantity with the monthly order quantity, the seller monitoring module 1107 determines the sign of the risk variable as a positive number when the monthly order quantity increases, and changes the sign of the risk variable when the monthly order quantity decreases. set to negative The seller monitoring module 1107 determines the sign of the risk variable as a positive number as a result of comparison between the total prior monthly average sales and all subsequent monthly sales, and if the subsequent monthly sales are greater than or equal to the prior monthly average sales, and determines that the subsequent monthly sales are the prior monthly average sales. If it is less than, the sign of the risk variable is determined as a negative number. The seller monitoring module 1107 determines the sign of the risk variable as a negative number when the number of returns increases as a result of comparing the number of returns for all subsequent months, and determines the sign of the risk variable as a positive number when the number of returns decreases.

In addition, the seller monitoring module 1107 calculates the size of the risk variable based on the amount of change in order quantity, the amount of change in monthly sales, and the amount of change in the number of returns per month. The size of the risk variable is proportional to the change in order quantity, the change in monthly sales, and the change in the number of returns per month.

The seller monitoring module 1107 calculates a risk variable based on the sign of the determined risk variable and the size of the calculated risk variable.

The seller monitoring module 1107 calculates the seller's current condition score by summing the risk variables calculated in the above-described manner with the overall seller ratings.

In step 3083, the seller monitoring module 1107 determines whether an abnormal symptom occurs in the seller based on the calculated score of the current condition of the seller. More specifically, the seller monitoring module 1107 compares the current state score of the seller calculated in step 3082 with a preset reference state score, and if the current state score of the seller is greater than or equal to the reference state score as a result of the comparison, an abnormal symptom is displayed to the seller. If it is determined that it has not occurred, and as a result of the comparison, the seller's current condition score is less than the reference condition score, it is determined that the abnormality has occurred in the seller.

Here, the reference condition score is a score that is a criterion for determining whether an abnormal symptom has occurred in the seller, and may be set in advance by the user of the operation server 11 . The reference condition score varies depending on the category of product sold by the seller. The user may differently set the reference condition score according to the product category. For example, the user of the operation server 11 may set the standard condition score high when the product category is a luxury product, and set the reference condition score low when the product is a writing instrument or exercise book, which is a small amount.

In step 309, the operation server 11 generates a risk notification signal indicating that the payment of the subsequent pre-calculated usage amount to the seller is dangerous when it is determined that an abnormal symptom has occurred. When the risk signal generation module 1108 of the operation server 11 determines that an abnormality has occurred in the seller in step 308, it generates a risk notification signal indicating that it is dangerous to pay the additional pre-calculated usage amount to the seller. . The risk notification signal indicates that an abnormality has occurred to the seller after the pre-calculation usage amount has been paid. The risk notification signal may inform the seller that an abnormality has occurred in the economic situation and business situation after the user of the operation server 11 has paid the pre-calculation usage amount.

In step 310, the operation server 11 outputs the generated danger notification signal. The operation server 11 outputs a risk notification signal through the output module 1109, so that the user of the operation server 11 can be notified that an abnormal symptom has occurred with respect to the seller. The user of the operation server 11 may classify the seller as a high-risk group and restrict the use of the additional pre-calculation service in order to restrict the use of additional pre-calculation services to the seller for whom signs of abnormality have been detected.

Meanwhile, the above-described embodiments of the present invention can be written as a program that can be executed on a computer, and can be implemented in a general-purpose digital computer that operates the program using a computer-readable recording medium. In addition, the structure of data used in the above-described embodiments of the present invention can be recorded on a computer-readable recording medium through various means. The computer-readable recording medium includes a storage medium such as a magnetic storage medium (eg, a ROM floppy disk, a hard disk, etc.) and an optical reading medium (eg, CD-ROM, DVD, etc.). A program for performing a method for operating a pre-calculation service according to embodiments of the present invention is recorded on a computer-readable recording medium.

According to the above-described embodiments of the present invention, the method of operating the pre-calculation service is to quickly pay the seller who is a small business by paying the pre-calculation usage amount to the seller before the seller who sells through the online shopping mall receives the settlement of the sales price from the online shopping mall. Operating funds can be provided.

In addition, the present invention collects various types of data related to the seller, such as the seller's total sales, order quantity, and rating, from each online shopping mall server of the seller in the operation server, and determines eligibility for using the pre-calculation service based on the collected sales data. The risk of not recovering the pre-calculated amount can be minimized by determining and paying the pre-calculated amount only to sellers who are eligible for use.

In addition, the present invention scrapes data related to the seller's product sales after paying the pre-calculated usage amount to the seller, and analyzes the scraped data to monitor whether abnormal symptoms have occurred economically or in business to the seller. . The operation server may monitor whether an abnormal symptom occurs in the seller after the payment of the pre-calculated amount, and if an abnormal symptom occurs, the seller may notify the user of the operation server that the abnormal symptom has occurred. It is possible to prevent the risk of not recovering the amount paid by pre-calculation by continuously monitoring that anomalies have occurred to the seller while using the pre-calculation service, and blocking the use of the pre-calculation service later for the seller with the abnormal symptom. .

So far, the present invention has been looked at with respect to preferred embodiments. Those of ordinary skill in the art to which the present invention pertains will understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from an illustrative rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the equivalent scope will be construed as being included in the present invention.

1: Pre-production service operation system
11: operation server
1101: processor 1102: communication module
1103: Storage 1104: Utilization eligibility determination module
1105: Pre-calculated amount calculation module 1106: Pre-calculated execution module
1107: seller monitoring module 1108: danger signal generation module
1109: output module
12: Seller terminal
13: Online shopping mall server

Claims (10)

In the method of operating the selection service,
Receiving a pre-payment request signal including business data related to the seller, shopping mall account data, and pre-payment application data from a seller terminal of the seller ― the business data includes the number of products the seller is selling through the online shopping mall, product categories and a business registration number, the shopping mall account data includes a list of online shopping malls in which the seller is selling, and the pre-calculation application data includes a pre-calculation application amount and a pre-calculation payment account;
scraping pre-sale data associated with the seller based on the pre-sale request signal, and determining eligibility for using the pre-sale service of the seller based on the pre-sale data;
calculating a pre-calculation limit amount of the seller based on the pre-calculation application signal and the pre-sale data, when it is determined that the seller is eligible for the pre-calculation service;
Calculating a pre-calculation usage amount of the seller based on the pre-calculation usage limit amount and the pre-calculation application amount;
generating an account transfer request signal for requesting a transfer of the pre-calculated amount to a pre-selected balance payment account of the seller; and
The pre-calculation service operating method comprising the step of transmitting the account transfer request signal to a bank server. According to claim 1,
The pre-sale data includes order quantity for the previous 6 months from the date of application for pre-calculation service, sales for the previous 6 months, monthly sales for the previous 6 months, and seller ratings;
The step of determining eligibility for the seller's pre-calculation service is
scraping pre-sale data associated with the seller from each online shopping mall server included in the online shopping mall list, based on the shopping mall account data;
and determining eligibility for using the pre-sale service of the seller based on the scraped pre-sale data. According to claim 2,
Based on the scraped pre-sale data, the step of determining the eligibility of the seller to use the pre-sale service
Based on the scraped pre-sale data, calculate the total pre-order quantity, total pre-sales, total pre-monthly average sales, and total seller ratings of the seller, and
The method of operating a pre-calculation service, characterized in that determining eligibility for using the pre-calculation service of the seller based on the calculated total pre-order quantity, total pre-order sales, total pre-monthly average sales, and total seller ratings. According to claim 3,
receiving a pre-calculation payment completion signal from the bank server in response to the account transfer request signal; and
Further comprising the step of scraping subsequent sales data related to the seller when the pre-production payment completion signal is received, and determining whether an abnormal symptom occurs in the seller based on the scraped subsequent sales data and the pre-sale data. A method for operating a selection service, characterized in that for doing. According to claim 4,
The step of determining whether the seller has abnormal symptoms
After receiving the pre-calculation payment completion signal, scraping subsequent sales data of the seller from all online shopping mall servers included in the online shopping mall list - the follow-up sales data are monthly orders after the pre-calculation usage amount is paid Including quantity, monthly sales, and number of returns per month ―;
calculating a current status score of the seller based on the subsequent sales data and the pre-sale data; and
Based on the score of the current state of the seller, determining whether or not an abnormal symptom has occurred in the seller. According to claim 5,
The step of calculating the current state score of the seller is
Calculate a current state score of the seller by an artificial neural network pre-trained by a training data set to calculate a current state score of the seller based on the follow-up sales data and the pre-sale data;
The training data set includes a plurality of pre-sale data for learning, a plurality of follow-up sales data for learning, and a current state score for learning. According to claim 5,
The step of calculating the current state score of the seller is
calculating total follow-up monthly order quantities, total follow-up monthly sales, total follow-up monthly returns, and total follow-up monthly product return rates of the seller based on the follow-up sales data;
Calculate a risk variable based on the total pre-order quantity, total pre-monthly average sales, total follow-up monthly order quantity, total follow-up monthly sales, total follow-up monthly returns, and total follow-up monthly product return rates; and
The method of operating a pre-calculation service, characterized in that for calculating the current state score of the seller based on the risk variable and the overall seller rating. According to claim 5,
Based on the seller's current state score, determining whether the seller has an abnormal symptom
Comparing the current state score with a preset reference state score, and determining that an abnormality has occurred in the seller when the current state score is less than the reference state score as a result of the comparison;
The pre-calculation service operating method, characterized in that the reference state score is variable according to the product category. In the operation server 11 that executes the pre-calculation service operation method,
A communication module 1102 for receiving a prepayment request signal including business data related to the seller, shopping mall account data, and prepayment request data from the seller terminal of the seller ― the business data is a product that the seller is selling through an online shopping mall number, product category, and business registration number, the shopping mall account data includes a list of online shopping malls sold by the seller, and the prepayment request data includes a prepayment request amount and a prepayment payment account;
a use eligibility determination module 1104 for scraping pre-sales data associated with the seller based on the pre-sales request signal and determining eligibility for the pre-sales service of the seller based on the pre-sales data;
When it is determined that the seller is eligible to use the pre-calculation service, the pre-calculation usage limit amount of the seller is calculated based on the pre-calculation application signal and the pre-sale data, and the pre-calculation usage limit amount and the pre-calculation application amount Based on, the pre-calculated amount calculation module 1105 for calculating the pre-calculated usage amount of the seller; and
A pre-calculation execution module 1106 for generating an account transfer request signal for requesting a transfer of the pre-calculation usage amount to the selected balance payment account of the seller,
The communication module 1102 is an operating server, characterized in that for transmitting the account transfer request signal to the bank server. A computer-readable recording medium on which a program for performing the method according to any one of claims 1 to 8 is recorded.