WO2023136491A1 - Credit rating method based on data collected by ecommerce sales and distribution management system platform and device for performing same - Google Patents

Credit rating method based on data collected by ecommerce sales and distribution management system platform and device for performing same Download PDF

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Publication number
WO2023136491A1
WO2023136491A1 PCT/KR2022/020814 KR2022020814W WO2023136491A1 WO 2023136491 A1 WO2023136491 A1 WO 2023136491A1 KR 2022020814 W KR2022020814 W KR 2022020814W WO 2023136491 A1 WO2023136491 A1 WO 2023136491A1
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Prior art keywords
credit evaluation
data
basic data
seller
credit
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PCT/KR2022/020814
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French (fr)
Korean (ko)
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강정석
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주식회사 에이젠글로벌
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0609—Buyer or seller confidence or verification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof

Definitions

  • the present invention relates to a credit evaluation method based on e-commerce sales and distribution management system platform aggregate data and an apparatus for performing the method. More specifically, it relates to a method and apparatus for performing a credit evaluation on a seller based on basic credit evaluation data collected through an e-commerce sales and distribution management system platform.
  • the financial big data infrastructure consists of big data open systems, data exchanges, and data specialized institutions.
  • the object of the present invention is to solve all of the above problems.
  • the present invention is to collect basic credit evaluation data through an e-commerce sales and distribution management system platform, and to provide customized financial services to sellers by performing an adaptive credit evaluation on sellers based on the basic credit evaluation data.
  • the present invention preprocesses the credit evaluation basic data collected through the e-commerce sales and distribution management system platform in consideration of the characteristics of the supply chain and the seller, and generates an artificial intelligence model using the preprocessed credit evaluation basic data. Its purpose is to provide customized financial services to sellers.
  • a credit evaluation method for a seller includes the steps of collecting basic credit evaluation data for credit evaluation of a seller by a credit evaluation basic data collection unit, the credit evaluation basic data collected by a credit evaluation basic data preprocessing 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; Determining credit rating data.
  • the credit evaluation basic data is collected through various sales management platforms, and the sales management platforms are OMS (order management system), ERP (enterprise resource planning), WMS (warehouse management system), ECS (E-commerce solution) and at least one of product registration information, stock information, order information, return information, payment information, sales information, settlement information, and refund information generated by the OMS.
  • OMS order management system
  • ERP enterprise resource planning
  • WMS warehouse management system
  • ECS E-commerce solution
  • the credit evaluation basic data collection unit converts at least one of product warehousing information, product inventory information, product warehousing information, and product delivery information generated from the WMS into the credit rating of the seller. It can be collected as basic data.
  • the credit evaluation basic data pre-processing unit performs first pre-processing and second pre-processing, and the first pre-processing transmits the first pre-processed credit evaluation basic data obtained by pre-processing the credit evaluation basic data to the credit evaluation basic data learning unit.
  • the second pre-processing is performed to transmit the second pre-processed credit rating basic data obtained by preprocessing the credit rating basic data to the credit rating unit
  • the first preprocessing includes first preprocessing (supply chain) and first preprocessing. It includes pre-processing (seller), and the first pre-processing (supply chain) includes credit rating basic data (production stage), credit rating basic data (distribution stage), and credit rating basic data preprocessed for each supply chain step.
  • Sales step is generated as the first pre-processed credit evaluation basic data
  • the first pre-processing (seller) pre-processing considering seller characteristics classifies the credit evaluation basic data into seller data based on seller characteristics and seller data
  • the first pre-processed credit evaluation basic data may be generated by processing through augmentation.
  • a credit evaluation apparatus for performing a credit evaluation of a seller includes a credit evaluation basic data collection unit implemented to collect credit evaluation basic data for credit evaluation of the seller, the collected credit evaluation basic data A credit evaluation basic data pre-processing unit implemented to pre-process the credit evaluation basic data, a credit evaluation basic data learning unit implemented to generate an artificial intelligence engine for the credit evaluation of the seller based on the pre-processed credit evaluation basic data, and the artificial intelligence engine It may include a credit evaluation unit configured to determine credit evaluation data of the seller based on the above.
  • the credit evaluation basic data is collected through various sales management platforms, and the sales management platforms are OMS (order management system), ERP (enterprise resource planning), WMS (warehouse management system), ECS (E-commerce solution) and at least one of product registration information, stock information, order information, return information, payment information, sales information, settlement information, and refund information generated by the OMS.
  • OMS order management system
  • ERP enterprise resource planning
  • WMS warehouse management system
  • ECS E-commerce solution
  • the credit evaluation basic data collection unit converts at least one of product warehousing information, product inventory information, product warehousing information, and product delivery information generated from the WMS into the credit rating of the seller. It can be collected as basic data.
  • the credit evaluation basic data pre-processing unit performs first pre-processing and second pre-processing, and the first pre-processing transmits the first pre-processed credit evaluation basic data obtained by pre-processing the credit evaluation basic data to the credit evaluation basic data learning unit.
  • the second pre-processing is performed to transmit the second pre-processed credit rating basic data obtained by preprocessing the credit rating basic data to the credit rating unit
  • the first preprocessing includes first preprocessing (supply chain) and first preprocessing. It includes pre-processing (seller), and the first pre-processing (supply chain) includes credit rating basic data (production stage), credit rating basic data (distribution stage), and credit rating basic data preprocessed for each supply chain step.
  • Sales step is generated as the first pre-processed credit evaluation basic data
  • the first pre-processing (seller) pre-processing considering seller characteristics classifies the credit evaluation basic data into seller data based on seller characteristics and seller data
  • the first pre-processed credit evaluation basic data may be generated by processing through augmentation.
  • credit evaluation basic data is collected through the e-commerce sales and distribution management system platform, and based on the credit evaluation basic data, an adaptive credit evaluation is performed on the seller, so that a customized financial service can be provided to the seller.
  • credit evaluation basic data collected through the e-commerce sales and distribution management system platform is preprocessed in consideration of the characteristics of the supply chain and seller, and the creation of an artificial intelligence model using the preprocessed credit evaluation basic data Through this, customized financial services can be provided to sellers.
  • FIG. 1 is a conceptual diagram illustrating a credit evaluation apparatus according to an embodiment of the present invention.
  • FIG. 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.
  • FIG. 3 is a conceptual diagram illustrating the operation of a credit basic data pre-processing unit according to an embodiment of the present invention.
  • FIG. 4 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
  • FIG. 5 is a conceptual diagram illustrating a first preprocessing (a seller) according to an embodiment of the present invention.
  • FIG. 6 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
  • FIG. 7 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
  • FIG. 8 is a conceptual diagram illustrating a method of performing a first preprocessing according to an embodiment of the present invention.
  • FIG. 9 is a conceptual diagram illustrating the operation of a credit evaluation unit according to an embodiment of the present invention.
  • FIG. 1 is a conceptual diagram illustrating a credit evaluation apparatus according to an embodiment of the present invention.
  • 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.
  • 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.
  • 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
  • FIG. 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.
  • 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 enterprise resource planning
  • WMS warehouse management system
  • ECS E-commerce solution
  • OMS 210
  • ERP enterprise resource planning
  • WMS warehouse management system
  • ECS E-commerce solution
  • 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.
  • the OMS 210 functions such as batch product registration modification, order collection, invoice registration and transmission, inventory management, and the like may be provided.
  • 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, stocking, stocking, picking, and shipping of the seller's products 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.
  • 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.
  • FIG. 3 is a conceptual diagram illustrating the operation of a credit basic data pre-processing unit according to an embodiment of the present invention.
  • 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 .
  • 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 .
  • the first pre-processing 310 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.
  • preprocessing considering supply chain characteristics may be performed in consideration of a supply chain step corresponding to the credit evaluation basic data 300 .
  • preprocessing considering supply chain characteristics may be expressed as a first preprocessing (supply chain) 313 .
  • 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 .
  • the preprocessing considering seller characteristics may be performed through seller data classification and seller data augmentation based on seller characteristics.
  • 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.
  • FIG. 4 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
  • FIG. 4 a first pre-processing (seller) and a first pre-processing (supply chain) applied to credit evaluation basic data are disclosed.
  • credit evaluation basic data classified by seller through the first pre-processing (seller) 400 is pre-processed for each supply chain step through the first pre-processing (supply chain) 450, and the first pre-processed basic credit data (490).
  • the first pre-processing (supply chain) 450 may generate the first pre-processing credit 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • FIG. 5 is a conceptual diagram illustrating a first preprocessing (a seller) according to an embodiment of the present invention.
  • 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.
  • 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.
  • 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.
  • specific credit evaluation basic data may be augmented and generated as a plurality of lower credit evaluation basic data 540 .
  • data such as the size of the return, the discard rate, the average return rate, the stability of the change in the return rate, and the number of times the return rate exceeds the MAX may be generated as the lower credit evaluation basic data 540 .
  • learning 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 may be expressed in terms of a lower data augmentation 520 .
  • FIG. 6 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
  • 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.
  • 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.
  • FIG. 6(a) is a method of augmenting data by analyzing the credit evaluation base data 600 on a time scale 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 .
  • 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.
  • data on the number of sellers with an average return rate for 36 months may be augmented and generated.
  • FIG. 6 is a method of augmenting data by statistically analyzing the credit evaluation base data 650 among 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 .
  • 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.
  • FIG. 7 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
  • 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.
  • a method of augmenting data by two-dimensionally analyzing credit evaluation basic data among data augmentation methods is disclosed.
  • a method of augmenting credit evaluation basic data 700 as two-dimensional data among data augmentation methods is disclosed.
  • 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 .
  • 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
  • the second dimension may be the number of months in which the return rate exceeds 5% for 36 months.
  • FIG. 8 is a conceptual diagram illustrating a method of performing a first preprocessing according to an embodiment of the present invention.
  • the first preprocessing may use lower data augmentation 810, time scale data augmentation 820, statistical data augmentation 830, and multidimensional data augmentation 840.
  • a first pre-processing may be selectively performed to learn the plurality of artificial intelligence engines included in the credit evaluation basic data learning unit.
  • 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.
  • sub-data augmentation for 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.
  • various first preprocessing may be performed according to the properties of predicted credit evaluation data, and various artificial intelligence models may be generated.
  • FIG. 9 is a conceptual diagram illustrating the operation of a credit evaluation unit according to an embodiment of the present invention.
  • 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.
  • 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.
  • the credit evaluation unit may determine a reliability level for each seller characteristic information for each of the plurality of artificial intelligence engines.
  • 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
  • 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.
  • 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 vers

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Abstract

The present invention relates to a credit rating method based on data collected by an ecommerce sales and distribution management system platform and a device for performing same. A credit rating method for a seller may comprise the steps in which: a credit rating basic data collection unit collects credit rating basic data for rating the credit of the seller; a credit rating basic data preprocessing unit preprocesses the collected credit rating basic data; a credit rating basic data learning unit generates, on the basis of the preprocessed credit rating basic data, an artificial intelligence engine for rating the credit of the seller; and a credit rating unit determines credit rating data of the seller on the basis of the artificial intelligence engine.

Description

이커머스 판매 및 유통 관리 시스템 플랫폼 취합 데이터를 기반으로 한 신용 평가 방법 및 이러한 방법을 수행하는 장치Credit evaluation method based on e-commerce sales and distribution management system platform aggregate data and device for performing the method
본 발명은 이커머스 판매 및 유통 관리 시스템 플랫폼 취합 데이터를 기반으로 한 신용 평가 방법 및 이러한 방법을 수행하는 장치에 관한 것이다. 보다 상세하게는 이커머스 판매 및 유통 관리 시스템 플랫폼을 통해 수집된 신용 평가 기초 데이터를 기반으로 판매자에 대한 신용 평가를 수행하는 방법 및 장치에 관한 것이다.The present invention relates to a credit evaluation method based on e-commerce sales and distribution management system platform aggregate data and an apparatus for performing the method. More specifically, it relates to a method and apparatus for performing a credit evaluation on a seller based on basic credit evaluation data collected through an e-commerce sales and distribution management system platform.
4차 산업혁명에 의해 촉발된 지능 정보 사회로 진입하면서 데이터의 무한한 활용 가능성이 데이터 산업의 변화를 초래하고 있다. 데이터 시대가 도래함에 따라 향후 데이터 산업의 수준이 국가 사이에 경쟁력의 차이를 결정하게 될 것이다. 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.
특히 금융 시장에서의 빅데이터 인프라 구축은 매우 시급할 뿐만 아니라, 머지 않아 국가의 데이터 산업의 향방을 좌우할만큼 중요한 자산이 되었다. 금융 빅데이터 인프라는 빅데이터 개방 시스템, 데이터 거래소, 데이터 전문기관 등으로 구성된다. 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.
이러한 빅데이터 기반의 사용자 금융 데이터를 기반으로 한 새로운 금융 상품에 대한 연구가 필요하다. 사용자 금융 상품에 대한 인공 지능 기반의 학습을 통해 다양한 리스크 분석이 가능하고, 리스크 분석을 기반으로 현재까지 없었던 새로운 금융 서비스를 사용자들에게 제공할 수 있다. 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.
따라서, 사용자의 금융 데이터를 활용하고 사용자의 금융 데이터를 기반으로 다양한 금융 서비스를 제공하기 위한 구체적인 방법에 대한 연구가 필요하다.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.
본 발명은 상술한 문제점을 모두 해결하는 것을 그 목적으로 한다.The object of the present invention is to solve all of the above problems.
또한, 본 발명은, 이커머스 판매 및 유통 관리 시스템 플랫폼을 통해 신용 평가 기초 데이터를 수집하고, 신용 평가 기초 데이터를 기반으로 판매자에게 적응적인 신용 평가를 수행하여 판매자에게 맞춤형 금융 서비스를 제공하는 것을 목적으로 한다.In addition, the present invention is to collect basic credit evaluation data through an e-commerce sales and distribution management system platform, and to provide customized financial services to sellers by performing an adaptive credit evaluation on sellers based on the basic credit evaluation data. to be
또한, 본 발명은, 이커머스 판매 및 유통 관리 시스템 플랫폼을 통해 수집된 신용 평가 기초 데이터를 서플라이 체인 및 판매자의 특성을 고려하여 전처리하고, 전처리한 신용 평가 기초 데이터를 사용한 인공 지능 모델의 생성을 통해 판매자에게 맞춤형 금융 서비스를 제공하는 것을 목적으로 한다.In addition, the present invention preprocesses the credit evaluation basic data collected through the e-commerce sales and distribution management system platform in consideration of the characteristics of the supply chain and the seller, and generates an artificial intelligence model using the preprocessed credit evaluation basic data. Its purpose is to provide customized financial services to sellers.
상기 목적을 달성하기 위한 본 발명의 대표적인 구성은 다음과 같다.Representative configurations of the present invention for achieving the above object are as follows.
본 발명의 일 실시예에 따르면, 판매자에 대한 신용 평가 방법은 신용 평가 기초 데이터 수집부가 판매자의 신용 평가를 위한 신용 평가 기초 데이터를 수집하는 단계, 신용 평가 기초 데이터 전처리부가 수집된 상기 신용 평가 기초 데이터를 전처리하는 단계, 신용 평가 기초 데이터 학습부가 전처리된 상기 신용 평가 기초 데이터를 기반으로 상기 판매자의 상기 신용 평가를 위한 인공 지능 엔진을 생성하는 단계와 신용 평가부가 상기 인공 지능 엔진을 기반으로 상기 판매자의 신용 평가 데이터를 결정하는 단계를 포함할 수 있다.According to an embodiment of the present invention, a credit evaluation method for a seller includes the steps of collecting basic credit evaluation data for credit evaluation of a seller by a credit evaluation basic data collection unit, the credit evaluation basic data collected by a credit evaluation basic data preprocessing 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; Determining credit rating data.
한편, 상기 신용 평가 기초 데이터는 다양한 판매 관리 플랫폼을 통해 수집되고, 상기 판매 관리 플랫폼은 OMS(order management system), ERP(enterprise resource planning), WMS(warehouse management system), ECS(E-commerce solution) 중 적어도 하나를 포함하고, 상기 신용 평가 기초 데이터 수집부는 상기 OMS에서 발생된 상품 등록 정보, 재고 정보, 주문 정보, 반품 정보, 결제 정보, 매출 정보, 정산 정보, 환불 정보 중 적어도 하나의 정보를 상기 판매자의 상기 신용 평가 기초 데이터로서 수집하고, 상기 신용 평가 기초 데이터 수집부는 상기 WMS에서 발생된 상품 입고 정보, 상품 재고 정보, 상품 출고 정보, 상품 배송 정보 중 적어도 하나의 정보를 상기 판매자의 상기 신용 평가 기초 데이터로서 수집할 수 있다.Meanwhile, the credit evaluation basic data is collected through various sales management platforms, and the sales management platforms are OMS (order management system), ERP (enterprise resource planning), WMS (warehouse management system), ECS (E-commerce solution) and at least one of product registration information, stock information, order information, return information, payment information, sales information, settlement information, and refund information generated by the OMS. Collected as the credit evaluation basic data of the seller, and the credit evaluation basic data collection unit converts at least one of product warehousing information, product inventory information, product warehousing information, and product delivery information generated from the WMS into the credit rating of the seller. It can be collected as basic data.
또한, 상기 신용 평가 기초 데이터 전처리부는 제1 전처리 및 제2 전처리를 수행하고, 상기 제1 전처리는 상기 신용 평가 기초 데이터를 전처리한 제1 전처리 신용 평가 기초 데이터를 상기 신용 평가 기초 데이터 학습부로 전송하기 위해 수행되고, 상기 제2 전처리는 상기 신용 평가 기초 데이터를 전처리한 제2 전처리 신용 평가 기초 데이터를 상기 신용 평가부로 전송하기 위해 수행되고, 상기 제1 전처리는 제1 전처리(서플라이 체인) 및 제1 전처리(판매자)를 포함하고, 상기 제1 전처리(서플라이 체인)는 서플라이 체인 단계별로 상기 신용 평가 기초 데이터를 전처리한 신용 평가 기초 데이터(생산 단계), 신용 평가 기초 데이터(유통 단계), 신용 평가 기초 데이터(판매 단계)를 상기 제1 전처리 신용 평가 기초 데이터로서 생성하고, 상기 제1 전처리(판매자)는 판매자 특성을 고려한 전처리는 상기 신용 평가 기초 데이터를 판매자 특성을 기초로 한 판매자 데이터 분류 및 판매자 데이터 증강(augmentation)을 통해 처리하여 상기 제1 전처리 신용 평가 기초 데이터를 생성할 수 있다.In addition, the credit evaluation basic data pre-processing unit performs first pre-processing and second pre-processing, and the first pre-processing transmits the first pre-processed credit evaluation basic data obtained by pre-processing the credit evaluation basic data to the credit evaluation basic data learning unit. The second pre-processing is performed to transmit the second pre-processed credit rating basic data obtained by preprocessing the credit rating basic data to the credit rating unit, and the first preprocessing includes first preprocessing (supply chain) and first preprocessing. It includes pre-processing (seller), and the first pre-processing (supply chain) includes credit rating basic data (production stage), credit rating basic data (distribution stage), and credit rating basic data preprocessed for each supply chain step. Data (sales step) is generated as the first pre-processed credit evaluation basic data, and the first pre-processing (seller) pre-processing considering seller characteristics classifies the credit evaluation basic data into seller data based on seller characteristics and seller data The first pre-processed credit evaluation basic data may be generated by processing through augmentation.
본 발명의 다른 실시예에 따르면, 판매자에 대한 신용 평가를 수행하는 신용 평가 장치는 판매자의 신용 평가를 위한 신용 평가 기초 데이터를 수집하도록 구현되는 신용 평가 기초 데이터 수집부, 수집된 상기 신용 평가 기초 데이터를 전처리하도록 구현되는 신용 평가 기초 데이터 전처리부, 전처리된 상기 신용 평가 기초 데이터를 기반으로 상기 판매자의 상기 신용 평가를 위한 인공 지능 엔진을 생성하도록 구현되는 신용 평가 기초 데이터 학습부와 상기 인공 지능 엔진을 기반으로 상기 판매자의 신용 평가 데이터를 결정하도록 구현되는 신용 평가부를 포함할 수 있다.According to another embodiment of the present invention, a credit evaluation apparatus for performing a credit evaluation of a seller includes a credit evaluation basic data collection unit implemented to collect credit evaluation basic data for credit evaluation of the seller, the collected credit evaluation basic data A credit evaluation basic data pre-processing unit implemented to pre-process the credit evaluation basic data, a credit evaluation basic data learning unit implemented to generate an artificial intelligence engine for the credit evaluation of the seller based on the pre-processed credit evaluation basic data, and the artificial intelligence engine It may include a credit evaluation unit configured to determine credit evaluation data of the seller based on the above.
한편, 상기 신용 평가 기초 데이터는 다양한 판매 관리 플랫폼을 통해 수집되고, 상기 판매 관리 플랫폼은 OMS(order management system), ERP(enterprise resource planning), WMS(warehouse management system), ECS(E-commerce solution) 중 적어도 하나를 포함하고, 상기 신용 평가 기초 데이터 수집부는 상기 OMS에서 발생된 상품 등록 정보, 재고 정보, 주문 정보, 반품 정보, 결제 정보, 매출 정보, 정산 정보, 환불 정보 중 적어도 하나의 정보를 상기 판매자의 상기 신용 평가 기초 데이터로서 수집하고, 상기 신용 평가 기초 데이터 수집부는 상기 WMS에서 발생된 상품 입고 정보, 상품 재고 정보, 상품 출고 정보, 상품 배송 정보 중 적어도 하나의 정보를 상기 판매자의 상기 신용 평가 기초 데이터로서 수집할 수 있다.Meanwhile, the credit evaluation basic data is collected through various sales management platforms, and the sales management platforms are OMS (order management system), ERP (enterprise resource planning), WMS (warehouse management system), ECS (E-commerce solution) and at least one of product registration information, stock information, order information, return information, payment information, sales information, settlement information, and refund information generated by the OMS. Collected as the credit evaluation basic data of the seller, and the credit evaluation basic data collection unit converts at least one of product warehousing information, product inventory information, product warehousing information, and product delivery information generated from the WMS into the credit rating of the seller. It can be collected as basic data.
또한, 상기 신용 평가 기초 데이터 전처리부는 제1 전처리 및 제2 전처리를 수행하고, 상기 제1 전처리는 상기 신용 평가 기초 데이터를 전처리한 제1 전처리 신용 평가 기초 데이터를 상기 신용 평가 기초 데이터 학습부로 전송하기 위해 수행되고, 상기 제2 전처리는 상기 신용 평가 기초 데이터를 전처리한 제2 전처리 신용 평가 기초 데이터를 상기 신용 평가부로 전송하기 위해 수행되고, 상기 제1 전처리는 제1 전처리(서플라이 체인) 및 제1 전처리(판매자)를 포함하고, 상기 제1 전처리(서플라이 체인)는 서플라이 체인 단계별로 상기 신용 평가 기초 데이터를 전처리한 신용 평가 기초 데이터(생산 단계), 신용 평가 기초 데이터(유통 단계), 신용 평가 기초 데이터(판매 단계)를 상기 제1 전처리 신용 평가 기초 데이터로서 생성하고, 상기 제1 전처리(판매자)는 판매자 특성을 고려한 전처리는 상기 신용 평가 기초 데이터를 판매자 특성을 기초로 한 판매자 데이터 분류 및 판매자 데이터 증강(augmentation)을 통해 처리하여 상기 제1 전처리 신용 평가 기초 데이터를 생성할 수 있다.In addition, the credit evaluation basic data pre-processing unit performs first pre-processing and second pre-processing, and the first pre-processing transmits the first pre-processed credit evaluation basic data obtained by pre-processing the credit evaluation basic data to the credit evaluation basic data learning unit. The second pre-processing is performed to transmit the second pre-processed credit rating basic data obtained by preprocessing the credit rating basic data to the credit rating unit, and the first preprocessing includes first preprocessing (supply chain) and first preprocessing. It includes pre-processing (seller), and the first pre-processing (supply chain) includes credit rating basic data (production stage), credit rating basic data (distribution stage), and credit rating basic data preprocessed for each supply chain step. Data (sales step) is generated as the first pre-processed credit evaluation basic data, and the first pre-processing (seller) pre-processing considering seller characteristics classifies the credit evaluation basic data into seller data based on seller characteristics and seller data The first pre-processed credit evaluation basic data may be generated by processing through augmentation.
본 발명에 의하면, 이커머스 판매 및 유통 관리 시스템 플랫폼을 통해 신용 평가 기초 데이터가 수집되고, 신용 평가 기초 데이터를 기반으로 판매자에게 적응적인 신용 평가를 수행하여 판매자에게 맞춤형 금융 서비스가 제공될 수 있다.According to the present invention, credit evaluation basic data is collected through the e-commerce sales and distribution management system platform, and based on the credit evaluation basic data, an adaptive credit evaluation is performed on the seller, so that a customized financial service can be provided to the seller.
또한, 본 발명에 의하면, 이커머스 판매 및 유통 관리 시스템 플랫폼을 통해 수집된 신용 평가 기초 데이터가 서플라이 체인 및 판매자의 특성을 고려하여 전처리되고, 전처리한 신용 평가 기초 데이터를 사용한 인공 지능 모델의 생성을 통해 판매자에게 맞춤형 금융 서비스가 제공될 수 있다.In addition, according to the present invention, credit evaluation basic data collected through the e-commerce sales and distribution management system platform is preprocessed in consideration of the characteristics of the supply chain and seller, and the creation of an artificial intelligence model using the preprocessed credit evaluation basic data Through this, customized financial services can be provided to sellers.
도 1은 본 발명의 실시예에 따른 신용 평가 장치를 나타낸 개념도이다.1 is a conceptual diagram illustrating a credit evaluation apparatus according to an embodiment of the present invention.
도 2는 본 발명의 실시예에 따른 판매 관리 플랫폼과 판매 관리 플랫폼을 통해 신용 평가 기초 데이터를 수집하는 방법을 나타낸 개념도이다.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.
도 3은 본 발명의 실시예에 따른 신용 기초 데이터 전처리부의 동작을 나타낸 개념도이다.3 is a conceptual diagram illustrating the operation of a credit basic data pre-processing unit according to an embodiment of the present invention.
도 4는 본 발명의 실시예에 따른 제1 전처리를 나타낸 개념도이다.4 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
도 5는 본 발명의 실시예에 따른 제1 전처리(판매자)를 나타낸 개념도이다.5 is a conceptual diagram illustrating a first preprocessing (a seller) according to an embodiment of the present invention.
도 6은 본 발명의 실시예에 따른 제1 전처리를 나타낸 개념도이다.6 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
도 7은 본 발명의 실시예에 따른 제1 전처리를 나타낸 개념도이다.7 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
도 8은 본 발명의 실시예에 따른 제1 전처리를 수행하는 방법을 나타낸 개념도이다.8 is a conceptual diagram illustrating a method of performing a first preprocessing according to an embodiment of the present invention.
도 9는 본 발명의 실시예에 따른 신용 평가부의 동작을 나타낸 개념도이다.9 is a conceptual diagram illustrating the operation of a credit evaluation unit according to an embodiment of the present invention.
후술하는 본 발명에 대한 상세한 설명은, 본 발명이 실시될 수 있는 특정 실시예를 예시로서 도시하는 첨부 도면을 참조한다. 이러한 실시예는 당업자가 본 발명을 실시할 수 있기에 충분하도록 상세히 설명된다. 본 발명의 다양한 실시예는 서로 다르지만 상호 배타적일 필요는 없음이 이해되어야 한다. 예를 들어, 본 명세서에 기재되어 있는 특정 형상, 구조 및 특성은 본 발명의 정신과 범위를 벗어나지 않으면서 일 실시예로부터 다른 실시예로 변경되어 구현될 수 있다. 또한, 각각의 실시예 내의 개별 구성요소의 위치 또는 배치도 본 발명의 정신과 범위를 벗어나지 않으면서 변경될 수 있음이 이해되어야 한다. 따라서, 후술하는 상세한 설명은 한정적인 의미로서 행하여 지는 것이 아니며, 본 발명의 범위는 특허청구범위의 청구항들이 청구하는 범위 및 그와 균등한 모든 범위를 포괄하는 것으로 받아들여져야 한다. 도면에서 유사한 참조부호는 여러 측면에 걸쳐서 동일하거나 유사한 구성요소를 나타낸다.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.
이하에서는, 본 발명이 속하는 기술분야에서 통상의 지식을 가진 자가 본 발명을 용이하게 실시할 수 있도록 하기 위하여, 본 발명의 여러 바람직한 실시예에 관하여 첨부된 도면을 참조하여 상세히 설명하기로 한다.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.
도 1은 본 발명의 실시예에 따른 신용 평가 장치를 나타낸 개념도이다.1 is a conceptual diagram illustrating a credit evaluation apparatus according to an embodiment of the present invention.
도 1에서는 이커머스 판매 및 유통 관리 시스템 플랫폼을 통해 수집된 데이터를 기반으로 판매자에 대한 신용 평가를 수행하는 신용 평가 장치가 개시된다.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.
도 1을 참조하면, 신용 평가 장치는 신용 평가 기초 데이터 수집부(110), 신용 평가 기초 데이터 전처리부(120), 신용 평가 기초 데이터 학습부(130), 신용 평가부(140), 금융 서비스부(150) 및 프로세서(160)를 포함할 수 있다.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.
신용 평가 기초 데이터 수집부(110)는 판매자의 신용 평가를 위한 신용 평가 기초 데이터를 수집하기 위해 구현될 수 있다. 판매자는 이커머스를 통해 상품을 판매하고 유통하기 위한 다양한 플랫폼을 통해 상품을 판매할 수 있다. 판매자의 상품 판매, 상품 유통, 상품 결제와 관련된 다양한 데이터를 관리하기 위한 이커머스 판매 및 유통 관리 시스템 플랫폼은 판매 관리 플랫폼(100)이라는 용어로 표현될 수 있다. 신용 평가 기초 데이터 수집부(110)는 다양한 판매 관리 플랫폼을 통해 판매자의 신용 평가를 위한 신용 평가 기초 데이터를 수집하도록 구현될 수 있다. 구체적인 판매 관리 플랫폼(100)은 후술된다. 본 발명에서 사용되는 상품이라는 용어는 판매자에 의해 제공되는 서비스도 하나의 상품으로서 포함하는 의미로 사용될 수 있다.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.
신용 평가 기초 데이터 전처리부(120)는 수집된 신용 평가 기초 데이터를 전처리하기 위해 구현될 수 있다. 신용 평가 기초 데이터는 전처리되어 신용 평가를 위한 인공 지능 엔진의 학습을 위해 활용될 수도 있고, 판매자의 신용 평가를 위해 활용될 수도 있다.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.
신용 평가를 위한 인공 지능 엔진의 학습을 위한 신용 평가 기초 데이터는 제1 전처리를 통해 신용 평가 기초 데이터 학습부(130)로 전송될 수 있다. 판매자의 신용 평가를 위한 인공 지능 엔진의 학습을 위한 신용 평가 기초 데이터는 제2 전처리를 통해 신용 평가부(140)로 전송될 수 있다.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.
신용 평가 기초 데이터 학습부(130)는 판매자의 신용 평가를 위한 인공 지능 학습을 위해 구현될 수 있다. 신용 평가 기초 데이터 학습부(130)는 판매자의 신용 평가를 위한 복수의 인공 지능 엔진을 포함하고, 복수의 인공 지능 엔진 각각은 판매자의 신용 평가를 위한 하위 신용 평가 요소를 결정하기 위해 구현될 수 있다.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. .
신용 평가부(140)는 판매자의 신용을 평가하여 판매자의 신용 평가 데이터를 결정하기 위해 구현될 수 있다. 신용 평가부(140)는 신용 평가 기초 데이터 학습부의 복수의 인공 지능 엔진 각각에 의해 결정된 복수의 하위 신용 평가 요소를 기반으로 판매자의 신용 평가 데이터를 결정할 수 있다. 또한 신용 평가부(140)는 인공 지능 엔진이 아닌 별도의 알고리즘을 기반으로 판매자의 신용 평가 데이터를 결정할 수 있다. 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.
금융 서비스부(150)는 판매자의 신용 평가 데이터를 기반으로 판매자에게 금융 서비스를 제공하기 위해 구현될 수 있다. The financial service unit 150 may be implemented to provide financial services to the seller based on the seller's credit evaluation data.
프로세서(160)는 신용 평가 기초 데이터 수집부(110), 신용 평가 기초 데이터 전처리부(120), 신용 평가 기초 데이터 학습부(130), 신용 평가부(140), 금융 서비스부(150)의 동작을 제어하기 위해 구현될 수 있다.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
도 2는 본 발명의 실시예에 따른 판매 관리 플랫폼과 판매 관리 플랫폼을 통해 신용 평가 기초 데이터를 수집하는 방법을 나타낸 개념도이다.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.
도 2에서는 판매 관리 플랫폼과 판매 관리 플랫폼에서 수집되는 신용 평가 기초 데이터가 개시된다.2 discloses a sales management platform and credit evaluation basic data collected from the sales management platform.
도 2를 참조하면, 판매 관리 플랫폼은 OMS(order management system)(210), ERP(enterprise resource planning)(220), WMS(warehouse management system)(230), ECS(E-commerce solution)(240) 등을 포함할 수 있다. OMS(210), ERP(220), WMS(230), ECS(240)는 하나의 예시로서 판매자의 상품 판매와 관련된 다른 다양한 주체가 판매 관리 플랫폼일 수 있다. 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)는 판매자의 상품 주문 관리를 위한 플랫폼이다.OMS (210) is a platform for product order management of the seller.
OMS(210)는 다수의 판매 채널을 통해 상품을 판매하는 판매자가 일련의 판매 과정 업무를 통합적으로 처리할 수 있는 전산 시스템이다. 판매자는 OMS(210)를 통해 복수의 판매 채널 상에서 주문된 상품 현황을 확인하고 결제 확인, 배송, 주문 취소, 반품 등을 총괄 처리할 수 있다.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.
구체적으로 OMS(210) 상에서는 상품 일괄 등록 수정, 주문 수집, 송장 등록 및 송신, 재고 관리 등과 같은 기능이 제공될 수 있다. 또한, OMS(210)는 복수의 판매 채널 상에서의 결제 정보, 매출 정보, 매출에 대한 정산 정보, 반품 정보, 반품으로 인한 환불 정보, 재고 정보 등을 관리하기 위한 기능을 제공할 수 있다. 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.
ERP(220)는 전사적 자원 관리로서 판매자의 상품 생산(구매), 물류, 재무, 회계, 영업, 구매, 재고 등과 같은 정보를 관리하기 위한 판매 관리 플랫폼일 수 있다.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)는 창고 관리 시스템으로서 창고 또는 배송 센터 관리를 지원하고 최적화하기 위한 판매 관리 플랫폼이다. WMS(230)는 판매자의 상품의 입고, 적치, 재고, 피킹, 출고 등 물류 프로세서를 전체적으로 통합하여 관리할 수 있다. 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, stocking, stocking, picking, and shipping of the seller's products as a whole.
ECS(240)는 판매자의 판매를 위한 온라인 몰에 대한 생성 및 관리를 위한 판매 관리 플랫폼일 수 있다. ECS(240)는 온라인 쇼핑몰을 생성하고 온라인 쇼핑몰 상에서 발생되는 데이터를 관리하고, 상품의 판매를 위한 마켓팅을 수행하기 위해 구현될 수 있다. 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.
신용 평가 기초 데이터 수집부는 OMS(210), ERP(220), WMS(230), ECS(240)와 같은 판매 관리 플랫폼과 연계되어 신용 평가 기초 데이터를 수집할 수 있다. 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.
예를 들어, 신용 평가 기초 데이터 수집부는 OMS(210)에서 발생된 상품 등록 정보, 재고 정보, 주문 정보, 반품 정보, 결제 정보, 매출 정보, 정산 정보, 환불 정보 등을 판매자의 신용 평가 기초 데이터로서 수집할 수 있다.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
신용 평가 기초 데이터 수집부는 WMS(230)에서 발생된 상품 입고 정보, 상품 재고 정보, 상품 출고 정보, 상품 배송 정보 등을 판매자의 신용 평가 기초 데이터로서 수집할 수 있다.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.
신용 평가 기초 데이터 수집부는 ECS(240)에서 발생된 상품 마켓팅 정보 등을 판매자의 신용 평가 기초 데이터로서 수집할 수도 있다.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.
도 3은 본 발명의 실시예에 따른 신용 기초 데이터 전처리부의 동작을 나타낸 개념도이다.3 is a conceptual diagram illustrating the operation of a credit basic data pre-processing unit according to an embodiment of the present invention.
도 3에서는 신용 기초 데이터 전처리부에서 신용 평가 기초 데이터를 전처리하는 방법이 개시된다.3 discloses a method of preprocessing credit evaluation basic data in a credit basic data preprocessing unit.
도 3을 참조하면, 신용 평가 기초 데이터(300)는 제1 전처리(310)를 통해 제1 전처리 신용 평가 기초 데이터(320)로서 신용 평가 기초 데이터 학습부(360)로 전송될 수 있다. 또한, 신용 평가 기초 데이터(300)는 제2 전처리(350)를 통해 제2 전처리 신용 평가 기초 데이터(355)로서 신용 평가부(370)로 전송될 수 있다.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 .
제1 전처리(310)는 인공 지능 엔진에서 학습을 위한 전처리일 수 있다.The first pre-processing 310 may be pre-processing for learning in an artificial intelligence engine.
제1 전처리(310)는 신용 평가 기초 데이터(300)를 생성한 판매 관리 플랫폼의 특성을 고려하여 수행될 수 있다. 본 발명에서 금융 서비스는 판매자 특성, 서플라이 체인 특성을 고려하여 제공되기 때문에 판매자 특성, 서플라이 체인 특성을 고려한 인공 지능 엔진의 학습을 위해 제1 전처리(310)가 수행될 수 있다.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.
제1 전처리(310) 중 서플라이 체인 특성을 고려한 전처리는 신용 평가 기초 데이터(300)에 대응되는 서플라이 체인 단계를 고려하여 수행될 수 있다. 제1 전처리(310) 중 서플라이 체인 특성을 고려한 전처리는 제1 전처리(서플라이 체인)(313)이라는 용어로 표현될 수 있다.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 .
예를 들어, 서플라이 체인이 생산(또는 구매) 단계, 유통 단계, 판매 단계로 구분되는 경우, 신용 평가 기초 데이터(300)는 1차적으로 데이터가 획득된 단계를 기초로 신용 평가 기초 데이터(생산 단계), 신용 평가 기초 데이터(유통 단계), 신용 평가 기초 데이터(판매 단계)로 구분되어 제1 전처리 신용 평가 기초 데이터(320)로서 생성될 수 있다.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 .
또한, 제1 전처리(310) 중 판매자 특성을 고려한 전처리는 판매자 특성을 기초로 한 판매자 데이터 분류 및 판매자 데이터 증강(augmentation)을 통해 수행될 수 있다. 제1 전처리 중 판매자 특성을 고려한 전처리는 제1 전처리(판매자)(316)라는 용어로 표현될 수 있다.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 .
제2 전처리(350)는 신용 평가부에 포함되는 인공 지능 엔진을 기반으로 판매자의 신용 평가를 위해 수행될 수 있다. 제2 전처리 신용 평가 기초 데이터(355)는 인공 지능 엔진으로 입력되어 하위 신용 평가 요소를 결정하기 위해 사용될 수 있다. 따라서, 제2 전처리(350)는 인공 지능 엔진의 입력 데이터 포맷을 고려하여 수행될 수 있다. 인공 지능 엔진 별로 서로 다른 신용 평가 기초 데이터에 대한 예측이 수행되고, 인공 지능 엔진 별로 서로 다른 데이터 포맷을 가질 수 있다. 판매자의 신용 평가를 위해 사용될 수 있는 적어도 하나의 인공 지능 엔진을 고려하여 제2 전처리(350)가 수행될 수 있다.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.
도 4는 본 발명의 실시예에 따른 제1 전처리를 나타낸 개념도이다.4 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
도 4에서는 신용 평가 기초 데이터에 적용되는 제1 전처리(판매자) 및 제1 전처리(서플라이 체인)이 개시된다.In FIG. 4 , a first pre-processing (seller) and a first pre-processing (supply chain) applied to credit evaluation basic data are disclosed.
도 4를 참조하면, 제1 전처리(판매자)(400)를 통해 판매자 별로 분류된 신용 평가 기초 데이터는 제1 전처리(서플라이 체인)(450)을 통해 서플라이 체인 단계 별로 전처리되어 제1 전처리 신용 기초 데이터(490)로서 생성될 수 있다.Referring to FIG. 4 , credit evaluation basic data classified by seller through the first pre-processing (seller) 400 is pre-processed for each supply chain step through the first pre-processing (supply chain) 450, and the first pre-processed basic credit data (490).
제1 전처리(서플라이 체인)(450)은 데이터 전송 주체인 판매 관리 플랫폼 및 판매 관리 플랫폼에서 전송되는 데이터 포맷을 고려하여 제1 전처리 신용 기초 데이터(490)를 생성할 수 있다.The first pre-processing (supply chain) 450 may generate the first pre-processing credit 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.
판매 관리 플랫폼에 의해 관리되는 서플라이 체인 단계 및 판매 관리 플랫폼에서 발생되는 신용 평가 기초 데이터에 포함되는 정보를 고려한 제1 전처리(서플라이 체인)(450)을 통해 신용 평가 기초 데이터는 신용 평가 기초 데이터(생산)(460), 신용 평가 기초 데이터(유통)(470), 신용 평가 기초 데이터(판매)(480)로 구분되어 제1 전처리 신용 평가 기초 데이터로서 생성될 수 있다.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.
또한, 제1 전처리(서플라이 체인)(450)은 판매 관리 플랫폼을 통해 전송되는 신용 평가 기초 데이터에 대한 중복 데이터 처리를 수행하여 제1 전처리 신용 평가 기초 데이터(490)를 생성할 수 있다. 복수의 판매 관리 플랫폼에서 동일한 상품에 대한 신용 평가 기초 데이터가 중복하여 발생되는 경우, 중복 데이터 처리가 수행될 수 있다. 예를 들어, 판매자가 판매를 위해 특정 상품을 구매하는 경우, OMS 상에서 상품 등록이 되고, WMS 상에서는 상품 적치가 이루어질 수 있다. 즉, 판매자가 특정 물품을 구매하는 행위는 1회로 이루어지나 이러한 구매 행위로 인한 상품 등록 및 상품 적치에 대한 데이터는 판매 관리 플랫폼 별로 생성되고, 이로 인해 신용 평가 기초 데이터의 중복이 발생될 수 있다.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.
제1 전처리(서플라이 체인)(450)은 신용 평가 기초 데이터의 데이터 발생 시간, 신용 평가 기초 데이터에 포함된 정보, 추후 전송되는 신용 평가 기초 데이터 정보를 고려하여 전송된 신용 평가 기초 데이터의 중복성을 판단하여 제1 전처리 신용 평가 기초 데이터(490)를 생성할 수 있다. 신용 평가 기초 데이터의 중복이 발생하는 경우, 제1 전처리(서플라이 체인)(450)을 통해 하나의 판매 관리 플랫폼의 데이터만이 사용되거나, 중복된 신용 평가 기초 데이터를 필터링하여 제외하고, 중복된 신용 평가 기초 데이터에 포함되는 정보를 포함하는 다른 신용 평가 기초 데이터만이 사용되도록 할 수 있다.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.
또한, 제1 전처리(서플라이 체인)(450)은 시간을 고려한 신용 평가 기초 데이터에 대한 전처리일 수 있다.Also, the first pre-processing (supply chain) 450 may be pre-processing of credit evaluation basic data considering time.
판매자의 신용 등급 및 판매자의 신용 평가 기초 데이터는 시간에 따라 변화될 수 있다. 따라서, 학습을 위한 신용 평가 기초 데이터에 대한 시간 스케일 설정이 인공 지능 엔진의 성능에 영향을 크게 끼칠 수 있다. 따라서, 본 발명에서는 획득된 신용 평가 기초 데이터에 대한 시간 스케일을 설정한 후, 시간 스케일을 고려한 신용 평가 기초 데이터를 전처리하여 제1 전처리 신용 기초 데이터(490)를 생성할 수 있다. 전처리를 위한 시간 스케일은 신용 평가 기초 데이터 별로 설정될 수 있다. 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.
도 5는 본 발명의 실시예에 따른 제1 전처리(판매자)를 나타낸 개념도이다.5 is a conceptual diagram illustrating a first preprocessing (a seller) according to an embodiment of the present invention.
도 5에서는 제1 전처리(판매자)를 통한 인공 지능 엔진의 학습을 위한 신용 평가 기초 데이터의 증강 처리 방법이 개시된다. 특히, 데이터 증강 방법 중 신용 평가 기초 데이터가 하위 신용 평가 기초 데이터로 분할되어 학습 데이터로서 사용되는 방법이 개시된다.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.
도 5를 참조하면, 특정 신용 평가 기초 데이터(500)를 증강 처리하여 보다 정확한 인공 지능 엔진 학습을 수행하는 방법이 개시된다. 예를 들어, 신용 평가 기초 데이터(500)는 계절성, 거래 규모, 배송 주기, 매출 추이, 반품률, 판매 상품, 재고 자산 규모, 운영 정보 등일 수 있다. 신용 평가 기초 데이터(500) 중 특정 신용 평가 기초 데이터는 증강되어 복수의 하위 신용 평가 기초 데이터(540)로서 생성될 수 있다.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 .
반품율 데이터가 증가되는 경우, 반품 규모, 폐기율, 반품율 평균, 반품률 변동 안정성, 반품률 MAX 초과 횟수 등과 같은 데이터가 하위 신용 평가 기초 데이터(540)로서 생성될 수 있다.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 the change in the return rate, and the number of times the return rate exceeds the MAX may be generated as the lower credit evaluation basic data 540 .
본 발명의 실시예에서는 신용 평가를 보다 정확하게 하기 위해 데이터 증강이 필요한 경우, 제1 전처리를 통해 신용 평가 기초 데이터(500)를 하위 신용 평가 기초 데이터(540)로 증강하여 학습을 수행할 수 있다. 이러한 제1 전처리(판매자)의 데이터 증강은 하위 데이터 증강(520)이라는 용어로 표현될 수 있다.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 .
도 6은 본 발명의 실시예에 따른 제1 전처리를 나타낸 개념도이다.6 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
도 6에서는 제1 전처리(판매자) 상에서 인공 지능 엔진의 학습을 위한 신용 평가 기초 데이터의 증강 처리 방법이 개시된다. 특히, 데이터 증강 방법 중 신용 평가 기초 데이터를 시간 스케일로 분석하여 데이터를 증강하는 방법, 통계적인 방법을 통해 데이터를 증강하는 방법이 개시된다.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.
도 6의 (a)는 데이터 증강 방법 중 신용 평가 기초 데이터(600)를 시간 스케일로 분석하여 데이터를 증강하는 방법이다. 데이터 증강 방법 중 신용 평가 기초 데이터를 시간 스케일로 분석하여 데이터를 증강하는 방법은 시간 스케일 데이터 증강(610)이라는 용어로 표현될 수 있다.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 .
예를 들어, 신용 평가 기초 데이터(600)가 반품율인 경우, 월 반품률 5% 이상을 기준으로 36개월 간 월 반품율 5% 이상 판매자 수에 대한 데이터가 증강되어 생성될 수 있다. 또 다른 예로, 월 반품율 평균율 기준으로 36개월 간 평균 반품율 판매자 수에 대한 데이터가 증강되어 생성될 수 있다.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, based on the average monthly return rate, data on the number of sellers with an average return rate for 36 months may be augmented and generated.
도 6의 (b)는 데이터 증강 방법 중 신용 평가 기초 데이터(650)를 통계적으로 분석하여 데이터를 증강하는 방법이다. 데이터 증강 방법 중 신용 평가 기초 데이터(650)를 통계적으로 분석하여 데이터를 증강하는 방법은 통계적 데이터 증강(660)이라는 용어로 표현될 수 있다. 예를 들어, 신용 평가 기초 데이터(650)가 반품율인 경우, 고객별 반품율의 평균, 표준 편차, 최고, 특정 구간 이상 등 통계적 방법을 통해 다각도로 증가하여 데이터 증강이 수행될 수 있다.(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, if the credit evaluation basic data 650 is the 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.
도 7은 본 발명의 실시예에 따른 제1 전처리를 나타낸 개념도이다.7 is a conceptual diagram illustrating a first preprocessing according to an embodiment of the present invention.
도 7에서는 제1 전처리(판매자) 상에서 인공 지능 엔진의 학습을 위한 신용 평가 기초 데이터의 증강 처리 방법이 개시된다. 특히 데이터 증강 방법 중 신용 평가 기초 데이터를 2차원으로 분석하여 데이터를 증강하는 방법이 개시된다. 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.
도 7을 참조하면, 데이터 증강 방법 중 신용 평가 기초 데이터(700)를 2차원 데이터로서 증강하는 방법이 개시된다 데이터 증강 방법 중 신용 평가 기초 데이터(700)를 복수의 차원으로 분할하여 데이터를 증강하는 방법은 다차원 데이터 증강(710)이라는 용어로 표현될 수 있다.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 .
예를 들어, 신용 평가 기초 데이터(700)가 반품율인 경우, 전체 반품율이 2차원의 데이터로 분할되어 증가될 수 있다. 1차원은 36개월 간 평균 반품율이고, 2차원은 36개월 간 반품율이 5%를 넘은 개월 수일 수 있다. 이러한 2차원 분석을 통해 보다 정확한 판매자의 신용 평가 기초 데이터(700)에 대한 평가가 가능할 수 있다.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.
도 8은 본 발명의 실시예에 따른 제1 전처리를 수행하는 방법을 나타낸 개념도이다.8 is a conceptual diagram illustrating a method of performing a first preprocessing according to an embodiment of the present invention.
도 8에서는 신용 평가 기초 데이터 학습부의 학습을 위한 제1 전처리(판매자)를 선택적으로 수행하는 방법이 개시된다.8 discloses a method of selectively performing a first preprocessing (seller) for learning of a credit evaluation basic data learning unit.
도 8을 참조하면, 제1 전처리(판매자)는 하위 데이터 증강(810), 시간 스케일 데이터 증강(820), 통계적 데이터 증강(830), 다차원 데이터 증강(840)을 사용할 수 있다.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.
신용 평가 기초 데이터 학습부에 포함되는 복수의 인공 지능 엔진에 대한 학습을 위해 제1 전처리(판매자)가 선택적으로 수행될 수 있다.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.
예를 들어, 하위 데이터 증강(810)은 인공 지능 엔진 중 특성 신용 평가 데이터에 대한 구체적인 분석을 통해 특화된 결과를 생성하기 위한 인공 지능 엔진의 학습을 위해 사용될 수 있다. 예를 들어, 반품율에 보다 가중치를 가지고 신용 평가 데이터를 생성하는 인공 지능 엔진의 학습을 위해서 반품율에 대한 하위 데이터 증강이 수행될 수 있다.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 for 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.
시간 스케일 데이터 증강(820)은 시간에 따른 신용 평가 데이터의 변화를 예측하기 위한 인공 지능 엔진의 학습을 위해 사용될 수 있다. Time scale data augmentation 820 can be used to train an artificial intelligence engine to predict changes in credit rating data over time.
통계적 데이터 증강(830)은 특정 기준을 별도로 미리 설정하고, 설정 기준에 따른 신용 평가 데이터를 예측하기 위한 인공 지능 엔진의 학습을 위해 사용될 수 있다. 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.
다차원 데이터 증강(840)은 2개의 차원에 대한 설정 기준을 기반으로 신용 평가 데이터를 예측하기 위한 인공 지능 엔진의 학습을 위해 사용될 수 있다.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.
본 발명의 실시예에서는 예측되는 신용 평가 데이터의 성질에 따라 제1 전처리(판매자)가 다양하게 수행되고 다양한 인공 지능 모델이 생성될 수 있다. 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.
도 9는 본 발명의 실시예에 따른 신용 평가부의 동작을 나타낸 개념도이다.9 is a conceptual diagram illustrating the operation of a credit evaluation unit according to an embodiment of the present invention.
도 9에서는 신용 평가부에서 인공 지능 엔진을 기반으로 판매자의 신용 평가 데이터를 생성하는 방법이 개시된다.9 discloses a method of generating credit evaluation data of a seller based on an artificial intelligence engine in a credit evaluation unit.
도 9를 참조하면, 신용 평가부는 적어도 하나의 인공 지능 엔진을 기반으로 한 판매자의 신용 평가를 통해 신용 평가 데이터를 생성할 수 있다.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.
신용 평가부는 하나의 인공 지능 엔진을 기반으로 판매자의 신용 평가 데이터를 생성할 수도 있으나, 신용 평가부는 판매자 특성 정보(900)를 기반으로 적응적으로 판매자에게 적용 가능한 인공 지능 엔진을 결정하고, 결정된 인공 지능 엔진을 기반으로 신용 평가 데이터(950)를 생성할 수 있다.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.
예를 들어, 판매자의 판매 상품, 판매자의 상품 판매 플랫폼, 판매자의 매출, 판매자의 순이익 등과 같은 판매자 정보를 기반으로 판매자 특성 정보(900)에 가장 적합한 신용 평가를 위한 타겟 인공 지능 엔진(920)이 결정될 수 있다.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
신용 평가부는 복수의 인공 지능 엔진 각각에 피드백 정보를 기반으로 판매자 특성 정보에 따른 인공 지능 엔진의 신뢰도를 결정할 수 있다. 또한, 신용 평가부는 복수의 인공 지능 엔진 각각에 대해 판매자 특성 정보별 신뢰도 등급을 결정할 수 있다. 구체적으로 판매자 특성 정보는 하위 판매자 특성 정보 각각을 기반으로 벡터화되고 공간 상에 표현될 수 있고, 공간 간의 거리 정보를 통해 판매자 특성 정보를 기반으로 판매자 그룹이 형성될 수 있고, 판매자 그룹별 신용 평가 데이터와 금융 서비스 결과 데이터를 비교하여 인공 지능 엔진의 판매자 그룹별 신뢰도가 결정될 수 있다. 신뢰도 등급은 인공 지능 엔진별 판매자 그룹에 대한 신뢰도의 통계적 특성을 고려하여 결정될 수 있다.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.
신용 평가부는 판매자 특성 정보를 기초로 상대적으로 높은 신뢰도 등급을 가지는 인공 지능 엔진을 타겟 인공 지능 엔진(920)으로 결정하여 판매자에 대한 신용 평가 데이터(950)를 생성할 수 있다.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.
이상 설명된 본 발명에 따른 실시예는 다양한 컴퓨터 구성요소를 통하여 실행될 수 있는 프로그램 명령어의 형태로 구현되어 컴퓨터 판독 가능한 기록 매체에 기록될 수 있다. 상기 컴퓨터 판독 가능한 기록 매체는 프로그램 명령어, 데이터 파일, 데이터 구조 등을 단독으로 또는 조합하여 포함할 수 있다. 상기 컴퓨터 판독 가능한 기록 매체에 기록되는 프로그램 명령어는 본 발명을 위하여 특별히 설계되고 구성된 것이거나 컴퓨터 소프트웨어 분야의 당업자에게 공지되어 사용 가능한 것일 수 있다. 컴퓨터 판독 가능한 기록 매체의 예에는, 하드 디스크, 플로피 디스크 및 자기 테이프와 같은 자기 매체, CD-ROM 및 DVD와 같은 광기록 매체, 플롭티컬 디스크(floptical disk)와 같은 자기-광 매체(magneto-optical medium), 및 ROM, RAM, 플래시 메모리 등과 같은, 프로그램 명령어를 저장하고 실행하도록 특별히 구성된 하드웨어 장치가 포함된다. 프로그램 명령어의 예에는, 컴파일러에 의하여 만들어지는 것과 같은 기계어 코드뿐만 아니라 인터프리터 등을 사용하여 컴퓨터에 의해서 실행될 수 있는 고급 언어 코드도 포함된다. 하드웨어 장치는 본 발명에 따른 처리를 수행하기 위하여 하나 이상의 소프트웨어 모듈로 변경될 수 있으며, 그 역도 마찬가지이다.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.
이상에서 본 발명이 구체적인 구성요소 등과 같은 특정 사항과 한정된 실시예 및 도면에 의하여 설명되었으나, 이는 본 발명의 보다 전반적인 이해를 돕기 위하여 제공된 것일 뿐, 본 발명이 상기 실시예에 한정되는 것은 아니며, 본 발명이 속하는 기술분야에서 통상적인 지식을 가진 자라면 이러한 기재로부터 다양한 수정과 변경을 꾀할 수 있다.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.
따라서, 본 발명의 사상은 상기 설명된 실시예에 국한되어 정해져서는 아니 되며, 후술하는 특허청구범위뿐만 아니라 이 특허청구범위와 균등한 또는 이로부터 등가적으로 변경된 모든 범위는 본 발명의 사상의 범주에 속한다고 할 것이다.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 (6)

  1. 판매자에 대한 신용 평가 방법은, How to evaluate the seller's credit,
    신용 평가 기초 데이터 수집부가 판매자의 신용 평가를 위한 신용 평가 기초 데이터를 수집하는 단계;collecting basic credit evaluation data for credit evaluation of the seller by a credit evaluation basic data collection unit;
    신용 평가 기초 데이터 전처리부가 수집된 상기 신용 평가 기초 데이터를 전처리하는 단계;pre-processing the collected credit evaluation basic data by a credit evaluation basic data pre-processing unit;
    신용 평가 기초 데이터 학습부가 전처리된 상기 신용 평가 기초 데이터를 기반으로 상기 판매자의 상기 신용 평가를 위한 인공 지능 엔진을 생성하는 단계; 및generating 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
    신용 평가부가 상기 인공 지능 엔진을 기반으로 상기 판매자의 신용 평가 데이터를 결정하는 단계를 포함하는 것을 특징을 하는 방법.and determining, by a credit evaluation unit, credit evaluation data of the seller based on the artificial intelligence engine.
  2. 제1항에 있어서, According to claim 1,
    상기 신용 평가 기초 데이터는 다양한 판매 관리 플랫폼을 통해 수집되고, The credit evaluation basic data is collected through various sales management platforms,
    상기 판매 관리 플랫폼은 OMS(order management system), ERP(enterprise resource planning), WMS(warehouse management system), ECS(E-commerce solution) 중 적어도 하나를 포함하고, The sales management platform includes at least one of an order management system (OMS), enterprise resource planning (ERP), warehouse management system (WMS), and E-commerce solution (ECS),
    상기 신용 평가 기초 데이터 수집부는 상기 OMS에서 발생된 상품 등록 정보, 재고 정보, 주문 정보, 반품 정보, 결제 정보, 매출 정보, 정산 정보, 환불 정보 중 적어도 하나의 정보를 상기 판매자의 상기 신용 평가 기초 데이터로서 수집하고,The credit evaluation basic data collection unit converts at least one of product registration information, stock information, order information, return information, payment information, sales information, settlement information, and refund information generated from the OMS into the credit evaluation basic data of the seller. collect as
    상기 신용 평가 기초 데이터 수집부는 상기 WMS에서 발생된 상품 입고 정보, 상품 재고 정보, 상품 출고 정보, 상품 배송 정보 중 적어도 하나의 정보를 상기 판매자의 상기 신용 평가 기초 데이터로서 수집하는 것을 특징으로 하는 방법.Wherein the credit evaluation basic data collection unit collects at least one of product storage information, product stock information, product release information, and product delivery information generated by the WMS as the credit evaluation basic data of the seller.
  3. 제2 항에 있어서,According to claim 2,
    상기 신용 평가 기초 데이터 전처리부는 제1 전처리 및 제2 전처리를 수행하고, The credit evaluation basic data pre-processing unit performs first pre-processing and second pre-processing,
    상기 제1 전처리는 상기 신용 평가 기초 데이터를 전처리한 제1 전처리 신용 평가 기초 데이터를 상기 신용 평가 기초 데이터 학습부로 전송하기 위해 수행되고,The first pre-processing is performed to transmit the first pre-processed credit evaluation basic data obtained by preprocessing the credit evaluation basic data to the credit evaluation basic data learning unit;
    상기 제2 전처리는 상기 신용 평가 기초 데이터를 전처리한 제2 전처리 신용 평가 기초 데이터를 상기 신용 평가부로 전송하기 위해 수행되고,The second pre-processing is performed to transmit second pre-processed credit evaluation basic data obtained by preprocessing the credit evaluation basic data to the credit evaluation unit;
    상기 제1 전처리는 제1 전처리(서플라이 체인) 및 제1 전처리(판매자)를 포함하고, The first pre-processing includes a first pre-processing (supply chain) and a first pre-processing (seller);
    상기 제1 전처리(서플라이 체인)는 서플라이 체인 단계별로 상기 신용 평가 기초 데이터를 전처리한 신용 평가 기초 데이터(생산 단계), 신용 평가 기초 데이터(유통 단계), 신용 평가 기초 데이터(판매 단계)를 상기 제1 전처리 신용 평가 기초 데이터로서 생성하고,The first pre-processing (supply chain) converts the basic credit rating data (production stage), the basic credit rating data (distribution stage), and the basic credit rating data (sale stage) obtained by pre-processing the basic credit rating data for each supply chain step into the first pre-processing unit. 1 Created as pre-processing credit evaluation basic data,
    상기 제1 전처리(판매자)는 판매자 특성을 고려한 전처리는 상기 신용 평가 기초 데이터를 판매자 특성을 기초로 한 판매자 데이터 분류 및 판매자 데이터 증강(augmentation)을 통해 처리하여 상기 제1 전처리 신용 평가 기초 데이터를 생성하는 것을 특징으로 하는 방법.The first pre-processing (seller) generates the first pre-processing credit rating basic data by processing the credit evaluation basic data through seller data classification and seller data augmentation based on the seller characteristic. A method characterized by doing.
  4. 판매자에 대한 신용 평가를 수행하는 신용 평가 장치는,A credit evaluation device that performs a credit evaluation on a seller,
    판매자의 신용 평가를 위한 신용 평가 기초 데이터를 수집하도록 구현되는 신용 평가 기초 데이터 수집부;a credit evaluation basic data collection unit implemented to collect credit evaluation basic data for credit evaluation of the seller;
    수집된 상기 신용 평가 기초 데이터를 전처리하도록 구현되는 신용 평가 기초 데이터 전처리부;a credit evaluation basic data pre-processing unit configured to pre-process the collected credit evaluation basic data;
    전처리된 상기 신용 평가 기초 데이터를 기반으로 상기 판매자의 상기 신용 평가를 위한 인공 지능 엔진을 생성하도록 구현되는 신용 평가 기초 데이터 학습부; 및a 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
    상기 인공 지능 엔진을 기반으로 상기 판매자의 신용 평가 데이터를 결정하도록 구현되는 신용 평가부를 포함하는 것을 특징을 하는 신용 평가 장치.and a credit evaluation unit configured to determine credit evaluation data of the seller based on the artificial intelligence engine.
  5. 제4항에 있어서,According to claim 4,
    상기 신용 평가 기초 데이터는 다양한 판매 관리 플랫폼을 통해 수집되고, The credit evaluation basic data is collected through various sales management platforms,
    상기 판매 관리 플랫폼은 OMS(order management system), ERP(enterprise resource planning), WMS(warehouse management system), ECS(E-commerce solution) 중 적어도 하나를 포함하고, The sales management platform includes at least one of an order management system (OMS), enterprise resource planning (ERP), warehouse management system (WMS), and E-commerce solution (ECS),
    상기 신용 평가 기초 데이터 수집부는 상기 OMS에서 발생된 상품 등록 정보, 재고 정보, 주문 정보, 반품 정보, 결제 정보, 매출 정보, 정산 정보, 환불 정보 중 적어도 하나의 정보를 상기 판매자의 상기 신용 평가 기초 데이터로서 수집하고,The credit evaluation basic data collection unit converts at least one of product registration information, stock information, order information, return information, payment information, sales information, settlement information, and refund information generated from the OMS into the credit evaluation basic data of the seller. collect as
    상기 신용 평가 기초 데이터 수집부는 상기 WMS에서 발생된 상품 입고 정보, 상품 재고 정보, 상품 출고 정보, 상품 배송 정보 중 적어도 하나의 정보를 상기 판매자의 상기 신용 평가 기초 데이터로서 수집하는 것을 특징으로 하는 신용 평가 장치.The credit rating base data collection unit collects at least one of product storage information, product stock information, product release information, and product delivery information generated by the WMS as the credit rating base data of the seller. Device.
  6. 제5항에 있어서,According to claim 5,
    상기 신용 평가 기초 데이터 전처리부는 제1 전처리 및 제2 전처리를 수행하고, The credit evaluation basic data pre-processing unit performs first pre-processing and second pre-processing;
    상기 제1 전처리는 상기 신용 평가 기초 데이터를 전처리한 제1 전처리 신용 평가 기초 데이터를 상기 신용 평가 기초 데이터 학습부로 전송하기 위해 수행되고,The first pre-processing is performed to transmit the first pre-processed credit evaluation basic data obtained by preprocessing the credit evaluation basic data to the credit evaluation basic data learning unit;
    상기 제2 전처리는 상기 신용 평가 기초 데이터를 전처리한 제2 전처리 신용 평가 기초 데이터를 상기 신용 평가부로 전송하기 위해 수행되고,The second pre-processing is performed to transmit the second pre-processed credit evaluation basic data obtained by preprocessing the credit evaluation basic data to the credit evaluation unit;
    상기 제1 전처리는 제1 전처리(서플라이 체인) 및 제1 전처리(판매자)를 포함하고, The first pre-processing includes a first pre-processing (supply chain) and a first pre-processing (seller),
    상기 제1 전처리(서플라이 체인)는 서플라이 체인 단계별로 상기 신용 평가 기초 데이터를 전처리한 신용 평가 기초 데이터(생산 단계), 신용 평가 기초 데이터(유통 단계), 신용 평가 기초 데이터(판매 단계)를 상기 제1 전처리 신용 평가 기초 데이터로서 생성하고,The first pre-processing (supply chain) converts the basic credit rating data (production stage), the basic credit rating data (distribution stage), and the basic credit rating data (sale stage) obtained by pre-processing the basic credit rating data for each supply chain step to the first preprocessing step. 1 Created as pre-processing credit evaluation basic data,
    상기 제1 전처리(판매자)는 판매자 특성을 고려한 전처리는 상기 신용 평가 기초 데이터를 판매자 특성을 기초로 한 판매자 데이터 분류 및 판매자 데이터 증강(augmentation)을 통해 처리하여 상기 제1 전처리 신용 평가 기초 데이터를 생성하는 것을 특징으로 하는 신용 평가 장치.The first pre-processing (seller) generates the first pre-processing credit rating basic data by processing the credit evaluation basic data through seller data classification and seller data augmentation based on the seller characteristic. A credit evaluation device characterized in that for doing.
PCT/KR2022/020814 2022-01-12 2022-12-20 Credit rating method based on data collected by ecommerce sales and distribution management system platform and device for performing same WO2023136491A1 (en)

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