WO2023153681A1

WO2023153681A1 - Credit evaluation method based on end-to-end data generated during purchase, sale, inventory, logistics, distribution, and settlement of e-commerce solution, and apparatus for performing same method

Info

Publication number: WO2023153681A1
Application number: PCT/KR2023/001174
Authority: WO
Inventors: 강정석
Original assignee: 주식회사 에이젠글로벌
Priority date: 2022-02-14
Filing date: 2023-01-26
Publication date: 2023-08-17
Also published as: KR102464995B1; KR20230122524A

Abstract

The present invention relates to a credit evaluation method based on end-to-end data generated during purchase, sale, inventory, logistics, distribution, and settlement of an e-commerce solution, and an apparatus for performing the method. The credit evaluation method based on end-to-end data generated during purchase, sale,　 inventory,　 logistics,　 distribution, and settlement of an e-commerce solution may comprise: a step in which a credit evaluation raw data collection unit collects credit evaluation raw data for a seller's credit evaluation; a step in which a credit evaluation raw data pre-processing unit pre-processes the collected credit evaluation raw data; a step in which a credit evaluation raw data learning unit generates an artificial intelligence engine for the seller's credit evaluation on the basis of the pre-processed credit evaluation raw data; and a step in which a credit evaluation unit determines credit evaluation data of the seller on the basis of the artificial intelligence engine.

Description

Credit evaluation method based on end-to-end data generated during purchase, sale, inventory, logistics, distribution, and settlement of e-commerce solutions, and a device that performs the method

The present invention relates to a credit evaluation method based on end-to-end data generated in the course of purchase, sale, inventory, logistics, distribution, and settlement of an e-commerce solution, and an apparatus for performing the method. More specifically, it relates to a method and apparatus for performing credit evaluation on a seller based on data generated in an e-commerce solution.

As we enter the intelligent information society triggered by the 4th industrial revolution, the possibility of unlimited utilization of data is bringing about changes in the data industry. With the arrival of the data era, the level of the future data industry will determine the difference in competitiveness between countries.

In particular, the establishment of big data infrastructure in the financial market is not only very urgent, but soon it has become an important asset enough to determine the direction of the country's data industry. The financial big data infrastructure consists of big data open systems, data exchanges, and data specialized institutions.

Research on new financial products based on these big data-based user financial data is needed. It is possible to analyze various risks through artificial intelligence-based learning of user financial products, and based on risk analysis, new financial services that have not been available before can be provided to users.

Therefore, it is necessary to study specific methods for utilizing user's financial data and providing various financial services based on the user's financial data.

The object of the present invention is to solve all of the above problems.

In addition, an object of the present invention is to provide customized financial services to sellers through credit evaluation based on end-to-end data generated during purchase, sale, inventory, logistics, distribution, and settlement of e-commerce solutions.

In addition, an object of the present invention is to perform filtering to eliminate redundancy with respect to current data and predicted data generated on ECS and grouping in consideration of reliability for each data to be used as credit evaluation data.

Representative configurations of the present invention for achieving the above object are as follows.

According to an embodiment of the present invention, the credit evaluation method based on end-to-end data generated in the process of purchasing, selling, inventory, logistics, distribution, and settlement of an e-commerce solution is provided by the credit evaluation basic data collection unit for the seller's credit evaluation. Collecting basic credit rating data; Step of pre-processing the collected basic credit rating data by a credit rating basic data pre-processing unit; calculating the credit rating of the seller based on the pre-processed basic credit rating data by a credit rating basic data learning unit; generating an artificial intelligence engine for the above and determining credit evaluation data of the seller based on the artificial intelligence engine by a credit rating unit, but in an e-commerce solution, the credit evaluation basic data generated over the entire process It can be processed by classifying it into three basic credit rating data (production), credit rating basic data (distribution), and credit rating basic data (sales).

On the other hand, the first type data is data about a product that is only produced or purchased by the seller, before being distributed and sold, and is data consisting only of the credit rating basic data (production), and the second type data is produced by the seller. and data on a product that has been distributed but before being sold, which is data consisting of only the credit rating basic data (production) and the credit rating basic data (distribution), and the third type data is data on products produced, distributed, and sold by the seller. As the data for credit rating, the data is composed of the credit rating base data (production), the credit rating base data (distribution), and the credit rating base data (sale), the credit rating data being the first type data and the second type data. and the credit evaluation basic data based on the third type of data.

Also, the credit evaluation data may be determined using the credit evaluation basic data based on the first type data, the second type data, and the third type data.

According to another embodiment of the present invention, a credit evaluation device based on end-to-end data generated during purchase, sale, inventory, logistics, distribution, and settlement of an e-commerce solution collects credit evaluation basic data for seller credit evaluation. A credit evaluation basic data collection unit implemented to preprocess the collected credit evaluation basic data, a credit evaluation basic data preprocessor implemented to preprocess the collected credit evaluation basic data, and an artificial intelligence engine for the credit evaluation of the seller based on the preprocessed credit evaluation basic data. It may include a credit evaluation basic data learning unit implemented to generate a credit evaluation unit configured to determine credit evaluation data of the seller based on the artificial intelligence engine, and an evaluation unit configured to determine the seller's credit evaluation data. Credit rating basic data can be processed by classifying it into three basic credit rating data (production), credit rating basic data (distribution), and credit rating basic data (sales).

According to the present invention, customized financial services can be provided to sellers through credit evaluation based on end-to-end data generated during purchase, sale, inventory, logistics, distribution, and settlement of e-commerce solutions.

In addition, according to the present invention, a customized financial service can be provided to a seller through filtering to eliminate redundancy between current data and predicted data generated on the ECS and grouping in consideration of reliability for each data.

1 is a conceptual diagram illustrating a credit evaluation apparatus according to an embodiment of the present invention.

2 is a conceptual diagram illustrating a sales management platform and a method of collecting basic credit evaluation data through the sales management platform according to an embodiment of the present invention.

3 is a conceptual diagram illustrating the operation of a credit basic data pre-processing unit according to an embodiment of the present invention.

4 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.

5 is a conceptual diagram illustrating a first preprocessing (a seller) according to an embodiment of the present invention.

6 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.

7 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.

8 is a conceptual diagram illustrating a method of performing a first preprocessing according to an embodiment of the present invention.

9 is a conceptual diagram illustrating the operation of a credit evaluation unit according to an embodiment of the present invention.

10 is a conceptual diagram illustrating a scorecard determination method according to an embodiment of the present invention.

11 is a conceptual diagram illustrating a scorecard determination method according to an embodiment of the present invention.

12 is a conceptual diagram illustrating a method for determining a scorecard according to an embodiment of the present invention.

13 is a conceptual diagram illustrating a credit evaluation device that performs scorecard determination according to an embodiment of the present invention.

14 is a conceptual diagram illustrating a seller credit evaluation method based on credit evaluation basic data generated in the OMS according to an embodiment of the present invention.

15 is a conceptual diagram illustrating a method of setting a scale of basic credit evaluation data according to an embodiment of the present invention.

16 is a conceptual diagram illustrating a seller credit evaluation method based on product sales data generated by OMS according to an embodiment of the present invention.

17 is a conceptual diagram illustrating a seller credit evaluation method based on credit evaluation basic data generated by WMS according to an embodiment of the present invention.

18 discloses a method of generating WMS-based credit evaluation basic data according to an embodiment of the present invention.

19 illustrates a method of generating WMS-based credit evaluation basic data according to an embodiment of the present invention.

20 is a conceptual diagram illustrating a seller credit evaluation method based on product sales cycle data generated in the WMS according to an embodiment of the present invention.

21 is a conceptual diagram illustrating a seller credit evaluation method based on credit evaluation basic data generated in the ECS according to an embodiment of the present invention.

22 is a conceptual diagram illustrating a seller credit evaluation method based on credit evaluation basic data generated in the ECS according to an embodiment of the present invention.

23 is a conceptual diagram illustrating a seller credit evaluation method based on credit evaluation basic data generated in the ECS according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description of the present invention which follows refers to the accompanying drawings which illustrate, by way of illustration, specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable any person skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention are different from each other but are not necessarily mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented from one embodiment to another without departing from the spirit and scope of the present invention. It should also be understood that the location or arrangement of individual components within each embodiment may be changed without departing from the spirit and scope of the present invention. Therefore, the detailed description to be described later is not performed in a limiting sense, and the scope of the present invention should be taken as encompassing the scope claimed by the claims and all scopes equivalent thereto. Like reference numbers in the drawings indicate the same or similar elements throughout the various aspects.

Hereinafter, various preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings in order to enable those skilled in the art to easily practice the present invention.

In FIG. 1, a credit evaluation device for performing a credit evaluation on a seller based on data collected through an e-commerce sales and distribution management system platform is disclosed.

Referring to FIG. 1 , the credit evaluation apparatus includes a credit evaluation basic data collection unit 110, a credit evaluation basic data pre-processing unit 120, a credit evaluation basic data learning unit 130, a credit evaluation unit 140, and a financial service unit. 150 and a processor 160.

The credit evaluation basic data collection unit 110 may be implemented to collect credit evaluation basic data for a seller's credit evaluation. Sellers can sell products through various platforms for selling and distributing products through e-commerce. An e-commerce sales and distribution management system platform for managing various data related to seller's product sales, product distribution, and product payment may be expressed as a sales management platform 100 . The credit evaluation basic data collection unit 110 may be implemented to collect credit evaluation basic data for seller's credit evaluation through various sales management platforms. A specific sales management platform 100 will be described later. The term product used in the present invention may be used as a meaning including a service provided by a seller as one product.

The credit evaluation basic data preprocessing unit 120 may be implemented to preprocess the collected credit evaluation basic data. The basic credit evaluation data may be preprocessed and used for learning of an artificial intelligence engine for credit evaluation, or may be used for credit evaluation of a seller.

The credit evaluation basic data for learning of the artificial intelligence engine for credit evaluation may be transmitted to the credit evaluation basic data learning unit 130 through the first pre-processing. Credit evaluation basic data for learning of the artificial intelligence engine for credit evaluation of the seller may be transmitted to the credit evaluation unit 140 through the second pre-processing.

The credit evaluation basic data learning unit 130 may be implemented for artificial intelligence learning for seller's credit evaluation. The credit evaluation basic data learning unit 130 includes a plurality of artificial intelligence engines for credit evaluation of the seller, and each of the plurality of artificial intelligence engines may be implemented to determine a lower credit evaluation factor for the seller's credit evaluation. .

The credit evaluation unit 140 may be implemented to evaluate the seller's credit and determine the seller's credit evaluation data. The credit evaluation unit 140 may determine the seller's credit evaluation data based on a plurality of sub-level credit evaluation factors determined by each of a plurality of artificial intelligence engines of the credit evaluation basic data learning unit. In addition, the credit evaluation unit 140 may determine the seller's credit evaluation data based on a separate algorithm rather than an artificial intelligence engine.

The financial service unit 150 may be implemented to provide financial services to the seller based on the seller's credit evaluation data.

The processor 160 operates the credit evaluation basic data collection unit 110, the credit evaluation basic data pre-processing unit 120, the credit evaluation basic data learning unit 130, the credit evaluation unit 140, and the financial service unit 150. can be implemented to control.

2 discloses a sales management platform and credit evaluation basic data collected from the sales management platform.

Referring to FIG. 2, the sales management platform includes an order management system (OMS) 210, an enterprise resource planning (ERP) 220, a warehouse management system (WMS) 230, and an E-commerce solution (ECS) 240. etc. may be included. OMS (210), ERP (220), WMS (230), ECS (240), as an example, may be a sales management platform for various other entities related to product sales of sellers.

OMS (210) is a platform for product order management of the seller.

The OMS 210 is a computer system through which a seller who sells a product through a plurality of sales channels can integrally process a series of sales processes. The seller may check the status of products ordered through the plurality of sales channels through the OMS 210 and may collectively process payment confirmation, delivery, order cancellation, return, and the like.

In detail, on the OMS 210, functions such as batch product registration modification, order collection, invoice registration and transmission, inventory management, and the like may be provided. In addition, the OMS 210 may provide functions for managing payment information, sales information, settlement information for sales, return information, refund information due to return, and inventory information on a plurality of sales channels.

The ERP 220 may be a sales management platform for managing information such as product production (purchase), logistics, finance, accounting, sales, purchase, and inventory of a seller as enterprise resource management.

WMS 230 is a warehouse management system and a sales management platform for supporting and optimizing warehouse or distribution center management. The WMS 230 may integrate and manage logistics processes such as warehousing, stacking, stocking, packing, and shipping of goods of the seller as a whole.

The ECS 240 may be a sales management platform for creating and managing an online mall for sale by a seller. The ECS 240 may be implemented to create an online shopping mall, manage data generated on the online shopping mall, and perform marketing for product sales.

Credit evaluation basic data collection unit OMS (210), ERP (220), WMS (230), ECS (240), such as sales management platform in association with the credit evaluation basic data may be collected.

For example, the credit evaluation basic data collection unit converts product registration information, inventory information, order information, return information, payment information, sales information, settlement information, refund information, etc. generated in the OMS 210 into the seller's credit evaluation basic data. can be collected

The credit evaluation basic data collection unit may collect product storage information, product stock information, product release information, and product delivery information generated by the WMS 230 as the seller's credit evaluation basic data.

The credit evaluation basic data collection unit may collect the product marketing information generated by the ECS 240 as the seller's credit evaluation basic data.

3 discloses a method of preprocessing credit evaluation basic data in a credit basic data preprocessing unit.

Referring to FIG. 3 , the credit evaluation basic data 300 may be transmitted to the credit evaluation basic data learning unit 360 as the first preprocessing credit evaluation basic data 320 through the first preprocessing 310 . In addition, the credit evaluation basic data 300 may be transmitted to the credit evaluation unit 370 as the second preprocessing credit evaluation basic data 355 through the second preprocessing 350 .

The first pre-processing 310 may be pre-processing for learning in an artificial intelligence engine.

The first pre-processing 310 may be performed in consideration of characteristics of the sales management platform that generated the credit evaluation basic data 300 . In the present invention, since the financial service is provided considering seller characteristics and supply chain characteristics, the first pre-processing 310 may be performed to learn the artificial intelligence engine considering the seller characteristics and supply chain characteristics.

Among the first preprocessing 310 , preprocessing considering supply chain characteristics may be performed in consideration of a supply chain step corresponding to the credit evaluation basic data 300 . Among the first preprocessing 310 , preprocessing considering supply chain characteristics may be expressed as a first preprocessing (supply chain) 313 .

For example, when a supply chain is divided into a production (or purchase) stage, a distribution stage, and a sales stage, the credit evaluation basic data 300 is primarily credit evaluation basic data (production phase) based on the stage at which the data was acquired. ), credit evaluation basic data (distribution phase), and credit evaluation basic data (sale phase), and may be generated as first pre-processed credit evaluation basic data 320 .

Also, among the first preprocessing 310 , the preprocessing considering seller characteristics may be performed through seller data classification and seller data augmentation based on seller characteristics. Among the first pre-processing, the pre-processing considering seller characteristics may be expressed as a first pre-processing (seller) 316 .

The second pre-processing 350 may be performed for credit evaluation of the seller based on an artificial intelligence engine included in the credit evaluation unit. The second pre-processed credit evaluation base data 355 may be input to an artificial intelligence engine and used to determine lower credit evaluation factors. Accordingly, the second pre-processing 350 may be performed in consideration of the input data format of the artificial intelligence engine. Prediction on different credit rating base data is performed for each artificial intelligence engine, and each artificial intelligence engine may have a different data format. A second pre-processing 350 may be performed in consideration of at least one artificial intelligence engine that may be used for credit evaluation of the seller.

In FIG. 4 , a first pre-processing (seller) and a first pre-processing (supply chain) applied to credit evaluation basic data are disclosed.

Referring to FIG. 4 , credit evaluation basic data classified for each seller through the first preprocessing (seller) 400 is preprocessed for each supply chain step through the first preprocessing (supply chain) 450, and the first preprocessing credit evaluation basis data 490 .

The first pre-processing (supply chain) 450 may generate the first pre-processing credit evaluation basic data 490 in consideration of the sales management platform, which is the subject of data transmission, and the data format transmitted from the sales management platform.

Through the first pre-processing (supply chain) 450 that considers the information included in the supply chain stage managed by the sales management platform and the credit rating basic data generated in the sales management platform, the credit rating basic data is converted into credit rating basic data (produced). ) 460, credit evaluation basic data (distribution) 470, and credit evaluation basic data (sales) 480, and may be generated as first pre-processed credit evaluation basic data.

In addition, the first pre-processing (supply chain) 450 may generate first pre-processing credit evaluation basic data 490 by performing redundant data processing on the credit evaluation basic data transmitted through the sales management platform. When credit rating base data for the same product is duplicated in a plurality of sales management platforms, duplicate data processing may be performed. For example, when a seller purchases a specific product for sale, the product may be registered on the OMS and the product may be placed on the WMS. That is, an act of purchasing a specific product by a seller is performed only once, but data on product registration and product placement resulting from such a purchase action are generated for each sales management platform, which may result in duplication of basic credit evaluation data.

The first pre-processing (supply chain) 450 determines the redundancy of the transmitted credit rating basic data in consideration of the data generation time of the credit rating basic data, the information included in the credit rating basic data, and the later transmitted credit evaluation basic data information. Thus, first pre-processing credit evaluation basic data 490 may be generated. When duplication of credit evaluation basic data occurs, only data of one sales management platform is used through the first preprocessing (supply chain) 450, or duplicated credit evaluation basic data is filtered out and duplicate credit Only other credit evaluation basic data including information included in the evaluation basic data may be used.

Also, the first pre-processing (supply chain) 450 may be pre-processing of credit evaluation basic data considering time.

The seller's credit rating and the data underlying the seller's credit rating may change over time. Therefore, setting a time scale for credit evaluation basic data for learning may greatly affect the performance of the artificial intelligence engine. Accordingly, in the present invention, after setting a time scale for the obtained basic credit evaluation data, the first preprocessed basic credit data 490 may be generated by pre-processing the basic credit evaluation data considering the time scale. A time scale for preprocessing may be set for each credit evaluation basic data.

In FIG. 5, a method for augmenting processing of credit evaluation base data for learning of an artificial intelligence engine through a first preprocessing (seller) is disclosed. In particular, a method in which basic credit evaluation data is divided into sub-level credit evaluation basic data and used as learning data among data augmentation methods is disclosed.

Referring to FIG. 5 , a method of performing more accurate artificial intelligence engine learning by augmenting specific credit evaluation base data 500 is disclosed. For example, the credit evaluation basic data 500 may include seasonality, transaction size, delivery cycle, sales trend, return rate, sales product, inventory size, operational information, and the like. Among the credit evaluation basic data 500 , specific credit evaluation basic data may be augmented and generated as a plurality of lower credit evaluation basic data 540 .

When the return rate data is increased, data such as the size of the return, the discard rate, the average return rate, the stability of change in the return rate, and the number of times the return rate exceeds MAX may be generated as the lower credit evaluation basic data 540 .

In an embodiment of the present invention, when data augmentation is required to make credit evaluation more accurate, learning may be performed by augmenting the credit evaluation basic data 500 with lower credit evaluation basic data 540 through a first preprocessing. Data augmentation of the first pre-processing (seller) may be expressed in terms of a lower data augmentation 520 .

In FIG. 6, a method of augmenting processing of credit evaluation basic data for learning of an artificial intelligence engine on a first preprocessing (seller) is disclosed. In particular, among data augmentation methods, a method of augmenting data by analyzing data based on credit evaluation on a time scale and a method of augmenting data through a statistical method are disclosed.

6(a) is a method of augmenting data by analyzing the credit evaluation base data 600 on a time scale among data augmentation methods. Among data augmentation methods, a method of augmenting data by analyzing credit evaluation base data on a time scale may be expressed as a time scale data augmentation 610 .

For example, when the credit evaluation basic data 600 is a return rate, data on the number of sellers with a monthly return rate of 5% or more for 36 months may be augmented and generated based on the monthly return rate of 5% or more. As another example, data on the number of sellers with an average return rate for 36 months based on the average monthly return rate may be augmented and generated.

(b) of FIG. 6 is a method of augmenting data by statistically analyzing the credit evaluation base data 650 among data augmentation methods. Among the data augmentation methods, a method of statistically analyzing the credit evaluation base data 650 to augment data may be expressed as a statistical data augmentation 660 . For example, when the credit evaluation basic data 650 is a return rate, data augmentation may be performed by increasing the return rate in various ways through a statistical method such as average, standard deviation, maximum, or more than a specific range of return rates for each customer.

In FIG. 7 , a method of augmenting processing of credit evaluation basic data for learning of an artificial intelligence engine on a first preprocessing (seller) is disclosed. In particular, a method of augmenting data by two-dimensionally analyzing credit evaluation basic data among data augmentation methods is disclosed.

Referring to FIG. 7 , a method of augmenting credit evaluation basic data 700 as two-dimensional data among data augmentation methods is disclosed. Among data augmentation methods, credit evaluation basic data 700 is divided into a plurality of dimensions to augment data. The method may be expressed in terms of multidimensional data augmentation 710 .

For example, when the credit evaluation basic data 700 is a return rate, the entire return rate may be divided into two-dimensional data and increased. The first dimension may be the average return rate for 36 months, and the second dimension may be the number of months in which the return rate exceeds 5% for 36 months. Through this two-dimensional analysis, a more accurate assessment of the seller's credit evaluation basic data 700 may be possible.

8 discloses a method of selectively performing a first preprocessing (seller) for learning of a credit evaluation basic data learning unit.

Referring to FIG. 8 , the first preprocessing (seller) may use lower data augmentation 810, time scale data augmentation 820, statistical data augmentation 830, and multidimensional data augmentation 840.

A first pre-processing (seller) may be selectively performed to learn the plurality of artificial intelligence engines included in the credit evaluation basic data learning unit.

For example, the sub-data augmentation 810 may be used for training of an artificial intelligence engine to generate a specialized result through specific analysis of characteristic credit evaluation data among artificial intelligence engines. For example, sub-data augmentation on the return rate may be performed for learning of an artificial intelligence engine that generates credit evaluation data with more weight on the return rate.

Time scale data augmentation 820 can be used to train an artificial intelligence engine to predict changes in credit rating data over time.

Statistical data augmentation 830 may be used for training of an artificial intelligence engine for predicting credit evaluation data according to separately preset specific criteria.

Multi-dimensional data augmentation 840 can be used to train an artificial intelligence engine to predict credit rating data based on set criteria for two dimensions.

In an embodiment of the present invention, various first preprocessing (seller) may be performed according to the properties of predicted credit evaluation data, and various artificial intelligence models may be generated.

9 discloses a method of generating credit evaluation data of a seller based on an artificial intelligence engine in a credit evaluation unit.

Referring to FIG. 9 , the credit evaluation unit may generate credit evaluation data through a seller's credit evaluation based on at least one artificial intelligence engine.

The credit evaluation unit may generate the seller's credit evaluation data based on one artificial intelligence engine, but the credit evaluation unit adaptively determines the artificial intelligence engine applicable to the seller based on the seller characteristic information 900, and the determined artificial intelligence engine. Credit evaluation data 950 may be generated based on an intelligence engine.

For example, the target artificial intelligence engine 920 for credit evaluation most suitable for the seller characteristic information 900 based on seller information such as seller's sales product, seller's product sales platform, seller's sales, seller's net profit, etc. can be determined

The credit evaluation unit may determine the reliability of the artificial intelligence engine according to the seller characteristic information based on the feedback information of each of the plurality of artificial intelligence engines. In addition, the credit evaluation unit may determine a reliability level for each seller characteristic information for each of the plurality of artificial intelligence engines. Specifically, seller characteristic information may be vectorized and expressed on a space based on each sub-seller characteristic information, seller groups may be formed based on seller characteristic information through distance information between spaces, and credit evaluation data for each seller group The reliability of each seller group of the artificial intelligence engine may be determined by comparing the result data of the financial service and the financial service result data. The reliability level may be determined in consideration of statistical characteristics of reliability for seller groups for each artificial intelligence engine.

The credit evaluation unit may determine an artificial intelligence engine having a relatively high reliability level as the target artificial intelligence engine 920 based on the seller characteristic information and generate credit evaluation data 950 for the seller.

10 is a conceptual diagram illustrating a method for determining a scorecard according to an embodiment of the present invention.

10 discloses a method for determining a scorecard for determining a seller's credit rating data.

Referring to FIG. 10 , the scorecard 1000 may be a card including a plurality of basic credit evaluation data for determining credit evaluation data of a seller. A plurality of credit evaluation base data included in the scorecard 1000 may be preprocessed and then input to the artificial intelligence engine 1020 to determine credit evaluation data.

For example, the plurality of credit evaluation basic data included in the scorecard 1000 is information such as residence status, residence period, occupation, job retention period, bank records, card use records, existing loan records, and the like. can

The scorecard 1000 includes a combination of a plurality of basic credit evaluation data, and can be generated in various types according to the combination of a plurality of basic credit evaluation data. A combination of a plurality of credit rating base data included in the scorecard 1000 may be pre-processed and input to the artificial intelligence engine 1020 for determining the seller's credit rating data.

In the embodiment of the present invention, not only can there be a plurality of scorecards 1000 composed of combinations of a plurality of various credit evaluation base data, but also the scorecard 1000 including the same credit evaluation base data includes learning and In order to generate credit evaluation data, it may be defined as a different scorecard 1000 according to a weight applied to each of a plurality of credit evaluation basic data, a scale applied to each of a plurality of credit evaluation basic data, and the like, and input to the artificial intelligence engine 1020. there is.

That is, in the present invention, a plurality of scorecards 1000 may be input to and learned from different artificial intelligence engines 1020, and thus different credit evaluation data 1040 may be generated. Among the plurality of scorecards 1000, a specific scorecard 1000 may be selectively used. In the present invention, various scorecard determination methods may be used to determine the scorecard 1000 with the highest accuracy.

Referring to FIG. 11, a plurality of primary candidate scorecards including a plurality of different credit evaluation basic data are variously combined with a plurality of credit evaluation basic data to determine a scorecard that generates highly accurate credit evaluation data. (1100) can be determined.

After the plurality of primary candidate scorecards 1100 are determined, credit evaluation base data corresponding to the plurality of primary candidate scorecards 1100 is input to each of a plurality of artificial intelligence engines, and each of the plurality of artificial intelligence engines learns. this can be done

A plurality of artificial intelligence engines that are trained based on credit evaluation basic data corresponding to the plurality of primary candidate scorecards 1100 may be expressed as a term of primary candidate artificial intelligence engines 1120 .

If the reliability of the primary candidate artificial intelligence engine 1120 is greater than or equal to the threshold reliability, the corresponding artificial intelligence engine may be determined as the secondary candidate artificial intelligence engine 1160 .

A plurality of credit evaluation basic data included in the primary candidate scorecard 1100 corresponding to the secondary candidate artificial intelligence engine 1160 having a threshold reliability or higher is weighted, scaled, or generated by adjusting the secondary candidate scorecard 1100. Scorecard 1140 may include.

Credit evaluation base data corresponding to the secondary candidate scorecard 1140 is input to the secondary candidate artificial intelligence engine 1160, and the secondary candidate artificial intelligence engine 1160 may be trained.

Among the secondary candidate artificial intelligence engines 1160 , at least one artificial intelligence engine whose reliability is equal to or higher than a critical reliability level or an artificial intelligence engine having the highest reliability may be finally determined as an artificial intelligence engine to be used in the credit evaluation unit. In addition, the secondary candidate scorecard 1140 used in the finally determined artificial intelligence engine may be determined as the final scorecard to be used.

The above scorecard and artificial intelligence engine determining operation may be performed for each seller group in consideration of seller characteristic information, an artificial intelligence engine may be determined for each seller group, and a scorecard to be used for each seller group may be determined. That is, for each seller group, the first candidate scorecard 1000, the first candidate artificial intelligence engine 1120, the second candidate scorecard 1140, and the second candidate artificial intelligence engine 1160 may be determined.

12 discloses a method of determining a secondary candidate scorecard by performing weight adjustment, scale adjustment, or generation timing adjustment.

Referring to FIG. 12 , the weight adjustment 1200 may be set in consideration of the importance of each of the basic credit evaluation data included in the scorecard. For credit evaluation basic data that has a greater impact on actual financial service results, a larger weight can be set to perform learning on the artificial intelligence engine.

Scale adjustment 1210 may be an adjustment of a range scale for classifying the credit rating base data. For example, in the case of the number of years of service at work, it can be classified into n categories, and the scale on which the basic credit evaluation data is classified and learned determines whether the credit evaluation data of the artificial intelligence engine will reflect the actual financial service results. can affect what you can do. Accordingly, optimal artificial intelligence engine learning may be performed through scaling 1210 for each of a plurality of credit evaluation base data included in the secondary candidate scorecard 1240 .

The creation time point adjustment 1220 (or scoring time point adjustment) may perform learning of the artificial intelligence engine in consideration of the generation time point (or scoring time point) of the credit evaluation base data. A plurality of credit evaluation basic data included in the scorecard may be grouped and input to the artificial intelligence engine. Accordingly, it is possible to determine which learning data is generated by setting a generation time point of the credit evaluation basic data. Therefore, more accurate learning of the artificial intelligence engine can be performed through the adjustment of the generation time point 1220 of the credit evaluation base data.

In addition, the creation time point adjustment 1220 may reduce a score error generated according to the generation time point by setting a plurality of creation time points. In the basic credit evaluation data, the observation period 1250, the scoring time 1260, and the operation period 1270 are separately classified, and the observation period 1250 and the operation period 1270 are set differently to set a plurality of scoring times 1260. can be set By setting a plurality of scoring time points 1260, it is possible to reduce a score error according to the time of creation, which may occur according to a seller's product, such as a seasonal change, and to reflect the score change according to time.

Referring to FIG. 13 , the credit evaluation device may include a scorecard determining unit.

A scorecard determination unit may be implemented to determine a scorecard. The scorecard determining unit includes a first candidate scorecard determining unit 1310, a first candidate artificial intelligence engine generating unit 1320, a second candidate scorecard determining unit 1330, a second candidate artificial intelligence engine generating unit 1340, and A scorecard determining unit 1350 may be included.

The primary candidate scorecard determining unit 1310 variously combines a plurality of credit evaluation basic data to determine a scorecard for determining highly accurate credit evaluation data, and includes a plurality of different credit evaluation basic data. A primary candidate scorecard can be determined.

The first candidate artificial intelligence engine generation unit 1320 may be implemented to generate the first candidate artificial intelligence engine. After determining the plurality of primary candidate scorecards, the credit evaluation basic data corresponding to the plurality of primary candidate scorecards is input to each of the plurality of artificial intelligence engines, learning of each of the plurality of artificial intelligence engines is performed, and the primary A candidate artificial intelligence engine may be determined.

The secondary candidate artificial intelligence engine generating unit 1340 may determine the corresponding artificial intelligence engine as a secondary candidate artificial intelligence engine when reliability among the primary candidate artificial intelligence engines is greater than or equal to a critical reliability level.

The secondary candidate scorecard generating unit 1330 performs weight adjustment, scale adjustment, or creation time adjustment on a plurality of credit evaluation base data included in the primary candidate scorecard corresponding to the secondary candidate artificial intelligence engine to generate secondary candidate scorecards. A candidate scorecard can be determined.

The scorecard determination unit 1350 may be implemented to determine a final scorecard to be used among secondary candidate scorecards.

14 discloses a method of performing credit evaluation of a seller by processing basic credit evaluation data generated on the OMS.

Referring to FIG. 14 , credit evaluation basic data generated on the OMS may include product registration management data 1410 , product order management data 1420 , and product inventory management data 1430 .

The product registration management data 1410 is registered for sale in an online shopping mall, such as online shopping mall data in which a product is registered, product registration data registered in the online shopping mall, product sales data (sale quantity, selling price, cost, etc.), and product out-of-stock data. It may contain data related to the product being sold.

The product order management data 1420 may include data related to product ordering and distribution after product registration, such as product order quantity data for each period, product delivery data, and product refund data.

The product inventory management data 1430 may include data related to product inventory, such as product inventory data, product out-of-stock data, and product safety inventory data.

As described above, the product registration management data 1410, product order management data 1420, and product inventory management data 1430, respectively, are credit rating basic data (production) 1415 and credit rating basic data (distribution) 1425 , credit evaluation basic data (sales) 1435 may be pre-processed and generated as first pre-processed credit evaluation data.

Credit evaluation basic data generated by OMS can be used for learning of artificial intelligence engine. Credit rating basic data can be processed through weight adjustment, scale adjustment, or generation timing adjustment, etc., and used for artificial intelligence engine learning.

In the present invention, a method for setting a time scale for credit rating base data is disclosed. Different time scales may be applied to each of the product registration management data 1410 , the product order management data 1420 , and the product inventory management data 1430 .

A time scale for generating credit evaluation basic data based on the product registration management data 1410 may be expressed in terms of a product registration time scale. A time scale for generating credit evaluation basic data based on the product order management data 1420 may be expressed as a product order time scale. A time scale for generating credit evaluation basic data based on the product inventory management data 1430 may be expressed as a product inventory time scale. Methods for setting each of the product registration time scale, product order time scale, and product inventory time scale will be described later.

15 discloses a method of setting a product registration time scale, a product order time scale, and a product inventory time scale.

Referring to FIG. 15, product registration time scale 1515, product order time scale 1525, and product inventory time scale 1535 are product registration management data 1510 for generating seller's credit evaluation data, product ordering, respectively. The management data 1520 and the product inventory management data 1530 may be collection periods, respectively.

Hereinafter, for convenience of description, product registration management data 1510, product order management data 1520, and product inventory management data 1530 are expressed in terms of OMS data, and product registration time scale 1515, product order time scale ( 1525) and product inventory time scale 1535 may be expressed in terms of OMS data time scale. The OMS data time scale may be a time unit of OMS data collected for changes in the seller's credit evaluation data.

A seller registers a product, sells the product, and delivers the product constitutes one cycle, and the OMS data time scale may be determined based on the product distribution cycle of product registration, sale, and delivery.

A plurality of determination units may be set in consideration of each product and the number of each product, and a plurality of distribution cycle data for registration, sale, and delivery of the plurality of determination units may be collected. For example, if a seller sells product 1 to product 3, and the number of judgment units for product 1 is 10, the number of judgment units for product 2 is 20, and the number of judgment units for product 3 is 30, the judgment units set for each product are registered. , sales, and delivery cycle data can be collected. The initial product judgment unit can be adaptively adjusted in consideration of seller characteristics, credit rating data feedback, and product characteristics. Afterwards, the product judgment unit takes into account the accumulated amount of distribution cycle data for each product and has the same/similar range for each product. The number of distribution cycle data can be adaptively adjusted to be collected.

The first OMS data time scale 1550 may be determined based on the first distribution cycle data for product 1, the distribution cycle data for product 2, and the third distribution cycle data for product 3.

The first OMS data time scale 1550 can be set to be a multiple of the first distribution cycle data, the second distribution cycle data, and the third distribution cycle data.

Thereafter, the first OMS data time scale 1550 may change to the second OMS data scale 1560 according to the feedback on the credit evaluation result. In consideration of the seller's credit evaluation result feedback, the first OMS data time scale 1550 may be adjusted so that the seller's credit evaluation data may have high reliability, and the second OMS data time scale 1560 may be determined. That is, the first OMS data time scale 1550 may be updated to the second OMS data time scale 1560 in consideration of the seller's feedback on the credit evaluation data.

The first OMS data time scale 1550 can be adjusted according to changes in products and distribution cycles of products, and the second OMS data time scale 1560 corresponds to changes in the first OMS data time scale 1550 and sellers' It can be adjusted according to credit rating data.

16 discloses a method of processing product sales data to generate credit evaluation data for a seller.

Referring to FIG. 16 , although other data may also be considered, a method of pre-processing product sales data 1600 , which may be data having the highest correlation with credit rating data of a seller, for learning is disclosed.

Product sales data 1600 may be converted into predicted product sales revenue data 1620 . Product sales data may be converted into predicted product sales revenue data 1620 for each period in consideration of product sales price and cost information or sales margin rate.

The predicted product sales revenue data 1620 may be converted into data for each period in consideration of a predetermined sales period such as day/week/month, seller financial service period, and the like. A first data reliability 1640 of the predicted product sales revenue data 1620 may be determined based on the predicted product sales revenue data 1620 for each period. The first data reliability 1640 is the reliability of the repeatability of the pattern, and may have a higher value as the probability that similar prediction product sales revenue data 1620 will occur in the future is high.

For example, the prediction product sales revenue data 1620 may be separated based on n time intervals of different lengths to increase predictability in consideration of time, and the first data reliability 1640 may be the highest n number of times. A combination of time intervals may be defined as one prediction cycle. Thereafter, prediction product sales revenue data 1620 for the next n time intervals may be predicted by considering n different time intervals corresponding to the prediction cycle.

The n time intervals for sellers with no seasonality and constant sales can be divided almost uniformly. In the case of a seller having high product seasonality or a relatively large change in product sales, n time intervals may be divided into different lengths in consideration of seasonality and product sales change.

In this way, one prediction cycle for future prediction of the predicted product sales revenue data 1620 is divided into n time intervals, and learning and prediction considering the n time intervals are performed, thereby more accurate prediction of the seller's credit rating data. this could be possible In addition, in the present invention, the second data reliability 1660 of the predicted product sales revenue data 1620 may be determined by comparing with actual revenue. Depending on the second data reliability 1660 , a weight applied to the predicted product sales revenue data 1620 in determining the seller's credit evaluation data may be determined.

Predicted product sales revenue data 1620 based on the product sales data 1600 is determined in consideration of the aforementioned distribution cycle. That is, after the product sales data 1600 is generated, when a period considering the distribution cycle passes, product sales revenue data having a range similar to that of the predicted product sales revenue data 1620 should be generated.

If the error between the predicted product sales revenue data 1620 and the product sales revenue data 1600 is relatively large, it may be determined that the second data reliability 1660 is relatively low. Conversely, it may be determined that the second data reliability 1660 is relatively high as the error between the predicted product sales revenue data 1620 and the product sales revenue data 1600 is relatively small. In consideration of the reliability of the second data 1660, a weight applied to the prediction product sales revenue data 1620 in determining the seller's credit evaluation data may be determined.

17 discloses a method of performing credit evaluation of a seller by processing credit evaluation basic data generated on the WMS.

Referring to FIG. 17 , as described above, WMS is a warehouse management system and is a sales management platform for supporting and optimizing warehouse or delivery center management. WMS can integrate and manage logistics processes such as warehousing, stacking, inventory, packing, and shipping of the seller's products.

Credit evaluation basic data generated on the WMS may include product storage management data 1710 , product release management data 1720 , and product inventory management data 1730 .

Product warehousing management data 1710 is data generated when a product is warehousing in a distribution warehouse. The seller may store the product for sale in the warehouse, and product warehousing management data may be created upon warehousing.

The product release management data 1720 is data generated when a stored product is shipped to a buyer due to sale.

The product inventory management data 1730 is data on products currently remaining in the distribution warehouse in consideration of incoming and outgoing products.

As described above, each of the product warehousing management data 1710, product release management data 1720, and product inventory management data 1730 includes credit rating basic data (distribution) 1740 and credit rating basic data (sales) 1750 It may be pre-processed and generated as first pre-processed credit evaluation data.

Credit evaluation basic data generated by WMS can be used for learning of artificial intelligence engine. Hereinafter, a method of generating credit evaluation basic data by processing each of product warehousing management data 1710 , product warehousing management data 1720 , and product inventory management data 1730 is disclosed.

18 discloses a method of generating credit evaluation basic data by processing product warehousing management data, product warehousing management data, and product inventory management data, respectively.

Referring to FIG. 18 , product sales cycle data 1815 and product sales rate data 1825 may be determined based on product storage management data 1810 and product release management data 1820 . In addition, product stock rate data 1840 and product value data 1850 may be determined based on the product inventory management data 1830 .

Each of the product sales cycle data 1815, product sales rate data 1825, product inventory rate data 1840, and product value data 1850 may be used as basic credit evaluation data of the seller.

The product sales cycle data 1815 relates to a product sales cycle, and may be a cycle from the time the product is stored in the warehouse to the time it is shipped. Sales cycles for each of a plurality of products sold by a seller may be different from each other, and a sales cycle for each of a plurality of products may be determined.

The product sales rate data 1825 is data related to the amount of products stored and the amount of products shipped out, and may be data indicating how much a product has sold compared to the amount of products shipped. Such product sales rate data 1825 may also be determined for each of a plurality of products.

The product value data 1850 is data on the characteristics of a seller's product, and may include data on characteristics of the product, such as product price range, product sales target, product liquidity, product stock characteristics, and the like. The product value data 1850 may be generated based on data such as a product sales target, product liquidity, and product inventory characteristics generated through a product property database associated with an external server.

Data on consumer groups targeted by product, data on whether products can be converted into cash and the cash rate when products are converted into cash, data on costs incurred when storing products and how long they can be kept in stock, product sales volume per hour (day, month, year) ), product return rate, etc. may be stored in a database, and product value data 1850 may be created in conjunction with the database.

19 discloses a method of generating credit evaluation data considering product sales cycle data and product sales rate data.

Referring to FIG. 19 , product sales cycle data 1910 and product sales rate data 1920 may generate credit evaluation basic data (distribution) 1950 .

Based on the supply chain, product sales cycle data 1910 and product sales rate data 1920 may generate credit evaluation basic data (distribution) 1950 .

In the present invention, different weights are set for each of basic credit evaluation data (production) (1960), basic credit evaluation data (distribution) (1950), and basic credit evaluation data (sales) (1970) to set credit evaluation data for the seller. Is generated, and a loan to value ratio (LTV) for the seller may be determined.

Unlike the existing credit evaluation, the loanable amount and interest rate to the user may be determined by additionally considering the product currently in the warehouse, the sales cycle of the product, and the value of the product.

More specifically, based on the product sales cycle data 1910 , information on products to be sold from the point of loan to the maturity date of the loan may be predicted, and an amount and interest rate available for loan up to the maturity date of the loan may be determined. The product value data may determine a loanable amount and an interest rate until the loan maturity date in consideration of the possibility of liquidating the product.

20 discloses a method of processing product sales cycle data to generate credit evaluation data for a seller.

Referring to FIG. 20 , product sales cycle data 2010 may be converted into predicted product sales cycle data 2020 . Predicted product sales cycle data 2020 may be converted into predicted product sales revenue data 2030 for each period in consideration of product sales price and cost information or sales margin rate.

The predicted product sales revenue data 2030 may be converted into data for each period in consideration of a predetermined sales period such as day/week/month, seller financial service period, and the like. A first data reliability 2050 of the predicted product sales revenue data 2030 may be determined based on periodic data of the predicted product sales revenue data 2030 . The first data reliability 2050 is a reliability for repeatability of a pattern, and may have a higher value as the probability that similar predictive product sales revenue data will occur in the future is high.

For example, prediction product sales revenue data may be divided into n time intervals of different lengths to increase predictability in consideration of time, and a combination of n time intervals having the highest first data reliability 2050 is It can be defined as one prediction cycle. Thereafter, prediction product sales revenue data 2030 for the next n time intervals may be predicted by considering n different time intervals corresponding to the prediction cycle.

In this way, one prediction cycle for future prediction of the predicted product sales revenue data 2030 is divided into n time intervals, and learning and prediction considering the n time intervals are performed, thereby more accurate prediction of seller's credit rating data. It could be possible.

In addition, in the present invention, the second data reliability 2060 of the predicted product sales revenue data 2030 may be determined by comparing with actual revenue. Depending on the second data reliability 2060 , a weight applied to the predicted product sales revenue data 2030 in determining the seller's credit evaluation data may be determined.

The predicted product sales revenue data 2030 based on the product sales cycle data 2010 is determined in consideration of the aforementioned distribution cycle. That is, after the product sales cycle data 2030 is generated, when a period considering the distribution cycle passes, product sales revenue data having a range similar to the predicted product sales revenue data 2030 should be generated.

If an error between the predicted product sales revenue data 2030 and the product sales revenue data is relatively large, it may be determined that the second data reliability 2060 is relatively low. Conversely, it may be determined that the second data reliability 2060 is relatively high as the error between the predicted product sales revenue data 2030 and the product sales revenue data is relatively small. In consideration of the reliability of the second data 2060, a weight applied to the predicted product sales revenue data 2030 in determining the seller's credit evaluation data may be determined.

In order to distinguish the source of data, the predicted product sales revenue data based on product sales cycle data (2010) is defined by the term predicted product sales revenue data (product sales cycle data) (2070), and based on product sales data One predicted product sales revenue data may be defined as the term predicted product sales revenue data (product sales data) 2080 .

Each of the predicted product sales revenue data (product sales cycle data) 2070 and the predicted product sales revenue data (product sales data) 2080 takes into account the aforementioned second data reliability 2060, respectively. In determining the credit evaluation data of the weight can be determined.

In FIG. 21, a seller credit evaluation method based on end-to-end data generated in the process of purchasing, selling, inventory, logistics, distribution, and settlement of an e-commerce solution is disclosed.

Referring to FIG. 21 , the e-commerce solution may generate credit evaluation basic data based on data generated in a seller's sales process.

In the e-commerce solution, data generated in the entire process can be classified into three basic credit rating data (production), credit rating basic data (distribution), and credit rating basic data (sales) and processed.

Credit Rating Basic Data (Production), Credit Rating Basic Data (Distribution), Credit Rating Basic Data (Production), Credit Rating Basic Data (Distribution), Credit Rating Basic Data for Individual Products at the time of creation of Credit Rating Basic Data (Sales) (sales) is created. Accordingly, data filtering may be performed to remove redundant data through product-specific data identification.

In addition, credit evaluation basic data (production), credit evaluation basic data (distribution), and credit evaluation basic data (sales) may be configured as a set and used for learning.

A specific product may generate a data format of (x, y, z) = {credit evaluation basic data (production), credit evaluation basic data (distribution), credit evaluation basic data (sales)}.

The first type data 2110 may be data about a product that is only produced (or purchased) by a seller, before being distributed and sold, and has only basic credit evaluation data (production). The first type of data 2110 may include {credit evaluation basic data (production), 0, 0}.

The second type data 2120 is data about a product that has been produced and distributed by a seller but before being sold, and may be data consisting of only basic credit evaluation data (production) and basic credit evaluation data (distribution). The second type data 2120 may include {credit evaluation basic data (production), credit evaluation basic data (distribution), 0}.

The third type data 2130 is data about products produced, distributed, and sold by the seller, and may be data composed of basic credit evaluation data (production), basic credit evaluation data (distribution), and basic credit evaluation data (sale). there is. The third type data 2130 may include {credit evaluation basic data (production), credit evaluation basic data (distribution), credit evaluation basic data (sales)}.

In the present invention, credit evaluation basic data is generated based on the first type data 2110, the second type data 2120, and the third type data 2130, and credit evaluation data for determining financial services available to the seller. can be created.

The first type data 2110, the second type data 2120, and the third type data 2130 are data that can have a greater influence on sales (or net profit) of the seller. Accordingly, the first type data 2110, the second type data 2120, and the third type data 2130 may be used to determine the seller's credit evaluation data with higher weights.

In the present invention, based on credit evaluation data combining the first type data 2110, the second type data 2120, and the third type data 2130 on the time axis in consideration of the financial service period provided to the seller, it can be provided to the seller. A financial service (loan amount and loan interest rate) may be determined.

In FIG. 22, a method of determining financial services (loan amount and loan interest rate) that can be provided to a seller based on credit evaluation data combining first type data, second type data, and third type data on the time axis considering the financial service period this is initiated

Referring to FIG. 22 , first type data, second type data, and third type data may be determined according to the loan period and loan period.

The first type data, the second type data, and the third type data at the current time based on the seller's loan start time are the first type data (current) 2210 and the second type data (current) 2220 and third type data (current) 2230.

Based on the loan end point, the first type data (current) 2210 and the second type data (current) 2220 may be changed to other type data (second type data or third type data). In addition, new first type data, second type data, and third type data may be created based on the loan end time point.

The first type data, the second type data, and the third type data, accumulated and predicted based on the loan end point, are first type data (prediction) 2260, second type data (prediction) 2270, and second type data (prediction) 2270 as prediction data. It can be expressed in terms of the third type data (prediction) 2280. Prediction data may be generated through prediction based on product sales cycle data and distribution cycle data for current data.

In the present invention, the first type data (current) 2210, the second type data (current) 2220, and the third type data (current) 2230, which are current data based on the loan start time, and the loan end time The first type data (prediction) 2260, the second type data (prediction) 2270, and the third type data (prediction) 2280, which are cumulatively predicted prediction data based on the standard, are utilized as the seller's credit evaluation data, and the seller Financial services may be provided to

First type data (current) 2210 as current data, second type data (current) 2220 and third type data (current) 2230, and first type data (prediction) 2260 as predicted data, The second type data (prediction) 2270 and the third type data (prediction) 2280 may be used as credit evaluation data after filtering is performed to eliminate redundancy and grouping is performed considering the reliability of each data.

23 discloses a filtering and grouping method for generating credit evaluation data.

23, first type data (current), second type data (current), third type data (current), first type data (prediction), second type data (prediction), and third type data Filtering on (prediction) may be performed, and redundant data may be removed.

Filtering according to an embodiment of the present invention may be a process of removing data changed from current data to predicted data.

Second type data (prediction) and third type data ( Prediction) can be filtered as redundant data and removed from the current data.

The filtered first type data (current), second type data (current), and third type data (current) and the first type data (prediction), second type data (prediction), and third type data (prediction) are Based on the loan start time and loan end time, it can be divided into three data groups.

The first data group 2310 may include current data remaining after being filtered. That is, the first type data (current), the second type data (current), and the third type data (current) remaining after being filtered because they are not changed into prediction data may be included in the first data group 2310 .

The second data group 2320 may include predicted data (first type data (prediction), second type data (prediction), and third type data (prediction)) changed from current data to predicted data. That is, the second data group 2320 may include predicted data obtained by changing current data.

The third data group 2330 includes prediction data (first-type data (prediction), second-type data (prediction), and third-type data (prediction)) that is not based on current data and is directly generated as predicted data. can do. That is, the third data group may include prediction data generated without current data.

In the present invention, the credit evaluation data for the seller may be determined by sequentially assigning low weights to the first data group 2310, the second data group 2320, and the third data group 2330 in that order. That is, the credit evaluation data for the seller may be determined by setting a relatively high weight for data based on current data.

Embodiments according to the present invention described above may be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. The computer readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention, or may be known and usable to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. medium), and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes generated by a compiler. A hardware device may be modified with one or more software modules to perform processing according to the present invention and vice versa.

Although the present invention has been described above with specific details such as specific components and limited embodiments and drawings, these are only provided to help a more general understanding of the present invention, and the present invention is not limited to the above embodiments, and the present invention Those with ordinary knowledge in the technical field to which the invention belongs may seek various modifications and changes from these descriptions.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments and should not be determined, and all scopes equivalent to or equivalently changed from the claims as well as the claims described below are within the scope of the spirit of the present invention. will be said to belong to

Claims

The end-to-end data-based credit evaluation method that occurs in the process of purchasing, selling, inventory, logistics, distribution, and settlement of e-commerce solutions is,

collecting basic credit evaluation data for credit evaluation of the seller by a credit evaluation basic data collection unit;

pre-processing the collected credit evaluation basic data by a credit evaluation basic data pre-processing unit;

generating an artificial intelligence engine for the credit evaluation of the seller based on the preprocessed credit evaluation basic data by a credit evaluation basic data learning unit; and

A credit rating unit determining credit rating data of the seller based on the artificial intelligence engine,

In the e-commerce solution, the credit evaluation basic data generated in the entire process is classified into three credit evaluation basic data (production), credit evaluation basic data (distribution), and credit evaluation basic data (sales). .
According to claim 1,

The first type data is data about a product that is only produced or purchased by the seller, but before being distributed or sold, and is data consisting only of the basic credit evaluation data (production);

The second type data is data about products that have been produced and distributed by the seller but before being sold, and is data consisting of only the basic credit evaluation data (production) and the basic credit evaluation data (distribution);

The third type data is data on products produced, distributed, and sold by the seller, and is data composed of the credit evaluation basic data (production), the credit evaluation basic data (distribution), and the credit evaluation basic data (sale). ,

wherein the credit evaluation data is determined using the credit evaluation basic data based on the first type data, the second type data, and the third type data.
According to claim 2,

wherein the credit evaluation data is determined using the credit evaluation basic data based on the first type data, the second type data, and the third type data.
An end-to-end data-based credit evaluation device that occurs in the process of purchasing, selling, inventory, logistics, distribution, and settlement of e-commerce solutions,

a credit evaluation basic data collection unit implemented to collect credit evaluation basic data for credit evaluation of the seller;

a credit evaluation basic data pre-processing unit configured to pre-process the collected credit evaluation basic data;

a credit evaluation basic data learning unit configured to generate an artificial intelligence engine for the credit evaluation of the seller based on the preprocessed credit evaluation basic data; and

Including a credit evaluation unit configured to determine credit evaluation data of the seller based on the artificial intelligence engine;

In the e-commerce solution, the credit evaluation basic data generated in the entire process is classified into three credit evaluation basic data (production), credit evaluation basic data (distribution), and credit evaluation basic data (sales) and processed. evaluation device.
According to claim 4,

The first type data is data about a product that is only produced or purchased by the seller, but before being distributed or sold, and is data consisting only of the basic credit evaluation data (production);

The second type data is data about products that have been produced and distributed by the seller but before being sold, and is data consisting of only the basic credit evaluation data (production) and the basic credit evaluation data (distribution);

The third type data is data on products produced, distributed, and sold by the seller, and is data composed of the credit evaluation basic data (production), the credit evaluation basic data (distribution), and the credit evaluation basic data (sale). ,

wherein the credit evaluation data is determined using the credit evaluation basic data based on the first type data, the second type data, and the third type data.
According to claim 5,

wherein the credit evaluation data is determined using the credit evaluation basic data based on the first type data, the second type data, and the third type data.