Real-time credit rating and inventory collateral loan automatic review system based on logistics performance data

Abstract

A real-time credit evaluation and automated inventory-backed loan screening system based on logistics performance data is initiated. The real-time credit evaluation and automated inventory-backed loan screening system based on logistics performance data according to an embodiment of the present invention comprises: a data collection unit that collects incoming volume, incoming unit price, outgoing volume, selling unit price, inventory quantity, and inventory turnover rate in real time from a logistics center WMS; a data processing unit that calculates daily estimated sales revenue, cash conversion cycle, and inventory health indicators from the collected data; a credit evaluation unit that calculates a credit rating by summing the scores of six evaluation items—sales stability, profitability, inventory health, cash flow, business diversification, and growth potential—based on the calculated indicators; an inventory evaluation unit that selects inventory with a holding period of 90 days or less and a turnover rate of 80% or more of the industry average, and calculates the collateral value by applying a discount rate to the lower of the purchase price and market price of the selected inventory; and a loan screening unit that automatically calculates a loan limit based on the calculated credit rating and collateral value. The gist of the configuration is to include a risk monitoring unit that detects abnormal fluctuations in shipment volume, inventory volume, and margin rate in real time and generates an alarm after the loan is executed.

Description

Technology Field

[0001] The present invention relates to a real-time credit evaluation and automated inventory collateral loan screening system, and more specifically, to a logistics performance data-based real-time credit evaluation and automated inventory collateral loan screening system capable of automating credit loan and inventory collateral loan screening by financial institutions by collecting and analyzing logistics performance data of shippers in e-commerce logistics centers in real time. Background Technology

[0002] In the technical field to which this invention belongs, the screening of credit loans and inventory-backed loans intended to support financing for small and medium-sized e-commerce companies has primarily relied on traditional financial statement-centered methods, and most financial institutions have assessed the credit status of companies based on financial data submitted on a quarterly or annual basis. However, this method has a structural limitation in that it cannot reflect actual business conditions in real time, leading to the problem that accurate credit assessment is difficult in the e-commerce industry, which is characterized by rapid sales fluctuations and seasonal factors. In particular, despite the frequent occurrence of rapid surges or declines in sales over short cycles in the online sales environment, financial institutions have been unable to detect these changes in a timely manner due to the time lag nature of financial statements, resulting in reduced accuracy in credit evaluations.

[0003] In conventional technology, inventory valuation for collateral purposes was also conducted based on physical inspection, making it essential for personnel to visit warehouses and directly verify the quantity and condition of goods. This inspection method often took anywhere from a minimum of two weeks to as long as four weeks, and the significant costs associated with personnel and external services placed a burden on both financial institutions and companies. Furthermore, inventory fluctuations occurring after the inspection could not be reflected immediately; consequently, there was a problem in that financial institutions could not detect risk factors, such as a sharp decline in inventory or an increase in defect rates, in real time even if they occurred immediately after the inspection.

[0004] Another problem with conventional technology was that the assessment of collateral eligibility did not adequately consider the distribution characteristics of inventory assets. Since e-commerce products have short turnover cycles and rapid new product launch cycles, the stability of actual collateral value can vary significantly even for inventory of the same value; however, existing methods often calculated collateral value by simply applying the total inventory amount or a fixed-rate discount method. This approach failed to reflect actual risk factors such as seasonality, changes in profit margins, and market price declines, leading to errors of overvaluation or undervaluation, which increased the likelihood of loan default.

[0005] Furthermore, existing credit rating systems had a structural problem in that it was difficult to calculate key management indicators, such as sales stability, profitability, inventory health, cash flow, and business diversification, in real time. Although e-commerce companies have diverse sales channels and factors such as settlement cycles (D+N), return rates, and the impact of promotions interact complexly across each channel, existing systems lacked the functionality to automatically integrate and refine this data for evaluation. As a result, financial institutions performed evaluations based on generalized standards without sufficiently considering the specific operational characteristics of each company, which frequently led to discrepancies between actual creditworthiness and evaluation results.

[0006] Existing technologies also lacked the capability to calculate key indicators, such as the Cash Conversion Cycle (CCC), in real time. Although the Inventory Holding Period (DIO), Accounts Receivable Collection Period (DSO), and Accounts Payable Payment Period (DPO) that constitute the CCC fluctuate constantly, conventional methods could only verify them based on periodic reports. Consequently, it was difficult to identify signs of short-term cash flow deterioration in a timely manner. This became a particularly serious problem for companies with high inventory turnover rates; even if cash flow deteriorated due to temporary inventory increases or a surge in purchases, financial institutions only recognized this retrospectively, making early risk detection difficult.

[0007] Meanwhile, conventional systems had very limited post-loan risk monitoring capabilities. Even when major risk signals—such as a sharp drop in shipments, declining profit margins, increased long-term inventory, or delayed settlements—occurred after a loan was executed, existing methods relied on manual monitoring or periodic checks, making immediate response difficult. Consequently, there were numerous cases where this led to a deterioration in receivables quality and an increase in default rates. In particular, since e-commerce companies face significant sales volatility due to external factors, real-time data-based monitoring is essential; however, conventional technology failed to support this.

[0008] Existing collateral valuation methods also had the problem of being unable to reflect fluctuations in the market price of inventory. E-commerce products often experience rapid price drops, and factors such as prices of external competitors, platform-specific discount policies, and the timing of new product launches all influence market prices. However, conventional technology treated market prices as static data or calculated them using simple average prices, leading to the problem of overestimating the actual collateral value. This resulted in loan amounts being set excessively high relative to the collateral value, acting as a factor that increased risk for financial institutions.

[0009] Furthermore, conventional technology failed to reflect quality-related factors, such as return rates, defect rates, and product condition, in collateral valuation. In the e-commerce industry, return rates vary significantly by category, and even identical products differ greatly in value depending on their condition grade; however, existing systems lacked the functionality to incorporate this quality information. This resulted in the inability to properly exclude inventory with a high risk of value depreciation, even if such items were included in the collateral stock.

[0010] Conventional credit evaluation and collateral assessment systems also had the problem of lacking evaluation capabilities based on predictive models. Although e-commerce sales fluctuate significantly depending on the day of the week, seasonality, promotional policies, and special events, existing methods often relied on evaluations based only on past average sales or the experience of the personnel in charge. For instance, there were cases where excessive loans were approved because the possibility of a sharp decline in sales over the next 30 days was not predicted, or conversely, where an appropriate loan amount was not determined due to conservative evaluations despite growth potential.

[0011] Finally, conventional technologies suffered from low levels of data integration and automation, resulting in a significant reliance on manual work throughout the loan screening process. Many steps, from data collection, cleaning, analysis, and evaluation to report writing and approval, relied on manual labor by personnel, leading to prolonged processing times and a high risk of errors. This inefficiency caused delays in financing for small and medium-sized e-commerce companies, sometimes resulting in the loss of growth opportunities.

[0012] In order to solve the problems of the conventional technology described above, the present invention aims to fundamentally improve the limitations of the conventional technology by providing a system that automates the credit evaluation, inventory evaluation, and loan screening processes based on real-time logistics performance data generated in a logistics center, and enables real-time monitoring of collateral inventory risk factors. Prior art literature

[0013] Korean Patent Publication No. 10-2812894 (Registration Date: May 21, 2025) The problem to be solved

[0014] This invention aims to resolve the problems inherent in conventional loan assessments for e-commerce companies, which fail to reflect real-time business conditions due to a reliance on financial statement-centric evaluations, prolong the review period due to dependence on physical inventory audits, and fail to adequately reflect the characteristics of the e-commerce industry, such as inventory turnover, seasonality, and market price fluctuations. Furthermore, it seeks to overcome the limitations of increased default rates resulting from the failure to detect risk signals—such as sharp declines in shipments, surges in inventory, and falling profit margins—in a timely manner after loan execution. The objective is to provide a system that automates credit evaluation, collateral evaluation, and loan assessment by utilizing real-time logistics performance data, thereby enabling the immediate identification of risk factors. means of solving the problem

[0015] A system for real-time credit evaluation and automated inventory-backed loan assessment based on logistics performance data according to one aspect of the present invention for achieving the above purpose is a system that collects data on shippers of a logistics center and performs loan assessment, comprising: a data collection unit that collects incoming volume, incoming unit price, outgoing volume, selling unit price, inventory quantity, and inventory turnover rate in real time from the logistics center WMS; a data processing unit that calculates daily estimated sales revenue, cash conversion cycle, and inventory health indicators from the collected data; a credit evaluation unit that calculates a credit rating by summing the scores of six evaluation items—sales stability, profitability, inventory health, cash flow, business diversification, and growth potential—based on the calculated indicators; an inventory evaluation unit that selects inventory with a holding period of 90 days or less and a turnover rate of 80% or more of the industry average, and calculates the collateral value by applying a discount rate to the lower of the purchase price and market price of the selected inventory; and a loan assessment unit that automatically calculates a loan limit based on the calculated credit rating and collateral value. The configuration may include a risk monitoring unit that detects abnormal fluctuations in shipment volume, inventory volume, and margin rate in real time and generates an alarm after the loan is executed.

[0016] In one embodiment of the present invention, the data collected by the data collection unit comprises: receiving data including receiving volume (by SKU, by day), receiving unit price (purchase cost), receiving cycle (average / standard deviation), number of suppliers, supplier concentration, and payment conditions (cash / credit, payment deadline); outgoing data including outgoing volume (by SKU, by day, by channel), selling unit price (actual selling price by channel), outgoing cycle (order frequency, seasonality), order cancellation rate, and return rate (by product, by reason); inventory asset data including inventory quantity and total amount (based on purchase price) by product, inventory turnover rate (overall / by product group), average inventory holding period (DIO), long-term inventory (90 days / 180 days or more) ratio, and inventory obsolescence (days elapsed from receiving date); and sales channel data including the number of sales channels, sales share by sales channel, settlement cycle by channel (D+N), new channel entry history, and channel concentration (proportion of top channels). The configuration may include: profitability data including sales margin rate (selling price - purchase price), average margin rate, average margin trend, logistics cost ratio (relative to sales), and net profit margin estimates by product; cash flow data including daily estimated sales (shipping volume × selling price), daily purchase amount (incoming volume × purchase price), scheduled settlement date by channel, estimated settlement amount by channel, and cash conversion cycle (CCC = DIO + DSO - DPO); and product market data including product category, product lifecycle, seasonality index, market selling price trend (external crawling), and similar product price benchmark.

[0017] In one embodiment of the present invention, the credit rating unit comprises: a sales stability evaluation value of 25% based on data related to shipment volume growth rate, shipment volume coefficient of variation, and seasonally adjusted sales trend; a profitability evaluation value of 20% based on data related to average margin rate, average margin rate trend, and whether margin rate > industry average; an inventory health evaluation value of 20% based on data related to the value obtained by dividing the inventory turnover rate by the industry average inventory turnover rate and the long-term inventory ratio; a cash flow evaluation value of 20% based on data related to 30-day net cash flow forecast, with a higher evaluation value assigned as the cash conversion cycle (CCC) is shorter; a business diversification evaluation value of 10% based on data related to the number of sales channels and product category diversity; and a growth potential evaluation value of 5% based on data related to the growth rate compared to the previous month and new channel expansion. By combining them, the credit rating can be determined in the order of AAA (90-100) / AA (80-89) / A (70-79) / BBB (60-69) / BB (50-59) / B (less than 50).

[0018] In one embodiment of the present invention, the credit rating unit can predict the daily shipment volume for the next 30 days from the shipment volume data of the past 90 days using an LSTM time series model, and calculate the expected sales revenue by multiplying the predicted shipment volume by the sales unit price.

[0019] In one embodiment of the present invention, the inventory valuation unit may differentially apply a coefficient ranging from 0.7 to 1.1 depending on the inventory turnover rate, seasonality, and inventory holding period when calculating the collateral value of the inventory.

[0020] The present invention may provide a logistics data-based loan assessment method utilizing a real-time credit evaluation and inventory collateral loan automatic assessment system based on the logistics performance data. A logistics data-based loan assessment method according to one aspect of the present invention may comprise: a data collection step for collecting incoming and outgoing data and inventory data of a shipper in real time; a cash conversion cycle calculation step for calculating a cash conversion cycle (CCC) by adding the accounts receivable collection period (DSO) to the inventory holding period (DIO) and subtracting the accounts payable payment period (DPO) based on the collected data; a credit rating determination step for calculating a credit score and determining a credit rating by weighted summing of six evaluation items; a collateral value calculation step for selecting inventory that satisfies collateral eligibility conditions among the inventory and calculating the collateral value; a loan limit calculation step for calculating a loan limit by applying differential LTV according to credit rating; and an alarm output step for outputting an alarm signal when an event occurs such as a sharp decrease in outgoing volume, a sharp increase in inventory, or a decrease in margin rate after the loan is executed. Effects of the invention

[0021] According to the present invention, by utilizing real-time inbound, outbound, and inventory data based on a logistics center WMS, a company's operating cash flow can be estimated immediately, and credit ratings can be dynamically calculated based on six items, including sales stability, profitability, inventory soundness, and cash flow. Furthermore, by automatically selecting collateral-eligible inventory and enabling sophisticated collateral value calculation reflecting turnover coefficients, seasonality coefficients, and obsolescence coefficients, the accuracy of inventory collateral valuation and risk response capabilities are significantly improved. In addition, loan limits can be automatically calculated by applying LTV (Loan-to-Value) ratios according to credit ratings, thereby drastically reducing the review processing time to 24 to 48 hours. Moreover, by detecting risk events in real time—such as a sharp decline in shipment volume, a surge in inventory, an increase in the ratio of long-term inventory, a decline in profit margins, and settlement delays—credit or collateral revaluation can be performed immediately, contributing to a reduction in default rates and an improvement in receivables soundness. Consequently, it provides various benefits, including reduced physical inspection costs, expanded loan accessibility, and mitigation of risks for financial institutions. Brief explanation of the drawing

[0022] FIG. 1 is a block diagram showing a real-time credit evaluation and inventory collateral loan automatic screening system based on logistics performance data according to one embodiment of the present invention. FIG. 2 is a flowchart illustrating a logistics data-based loan screening method according to one embodiment of the present invention. Specific details for implementing the invention

[0023] Preferred embodiments of the present invention will be described in detail below with reference to the drawings. Prior to this, terms and words used in this specification and claims should not be interpreted as being limited to their ordinary or dictionary meanings, but should be interpreted in a meaning and concept consistent with the technical spirit of the present invention.

[0024] Throughout this specification, when it is stated that one component is located "on" another component, this includes not only cases where one component is in contact with another component, but also cases where another component exists between the two components. Throughout this specification, when it is stated that a part "includes" a component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0025] FIG. 1 shows a block diagram illustrating a real-time credit evaluation and inventory collateral loan automatic screening system based on logistics performance data according to one embodiment of the present invention, and FIG. 2 shows a flowchart illustrating a loan screening method based on logistics data according to one embodiment of the present invention.

[0026] Referring to these drawings, the real-time credit evaluation and inventory collateral loan automatic screening system (100) based on logistics performance data according to the present embodiment is equipped with a data collection unit (110), a data processing unit (120), a credit evaluation unit (130), an inventory evaluation unit (140), a loan screening unit (150), and a risk monitoring unit (160) that perform specific roles, thereby utilizing real-time logistics performance data of a shipper generated at a logistics center to estimate operating cash flow in real time and dynamically evaluate creditworthiness, automatically calculate inventory asset value and determine collateral eligibility in real time, shorten the loan approval time to 24 to 48 hours, and provide a system capable of monitoring the risk level of collateral inventory in real time.

[0027] Hereinafter, with reference to FIGS. 1 and 2, each component constituting the real-time credit evaluation and inventory collateral loan automatic screening system (100) based on logistics performance data according to the present embodiment will be described in detail.

[0028] Detailed description of the data collection unit (110)

[0029] The data collection unit (110) is the most fundamental component of the real-time credit evaluation and inventory collateral loan automatic screening system based on logistics performance data, and performs the role of collecting all logistics-related data from shippers generated during the operation of the logistics center in real-time or near-real-time. The data collection unit (110) is directly linked with the logistics center's WMS, OMS, and settlement system, and automatically collects information on incoming, outgoing, inventory changes, and sales channel settlements, thereby resolving the limitations of the conventional method that relied on manual input or post-reporting.

[0030] The data collection unit (110) goes beyond simple data aggregation functions to sort data of different formats and cycles by time and structure it by shipper unit, product unit, and SKU unit. In this process, the data collection unit (110) primarily verifies missing data, duplicate data, abnormal values, etc., thereby providing a foundation for the subsequent data processing unit (120) and credit evaluation unit (130) to utilize reliable data. Accordingly, the stability and repeatability of the credit evaluation results are secured.

[0031] The data collection unit (110) may adopt an event-based collection structure to ensure real-time performance. For example, it is configured so that the corresponding data is collected immediately upon the occurrence of logistics events such as the completion of receiving, confirmation of shipment, or receipt of returns, thereby allowing changes in inventory levels or sales occurrences to be reflected in the system without delay. Through this, cash flow estimation and collateral value calculation can be performed based on current data rather than past data.

[0032] Additionally, the data collection unit (110) may include an external data linkage function. External crawling or API-based data, such as product market data, external market price information, and price benchmarks for similar products, are also collected in an integrated manner along with internal logistics data through the data collection unit (110). This expanded structure contributes to increasing the accuracy of the market value assessment of inventory assets and the calculation of collateral risk.

[0033] Consequently, the data collection unit (110) acts as a key component that determines the reliability and level of automation of the entire system of the present invention, and serves as a starting point for converting complex and large-scale data generated at the logistics site into a form usable for financial evaluation. Through this, a real-time credit evaluation and collateral evaluation system, which was impossible in conventional technology, becomes possible.

[0034] Detailed explanation of the receiving data (111)

[0035] Incoming data (111) includes information generated during the process in which a shipper purchases goods from a supplier and brings them into a logistics center, and is an important set of data that defines the source nature of the inventory assets. Incoming data (111) is used as basic data to determine the cost structure and asset stability of the inventory assets based on the daily incoming quantity and purchase unit price by SKU.

[0036] Incoming data (111) includes not only simple quantity information but also incoming cycles and variability. The average incoming cycle and standard deviation information are used as indicators of supply chain stability, and if there is a high dependency on a specific supplier for incoming goods or if the incoming cycle is irregular, it is reflected as a potential risk factor. This enables the quantitative evaluation of supply chain risks that were not previously considered in credit evaluations.

[0037] The receiving data (111) includes information on the number of suppliers and supplier concentration, which can determine whether there is reliance on a single supplier. The more diversified the supplier structure, the lower the risk of receiving interruption can be assessed, which directly affects the assessment of the collateral stability of the inventory assets. Conversely, if the concentration of a specific supplier is excessively high, it acts as a negative factor for the sustainability and recoverability of the inventory assets.

[0038] In addition, the receiving data (111) is used for cash flow analysis by including payment condition information. Information on whether the purchase price is paid in cash, whether it is a credit transaction, and the payment deadline is a key element in calculating the accounts payable payment period (DPO), which contributes to increasing the accuracy of the cash conversion period (CCC) calculation. Through this, the short-term liquidity structure, which was not revealed in conventional financial statement-based analysis, can be precisely identified.

[0039] Consequently, the receiving data (111) is utilized as data that comprehensively reflects supply chain stability, cost structure, and cash outflow structure, going beyond simple information on inventory increase. In the present invention, by reflecting the receiving data (111) in real time, the basic stability of inventory assets can be evaluated more precisely during the inventory collateral loan screening process.

[0040] Detailed explanation of the shipment data (112)

[0041] The outbound data (112) includes information generated during the process of goods being shipped from the logistics center to consumers or distribution channels, and is key data directly related to the realization of actual sales. The outbound data (112) is collected in units of daily shipments by SKU and reflects the speed and scale of sales generation in real time.

[0042] The shipment data (112) includes information on unit prices by channel and is used to calculate actual sales revenue rather than simple shipment volume. By reflecting the characteristics of e-commerce where actual prices differ by sales channel, such as online shopping malls, open markets, and private malls, the sales structure and profitability of each channel can be analyzed in detail. This resolves the distortions that occurred in the conventional evaluation based on total sales.

[0043] Additionally, the shipment data (112) is used to evaluate sales stability by including shipment cycle, order frequency, and seasonality information. Products that maintain a consistent shipment pattern can be evaluated as stable revenue sources, while products whose shipments are concentrated only during specific periods are recognized as assets accompanied by seasonal risk. This information is directly reflected in the calculation of sales stability indicators by the credit evaluation department (130).

[0044] The shipment data (112) includes information on order cancellation rates and return rates, allowing for the determination of the likelihood that shipments formally counted as sales will lead to actual cash inflows. Products with high return rates may be conservatively reflected in collateral value and credit evaluation, which has the effect of quantifying quality and consumer reaction risks that were overlooked in conventional technology.

[0045] Consequently, the shipment data (112) is utilized as core data that comprehensively reflects the sales volume, sales stability, cash inflow potential, and product competitiveness. By analyzing this shipment data in real time, the present invention is configured so that credit evaluation and inventory collateral evaluation reflect current and future sales trends rather than past performance.

[0046] Detailed explanation of inventory asset data (113)

[0047] Inventory asset data (113) is key data that comprehensively represents the current status and asset value of goods stored in a logistics center, and is used as a direct basis for determining collateral stability during the inventory collateral loan screening process. The inventory asset data (113) includes the inventory quantity by product and the total inventory value based on the purchase price, and is configured to allow for the simultaneous identification of the asset scale and composition status rather than just simple quantity information.

[0048] Inventory asset data (113) is used to evaluate the liquidity of inventory and the recoverability of assets by including inventory turnover rate information. Since the turnover rate based on total inventory as well as the turnover rate by product group are managed together, it is possible to clearly distinguish between inventory that is quickly depleted and inventory that is stagnant. This serves to improve upon the limitations of the conventional method of calculating collateral value based only on the total amount of inventory.

[0049] In addition, the inventory asset data (113) quantitatively indicates the duration that inventory remains in the logistics center, including the average inventory holding period (DIO). The DIO is utilized as a key component of the cash conversion cycle and allows the characteristics of inventory, such as the decrease in value and disposal risk increasing as the holding period lengthens, to be reflected in credit and collateral evaluations.

[0050] The inventory asset data (113) includes long-term inventory ratio information, which allows for a clear identification of the proportion of inventory stored for longer than a specific period. For example, the proportion of inventory held for 90 days or more or 180 days or more acts as a depreciation factor when calculating collateral value and is used as an important exclusion criterion in the process of selecting collateral-eligible inventory.

[0051] Furthermore, the inventory asset data (113) includes inventory obsolescence information based on the elapsed days from the receiving date, thereby precisely reflecting the change in value over time even for the same product. Through this, the present invention recognizes inventory not as a simple asset but as a time-sensitive asset, and provides a basis for structurally managing collateral risk.

[0052] Detailed explanation of sales channel data (114)

[0053] Sales channel data (114) is data indicating the distribution structure and sales distribution status of a consignor selling goods, and is used to determine business stability and the level of risk distribution. Sales channel data (114) includes the number and composition of sales channels such as online malls, open markets, platforms, and private malls, and allows for a clear identification of whether the company's sales base is concentrated in a specific channel.

[0054] Sales channel data (114) includes information on the proportion of sales by channel, allowing for quantitative analysis of whether the sales structure is concentrated in a few channels or distributed across many channels. If the sales concentration is high, the risk of changes in specific platform policies or settlement delays may increase, and these factors are reflected as negative factors in credit evaluation.

[0055] In addition, sales channel data (114) includes channel-specific settlement cycle information, enabling precise prediction of the timing of cash inflow. The D+N settlement structure of each channel acts as a factor determining the time lag between the occurrence of sales and the actual cash inflow, which has a direct impact on the calculation of cash flow data and risk analysis.

[0056] Sales channel data (114) can determine business scalability and whether there is a change in sales strategy by including the history of entering new channels. While entering new channels may be a factor in increasing costs in the short term, it can serve as an indicator of sales diversification and growth in the medium to long term, and is therefore used as a complementary factor when calculating credit ratings.

[0057] In addition, the sales channel data (114) quantifies the risk of dependence on a specific platform by calculating the channel concentration based on the sales proportion of the top channels. Through this, the present invention can move away from an evaluation centered on simple sales volume and reflect the stability and sustainability of the sales structure in the credit evaluation.

[0058] Detailed explanation of profitability data (115)

[0059] Profitability data (115) is data for evaluating actual profit generation ability rather than sales volume, and serves to improve the quality level of credit evaluation. Profitability data (115) includes the sales margin rate by product and clearly shows how the profit structure differs depending on the product composition, even when the same sales are generated.

[0060] Profitability data (115) includes not only the average margin rate but also margin rate trend information, thereby enabling analysis of whether the medium-term profit structure improves or deteriorates rather than short-term performance. A continuous decline in the margin rate may imply increased price competition or rising costs, which acts as a factor increasing credit risk.

[0061] In addition, profitability data (115) includes information on the proportion of logistics costs to analyze the cost structure relative to sales in detail. In the e-commerce industry, since logistics costs have a significant impact on profitability, an increase in the proportion of logistics costs is reflected in credit ratings as a factor that undermines actual profitability.

[0062] Profitability data (115) includes estimates of net profit margins based on product groups or overall operations and is used as a criterion for determining the sustainability of business activities. Even if sales increase in the short term, if the net profit margin is low or deteriorates, it may have a negative impact on the ability to repay debt in the long term.

[0063] Consequently, the profitability data (115) eliminates the illusion that occurs in sales-centered evaluations and enables the reflection of intrinsic profit generation ability and cost control ability in the credit rating. By analyzing this profitability data in real time, the present invention implements a more sophisticated and realistic credit rating system compared to conventional technology.

[0064] Detailed explanation of cash flow data (116)

[0065] Cash flow data (116) is a set of data used to precisely calculate the timing and scale of actual cash inflows and outflows from logistics performance data, and is used as a key basis for judgment in credit evaluation and loan screening. Cash flow data (116) includes daily projected sales figures combining shipment volume and unit price, and is configured to analyze based on the potential for actual cash inflow rather than formal sales. This reduces the problem of misjudgment of cash liquidity that occurred in conventional financial statement-based evaluations.

[0066] Cash flow data (116) includes daily purchase amount information and simultaneously reflects a cash outflow structure based on the quantity received and the purchase unit price. In an e-commerce environment where purchase timing and payment conditions vary, cash flow data (116) contributes to analyzing a company's short-term payment ability based on cash flow rather than simple profit and loss. This serves as a very important factor in evaluating loan repayment ability.

[0067] In addition, cash flow data (116) includes information on the scheduled settlement date and estimated settlement amount for each sales channel. By reflecting a different D+N settlement structure for each sales channel, the timing of actual cash inflow after sales occur can be accurately predicted. This structure allows the risk of settlement delay, which was overlooked in conventional technology, to be directly reflected in the credit evaluation.

[0068] Cash flow data (116) includes the cash conversion cycle (CCC) as a key indicator and comprehensively reflects the inventory holding period (DIO), accounts receivable collection period (DSO), and accounts payable payment period (DPO). The CCC is an indicator representing the time required for a company to recover the cash it has invested, and a shorter CCC is evaluated as a structure with superior liquidity. In the present invention, the CCC is calculated in real time to identify cash flow risks in advance.

[0069] Consequently, cash flow data (116) is used as data that comprehensively reflects the possibility of short-term cash depletion, the risk of settlement delay, and financial pressure resulting from increased purchases. Through this, the present invention provides a basis for detecting liquidity crises in real time, which could only be identified retrospectively in conventional technology, and for responding proactively during the credit evaluation and risk monitoring stages.

[0070] Detailed explanation of product market data (117)

[0071] Product market data (117) is data intended to reflect the external market environment and value volatility of inventory assets, and plays an important role in ensuring the realism of the collateral value calculation. The product market data (117) includes product category information and is configured to allow for the evaluation of the competitive intensity and price fluctuation characteristics of the market to which each product group belongs. This prevents errors in evaluating inventory risk between different categories using the same standard.

[0072] Product market data (117) includes product life cycle information to determine which stage a product is in among the introduction, growth, maturity, and decline stages. Since products located in the later stages of the life cycle are more likely to decrease in value even with the same inventory quantity, they can be conservatively reflected when calculating collateral value. This improves the inventory valuation structure in which the time element was not reflected in the prior art.

[0073] In addition, product market data (117) includes a seasonality index to reflect the characteristics of products where demand is concentrated only during specific periods. Since seasonal products may experience a sharp decline in value during the off-season, they are used as basic data for applying seasonality coefficients during the collateral valuation process. This prevents the overvaluation of collateral value.

[0074] Product market data (117) includes market sales price trends collected through external crawling or API linkage. Since competitor prices, platform discount policies, and market average price fluctuations are reflected in real-time or periodically, the current market value of inventory assets can be estimated more accurately. This complements the limitations of the conventional internal purchase price-based valuation.

[0075] Furthermore, the product market data (117) includes price benchmark information for similar products, enabling the evaluation of relative competitiveness with products of the same or similar categories. Through this, the present invention recognizes inventory assets not as fixed internal assets but as assets that fluctuate within market competition, thereby significantly improving the precision of collateral value calculation and risk management.

[0076] Detailed description of the data processing unit (120)

[0077] The data processing unit (120) is a core processing module that converts various logistics, inventory, sales, and market data collected through the data collection unit (110) into analyzable indicators. The data processing unit (120) goes beyond simple aggregation of raw data and generates a data structure suitable for financial review by performing time-based sorting, SKU-unit integration, and shipper-unit aggregation. Through this, accuracy and consistency in data utilization are ensured.

[0078] The data processing unit (120) includes a function for calculating daily estimated sales. The estimated sales calculated by combining the shipment volume and the unit price is used as basic data for the sales stability evaluation and cash flow analysis of the credit evaluation unit (130). In this process, the data processing unit (120) corrects for abnormal shipments, temporary events, promotional effects, etc., to minimize distorted figures.

[0079] In addition, the data processing unit (120) calculates the cash conversion cycle (CCC) to quantitatively evaluate the speed of the company's cash recovery. By calculating the inventory holding period, accounts receivable collection period, and accounts payable payment period based on real-time data, it becomes possible to perform a cash flow analysis that is much more sensitive than conventional static financial indicators. This plays a key role in detecting short-term liquidity risk in advance.

[0080] The data processing unit (120) calculates inventory soundness indicators and uses them for collateral and credit evaluation. Indicators such as inventory turnover ratio, long-term inventory ratio, and inventory obsolescence indicate how efficiently inventory assets are managed and are reflected as key elements in the selection of collateral-eligible inventory and valuation. Through this, the evaluation is conducted based on the quality of the inventory rather than the simple size of the inventory.

[0081] Consequently, the data processing unit (120) performs the pivotal function of refining and interpreting complex logistics data into a form that can be directly utilized for financial evaluation. Through the data processing unit (120), the present invention integrates logistics, financial, and market information, which were managed in a fragmentary manner in conventional technology, into a single evaluation system, thereby structurally improving the accuracy and reliability of credit evaluation and loan screening.

[0082] Detailed explanation of the credit rating department (130)

[0083] The credit evaluation unit (130) is a core component that quantitatively calculates the creditworthiness of a shipper based on multidimensional logistics and financial indicators calculated through the data processing unit (120). Unlike the conventional static credit evaluation method based on financial statements, the credit evaluation unit (130) is configured to more accurately evaluate the current business soundness by reflecting in real-time the shipment volume, sales flow, inventory turnover characteristics, and cash flow status occurring in actual business activities. Accordingly, short-term market changes or fluctuations in the business environment can be immediately reflected in the credit rating.

[0084] The credit evaluation department (130) calculates credit scores based on six evaluation items: sales stability, profitability, inventory soundness, cash flow, business diversification, and growth potential. Each evaluation item consists of quantitative indicators directly linked to logistics data, and detailed data such as the shipment volume growth rate, shipment volatility, average margin rate, inventory turnover rate, cash conversion cycle, and sales channel structure are used to calculate scores for each item. This structure improves upon the problems of the prior art where individual indicators were managed in a dispersed manner or only some items were evaluated.

[0085] The credit evaluation department (130) is configured to include a time series forecast-based evaluation function, enabling credit evaluation that reflects not only past performance but also the business flow of a certain period in the future. For example, by reflecting in advance the possibility of future sales decrease or increase through LSTM-based shipment volume prediction results, evaluation errors caused by temporary boom or bust can be reduced. This is a factor that overcomes the structural limitations of conventional empirical judgment or evaluation based on simple average values.

[0086] In addition, the credit evaluation department (130) is configured to calculate a comprehensive credit score by applying weights to each evaluation item. At this time, each weight is set to reflect the characteristics of the e-commerce industry, and items directly related to short-term repayment ability, such as sales stability or cash flow, are reflected with a relatively high weight. This weighting structure ensures that even companies with the same sales volume can have different credit ratings depending on their business structure and operational efficiency.

[0087] As a result, the credit evaluation department (130) moves away from evaluations based on static financial information and implements a dynamic and predictive credit evaluation system based on logistics performance data. Through this, it simultaneously mitigates the problems of credit rating distortion, underestimation of growth companies, and overestimation of risk companies that occurred in conventional technology, and supports financial institutions in making more rational loan decisions.

[0088] Detailed explanation of the inventory evaluation department (140)

[0089] The inventory valuation unit (140) is configured to automatically calculate the collateral eligibility and collateral value of inventory assets stored in a logistics center, thereby providing key criteria for inventory collateral loans. Unlike conventional physical inspection or simple total inventory valuation methods, the inventory valuation unit (140) is configured to precisely evaluate collateral stability by considering both the qualitative characteristics of the inventory and the market environment. Through this, the recoverability of inventory assets and the risk of value depreciation can be structurally managed.

[0090] The inventory evaluation department (140) comprehensively reviews multiple conditions, such as inventory holding period, inventory turnover rate, return rate, product condition, and the ratio of purchase price to market price, in order to select inventory eligible for collateral. Inventory that has been stored for a long period or has a low turnover rate is automatically excluded, and products with a high return rate or inventory that is not in normal condition are also excluded from collateral. This selection structure solves the problem of the conventional technology where the entire inventory was collectively recognized as collateral.

[0091] The inventory valuation department (140) adopts a structure that sums the inventory quantity based on the unit collateral value when calculating the collateral value. The unit collateral value is calculated based on the lower value obtained by comparing the purchase price and the market price, and a comprehensive discount rate is applied thereto. This is intended to preemptively reflect the possibility of loss when the market price falls or inventory is disposed of, and has the effect of preventing the overestimation of the collateral value.

[0092] The comprehensive discount rate is calculated by applying a combination of the basic discount rate, turnover coefficient, seasonality coefficient, and obsolescence coefficient. Relatively high coefficients are applied to inventory with high turnover and low seasonal influence, while conversely, conservative coefficients are applied to seasonal goods or obsolete inventory. Unlike the conventional method of applying a uniform discount rate, this multi-coefficient structure enables precise evaluation that reflects the individual characteristics of inventory.

[0093] Consequently, the inventory valuation department (140) evaluates inventory assets not as simple quantity-based assets, but as collateral assets whose value changes according to time, market, and operational efficiency. Through this, collateral risk can be systematically managed, and financial institutions can increase the reliability of collateral value and suppress the possibility of default in advance. This provides the effect of significantly improving the structural stability of inventory-backed loans.

[0094] Detailed explanation of the loan screening department (150)

[0095] The loan review department (150) is a core decision-making module that automatically derives the actual loan amount, limit, and conditions by integrating the results produced by the credit evaluation department (130) and the inventory evaluation department (140). The loan review department (150) is designed to ensure consistency and objectivity in evaluation while drastically shortening the review speed by implementing the multi-stage review process, which was previously performed by reviewers at existing financial institutions, into system logic. In this process, the loan review department (150) does not simply mechanically combine credit ratings and collateral values, but rather determines whether to approve the loan and the limit by reflecting various logistics performance data, such as the business characteristics, sales structure, and cash flow patterns of the shipper.

[0096] The loan screening department (150) first receives credit rating information calculated from the credit evaluation department (130) and sets the basic LTV (Loan to Value) range for each rating. For example, it is configured so that a higher collateral recognition ratio can be applied as the rating increases, such as 80% for AAA rating, 75% for AA rating, 70% for A rating, 65% for BBB rating, and 60% for BB rating. This structure allows the loan amount to be reasonably differentiated according to the credit risk level of the company, even with the same collateral value. This can alleviate the problem of "collateral-centered excessive lending" that occurred in conventional technology.

[0097] Subsequently, the loan review department (150) calculates the final loan limit by combining the collateral value information calculated by the inventory valuation department (140). The loan limit is basically calculated according to the formula "Loan Limit = Collateral Value × LTV," where the collateral value is an amount calculated only for collateral-eligible inventory. If necessary, the loan review department (150) may automatically propose a limit that is more conservative than the theoretical maximum limit by reviewing the cash conversion cycle (CCC), short-term net cash flow, and settlement cycle distribution calculated from the cash flow data (116). This reduces the risk of excessive loans being approved for companies with insufficient short-term repayment ability.

[0098] The loan review department (150) is configured to output review results in a structured form so that they can be linked to the financial institution's internal system. By providing information such as approval status, approval limit, applicable LTV, credit rating, major risk factors, and conditional approval conditions (e.g., maintaining a specific channel sales ratio, managing long-term inventory ratio, etc.), the person in charge can quickly review the results proposed by the system and make a final approval decision. In this process, the loan review department (150) leaves log data to track the basis of the review afterward and provides grounds for responding to internal and external audits.

[0099] As a result, the loan screening department (150) organically combines credit evaluation, collateral evaluation, and cash flow analysis to automate the conventional time-consuming and manpower-dependent loan screening process and simultaneously ensure the sophistication and reproducibility of the screening. Through this, small and medium-sized e-commerce companies can raise funds in a much shorter time than in the past, and financial institutions can significantly improve screening efficiency while maintaining a risk management system.

[0100] Detailed explanation of the risk monitoring department (160)

[0101] The risk monitoring unit (160) is a post-management module that tracks and monitors the business activities and inventory status of the consignor in real time after the loan is executed, thereby enabling early detection of potential defaults and preemptive response. In the past, risk signals were often identified belatedly by relying on periodic document checks or limited on-site visits after the loan was executed, but the risk monitoring unit (160) structurally improves these limitations by performing automatic monitoring based on the real-time data stream of the logistics center. This module is closely linked with the credit evaluation unit (130), inventory evaluation unit (140), and data processing unit (120), and is characterized by monitoring risk changes using the same standard axis as the pre-loan evaluation.

[0102] The risk monitoring unit (160) predefines alarm trigger conditions for several key indicators and determines in real time whether each condition is met. For example, if the shipment volume drops to less than 50% of the average of the last 7 days, it recognizes this as a risk of a sharp decline in sales and automatically initiates a credit reassessment process. If the inventory volume increases to more than 200% of the average of the 7 days, it determines this as a signal of excess inventory and an increase in potential bad inventory, and triggers an alarm to request a collateral reassessment. Through this, the combined risk of sluggish sales, inventory accumulation, and slowing demand appearing simultaneously can be detected early.

[0103] In addition, the risk monitoring unit (160) monitors the long-term inventory ratio and the margin rate fluctuation together. If the long-term inventory ratio exceeds 30% of the total inventory, it is considered that the liquidity of the inventory has rapidly deteriorated, and an alarm is issued to lower the collateral value. If the margin rate falls to a certain standard (e.g., less than 10%), it is determined that profitability has decreased due to intensified price competition or rising costs, and a review for a credit rating downgrade is triggered. In this way, the risk monitoring unit (160) enables more three-dimensional risk management by monitoring profitability and inventory quality in an integrated manner, rather than just the sales volume.

[0104] The risk monitoring department (160) also recognizes delays in sales channel settlement as a significant risk signal. If the settlement amount is delayed by 14 days or more based on the scheduled settlement date for each channel, it is considered that the recovery risk of that channel has increased, and an immediate notification is issued. This notification is sent to a financial institution representative or linked with an internal risk management system to help quickly discuss follow-up measures, such as adjusting loan conditions, requiring additional collateral, or readjusting the repayment schedule, if necessary. This structure provides a comprehensive management system that reflects recovery risk at the channel level in detail.

[0105] Consequently, the risk monitoring unit (160) simultaneously monitors multiple indicators such as shipment volume, inventory volume, long-term inventory ratio, margin rate, and settlement delay, and performs the role of automatically connecting different response scenarios for each indicator, such as "credit re-evaluation," "collateral re-evaluation," "collateral value downgrade," "credit rating downgrade," and "immediate notification." Through this, the actual risk status of the consignor is managed at all times even after the loan is executed, and the financial institution can receive a prior warning and respond before default occurs. The risk monitoring unit (160) can be considered a core component that enables the system of the present invention to function as an intelligent infrastructure that manages the risk lifecycle of the entire loan process, going beyond simple screening automation.

[0106] Below, a logistics data-based loan screening method (S100) using the above-mentioned real-time credit evaluation and inventory collateral loan automatic screening system (100) based on logistics performance data is described in detail.

[0107] The logistics data-based loan screening method (S100) according to the present embodiment is configured to include a data collection step (S110) performing a specific role, a cash conversion cycle calculation step (S120), a credit rating determination step (S130), a collateral value calculation step (S140), a loan limit calculation step (S150), and an alarm output step (S160).

[0108] Detailed description of the data collection step (S110)

[0109] The data collection step (S110) is the first step performed in the logistics data-based loan screening method (S100), and serves to secure all basic data necessary for determining the credit rating, calculating the cash conversion cycle, determining the collateral value, and calculating the loan limit in subsequent steps. In the data collection step (S110), by linking with the shipper's logistics center WMS and related systems, at least the items required for evaluating the cash conversion cycle and inventory health among incoming data (111), outgoing data (112), inventory asset data (113), sales channel data (114), profitability data (115), cash flow data (116), and product market data (117) are collected in real-time or in batch mode. This enables a much faster and more accurate data-based screening compared to the conventional method where a person manually compiled Excel files or written reports.

[0110] In the data collection stage (S110), the inflow and outflow data (112) and inventory data of the consignor are collected with particular focus. For example, assuming that a specific consignor shows a pattern of shipping an average of 1,000 SKUs per day and receiving an average of 800 SKUs per day during the month of January 2025, the data collection stage (S110) records the daily and SKU-specific shipping and receiving quantities, the sales unit price and purchase unit price at that time, and shipping channel information. At this time, instead of simply collecting aggregated values ​​such as "total shipping volume of 30,000 in January," detailed event-unit data is accumulated, such as "January 5: 200 units of Product A, 150 units of Product B shipped, Channel: Company Mall / Open Market." This detailed collection structure improves the accuracy of subsequent time-series analysis and cash flow analysis.

[0111] In addition, in the data collection step (S110), to reflect changes in the time axis of the inventory data, the inventory inflow and outflow history is stored together with inventory snapshots at specific points in time. For example, if the inventory of product A changed from 500 units on January 1 to 800 units on January 10 and 300 units on January 20, the data collection step (S110) links all inflow and outflow events during the period to accumulate data so that it can track "how much was inflow and outflow on which date and in what structure the current inventory is formed." This structure is directly utilized for calculating the average inventory holding period (DIO) and the long-term inventory ratio.

[0112] The data collection step (S110) may also include processing logic for exceptional situations or data outliers. For example, if the shipment volume of a specific product on a certain day among the shipment data transmitted from the WMS is recorded as 10 times the usual average, it may be the result of an actual large-scale promotion or a simple input error. The data collection step (S110) detects such abnormal patterns and performs the function of marking them as "data requiring review" in subsequent steps, or automatically correcting or withholding them according to set rules. Such preprocessing functions have a significant impact on the stability of the cash conversion cycle calculation step (S120) and the credit rating determination step (S130).

[0113] For example, assuming that a shipper increased the volume of shipments to three times the usual amount while conducting a large-scale discount event for two weeks immediately preceding the Lunar New Year holiday, the data collection stage (S110) can accurately reflect the surge in shipment volume during this period, while also tagging whether the increase during that period is an event-driven increase caused by a specific promotion. Subsequently, the cash conversion cycle calculation stage (S120) or the credit evaluation department (130) can refer to the tagging information to perform a sophisticated analysis that distinguishes between short-term events and structural sales growth. In this way, the data collection stage (S110) performs the role of accumulating the "context" of the data, going beyond simple collection.

[0114] Detailed explanation of the cash conversion cycle calculation step (S120)

[0115] The cash conversion cycle calculation step (S120) is a step in which the cash conversion cycle (CCC) is calculated by adding the accounts receivable collection period (DSO) to the inventory holding period (DIO) and subtracting the accounts payable payment period (DPO), based on the inbound / outbound data (112) and inventory data collected in the data collection step (S110). The cash conversion cycle calculated in this step refers to the "average period it takes for a shipper to purchase goods, hold them in inventory, sell them, and recover the proceeds in actual cash." The shorter this value, the better the liquidity and the better the short-term repayment ability of the company. The cash conversion cycle calculation step (S120) is a key step that quantitatively reveals the real-time liquidity status, which was difficult to ascertain solely from conventional quarterly financial statements.

[0116] For example, assuming that a specific shipper's average DIO is 30 days, DSO is 20 days, and DPO is 25 days, the cash conversion cycle (CCC) is calculated as 30 + 20 - 25 = 25 days. This means that it takes an average of 25 days for this shipper to convert 1 unit of capital into inventory and for that capital to be recovered as cash. If the average CCC in the same industry is 45 days, this shipper can be evaluated as having a sound structure with a faster cash recovery speed than the industry average. The cash conversion cycle calculation step (S120) makes an important contribution to the credit rating determination step (S130) by enabling such comparative analysis.

[0117] In the cash conversion cycle calculation step (S120), DIO, DSO, and DPO can be calculated based on a rolling window, such as the last 30, 60, or 90 days, rather than as simple static values. For example, DIO based on the last 90 days is calculated in the form of "average inventory balance over 90 days ÷ cost of goods sold over 90 days × 90", and DSO is calculated according to a formula such as "accounts receivable ÷ average daily sales × 1 day". DPO can also be calculated as "accounts payable ÷ average daily purchase amount × 1 day". By continuously calculating CCC by period in this manner, it is possible to analyze the trend of whether CCC increases rapidly or remains stable after a specific point in time, and thus linkage with the risk monitoring unit (160) becomes possible.

[0118] To explain with a more specific example, it can be assumed that a shipper's DIO decreased to 20 days as inventory was rapidly depleted due to a surge in short-term sales caused by a promotion, while DSO increased to 40 days due to settlement delays at the sales channel. In this case, if DPO remains unchanged at the 25-day level, CCC is calculated as 20 + 40 - 25 = 35 days. Although it appears on the surface that inventory turnover has improved, in reality, the speed of accounts receivable collection has slowed, resulting in a structure where the overall cash recovery period has actually lengthened. The cash conversion cycle calculation step (S120) reveals these hidden liquidity risks numerically, complementing the limitations of evaluation methods that rely solely on simple inventory turnover ratios.

[0119] Another example is a case where a shipper with bargaining power with suppliers extends the DPO to 45 days. In the same structure of 30 days for DIO and 25 days for DSO, if the DPO is increased from 25 days to 45 days, the CCC is shortened to 30 + 25 - 45 = 10 days. This means that the company is efficiently utilizing working capital by having a very fast recovery speed relative to the cash invested in inventory and by making relatively delayed payments for purchases. The cash conversion cycle calculation step (S120) reflects the interrelationships of DIO, DSO, and DPO in this way, and by evaluating the "speed of cash circulation" rather than the simple sales volume, it provides a basis for setting a more aggressive or conservative limit in the loan limit calculation step (S150).

[0120] Detailed explanation of the credit rating determination stage (S130)

[0121] The credit rating determination step (S130) is a step in which a final credit score is derived by weighted summing various evaluation indicators calculated by the data processing unit (120) and the credit evaluation unit (130), and the credit score is mapped to grades AAA to B according to predefined intervals. In this step, six evaluation items—sales stability, profitability, inventory soundness, cash flow, business diversification, and growth potential—are each assigned different weights to form a comprehensive score, and based on this, the final credit rating to be used by the loan review unit (150) is determined. The credit rating determination step (S130) performs the function of comprehensively evaluating the actual repayment ability of an e-commerce shipper by reflecting multiple operational indicators in a balanced manner without leaning toward a single indicator.

[0122] In the credit rating determination stage (S130), scores for each item are first calculated. For example, sales stability is converted into a score between 0 and 100 by combining the shipment growth rate, the coefficient of variation of shipments, and the seasonally adjusted sales trend, while profitability is assigned a score reflecting the average margin rate, margin rate trend, and superiority compared to the industry average. Inventory health is scored based on the inventory turnover ratio / industry average and the long-term inventory ratio, and cash flow is scored based on the length of the CCC and the 30-day net cash flow forecast results. Business diversification is scored based on the number of sales channels and product category diversity, and growth is scored based on the month-over-month growth rate and the expansion of new channels. These item-specific scores are normalized to a range of 0 to 100 and used for weighted summation.

[0123] To explain with a specific example, it can be assumed that the sales stability score of shipper A is calculated as 85 points, profitability as 80 points, inventory health as 75 points, cash flow as 90 points, business diversification as 70 points, and growth as 65 points. The credit rating determination stage (S130) applies weights of 25% for sales stability, 20% for profitability, 20% for inventory health, 20% for cash flow, 10% for business diversification, and 5% for growth to these six items. Therefore, the total credit score can be calculated as 0.25×85 + 0.20×80 + 0.20×75 + 0.20×90 + 0.10×70 + 0.05×65 = 21.25 + 16 + 15 + 18 + 7 + 3.25 = 80.5 points. Since this shipper falls within the 80-89 point range, its credit rating is determined to be AA.

[0124] As another example, it can be assumed that Shipper B has recently experienced stagnant sales and a low inventory turnover ratio. In this case, relatively low scores may be calculated, such as Sales Stability 60 points, Profitability 65 points, Inventory Health 55 points, Cash Flow 50 points, Business Diversification 40 points, and Growth 45 points. Applying equal weighting, the total credit score becomes 0.25×60 + 0.20×65 + 0.20×55 + 0.20×50 + 0.10×40 + 0.05×45 = 15 + 13 + 11 + 10 + 4 + 2.25 = 55.25 points. Since this value falls within the 50–59 point range, the credit rating determination stage (S130) sets Shipper B's credit rating to BB. In this way, the system is configured to clearly distinguish different grades based on operational performance, even within the same industry.

[0125] The credit rating determination step (S130) is also linked with the risk monitoring unit (160) and performs the role of automatically triggering a re-evaluation when specific indicators deteriorate. For example, if an event occurs repeatedly where the volume of shipments after a loan execution drops to less than 50% of the 7-day average or the margin rate falls to less than 10%, the risk monitoring unit (160) generates a signal requesting a credit rating recalculation. At this time, the credit rating determination step (S130) recalculates the scores of six items by reflecting the latest data and, if necessary, lowers the rating from AA to A, or from A to BBB. This structure is significant in that it enables dynamic credit rating management based on real-time operational data, unlike the conventional method that relied only on periodic screening.

[0126] Detailed explanation of the collateral value calculation step (S140)

[0127] The collateral value calculation step (S140) is a step for calculating the unit collateral value of each inventory item selected by the inventory valuation unit (140) and summing them up to calculate the total collateral value. In this step, the collateral value is calculated by comprehensively considering multiple factors such as the inventory holding period, inventory turnover rate, seasonality, obsolescence, ratio of purchase price to market price, return rate, and product condition, rather than just the total inventory amount, thereby preventing the problem of overestimation or underestimation in conventional inventory collateral valuation. The value calculated in the collateral value calculation step (S140) is subsequently combined with the LTV in the loan limit calculation step (S150) and used as the final loan limit.

[0128] In the collateral value calculation step (S140), a rule for selecting eligible inventory for collateral is first applied. For example, it may be set that only inventory with an inventory holding period of 90 days or less, an inventory turnover rate of 80% or more of the industry average, a market price / purchase price ratio of 1.2 or more, a return rate of 15% or less, and normal product condition is recognized as eligible for collateral. The inventory valuation unit (140) analyzes inventory asset data (113) and product market data (117) to extract only SKUs that satisfy these conditions, and the collateral value calculation step (S140) calculates the unit collateral value limited to these selected inventory. In this process, long-term inventory, defective inventory, high-return goods, etc., that do not satisfy the conditions are automatically excluded from the collateral.

[0129] In the next step, the collateral value calculation step (S140) applies the formula “Unit Collateral Value = MIN(Purchase Price, Market Price × 0.7) × Comprehensive Discount Rate” to each inventory item. For example, the purchase price of a specific SKU is 10,000 won and the current market price is 15,000 won, and since the market price × 0.7 is 10,500 won, the standard unit price becomes MIN(10,000, 10,500) = 10,000 won. When the basic discount rate of 0.7, the turnover coefficient of 1.05, the seasonality coefficient of 0.9, and the obsolescence coefficient of 0.95 are applied, the comprehensive discount rate becomes 0.7 × 1.05 × 0.9 × 0.95 ≈ 0.627. In this case, the unit collateral value is calculated as 10,000 won × 0.627 = 6,270 won, and if the inventory quantity is 1,000 units, the collateral value of the corresponding SKU is approximately 6,270,000 won.

[0130] As another example, consider inventory of winter products with strong seasonality. For winter coat inventory with a purchase price of 50,000 won and a market price of 45,000 won, the base unit price becomes MIN(50,000, 45,000 × 0.7 = 31,500) = 31,500 won. By applying a basic discount rate of 0.6, a turnover coefficient of 0.9, a seasonality coefficient of 0.8, and an obsolescence coefficient of 0.8 to reflect the decrease in demand after the promotion ends and seasonal risk, the overall discount rate becomes 0.6 × 0.9 × 0.8 × 0.8 = 0.3456. Accordingly, the unit collateral value is 31,500 won × 0.3456 ≈ 10,886 won, and if the inventory quantity is 500 units, the collateral value of the corresponding SKU is calculated to be approximately 5,443,000 won. This represents a significantly conservative collateral value compared to the total inventory based on purchase cost (50,000 × 500 = 25,000,000 won), reflecting the full reflection of seasonality and depreciation risks.

[0131] The collateral value calculation step (S140) calculates the total collateral value by summing up all the collateral values ​​calculated for each SKU. For example, if the collateral value of 100 million won calculated from the high-turnover core product group, 54.43 million won calculated from the seasonal product group, and 70 million won calculated from the general product group are summed, the total collateral value becomes approximately 224.43 million won. Subsequently, in the loan limit calculation step (S150), the actual loan limit is calculated by multiplying this collateral value by the LTV for each credit rating. For example, if the credit rating is AA and an LTV of 75% is applied, the maximum loan limit for this shipper can be calculated as 224.43 million won × 0.75 ≈ 168.32 million won. In this way, the collateral value calculation step (S140) strictly reflects the characteristics of the inventory and the market environment, thereby serving to prevent the financial institution from being exposed to risks caused by excessive collateral loans.

[0132] Detailed explanation of the loan limit calculation step (S150)

[0133] The loan limit calculation step (S150) is a step that calculates the final loan limit that can be provided to the shipper by combining the collateral value derived in the collateral value calculation step (S140) and the credit rating calculated in the credit rating determination step (S130). In this step, a differential LTV (Loan To Value) ratio is applied according to the credit rating to reflect the fact that the level of risk varies depending on the credit rating even with the same collateral value. For example, it can be designed with a structure in which the loan allowance ratio relative to the collateral value is gradually reduced as the rating decreases, such as 80% for an AAA rating, 75% for an AA rating, 70% for an A rating, 65% for a BBB rating, and 60% for a BB rating. Through this, the effect of managing credit risk in a balanced manner is achieved, even though the loan is based on inventory collateral.

[0134] The loan limit calculation step (S150) first applies the basic formula "loan limit = collateral value × LTV". For example, if the collateral value calculated in the collateral value calculation step (S140) is 200 million won and the credit rating of the consignor is determined to be AA in the credit rating determination step (S130), applying an LTV of 75% results in a basic loan limit of 200 million won × 0.75 = 150 million won. This value corresponds to the theoretically allowable maximum limit, and subsequently, the present invention determines whether to apply this basic limit as is or to make partial adjustments by additionally considering the cash conversion cycle (CCC), short-term cash flow, sales volatility, etc.

[0135] To provide a more specific example, let's assume there are two shippers with identical collateral values ​​of 200 million won. Shipper A has a credit rating of AAA, a short cash conversion cycle of approximately 20 days, and has maintained stable sales and margins over the past six months. In this case, the loan limit calculation step (S150) can calculate a loan limit of approximately 160 million won by applying an LTV of 80%, which corresponds to the AAA rating. On the other hand, if Shipper B has the same collateral value but a credit rating of BBB, a long cash conversion cycle of 50 days or more, and has experienced a decline in sales and margins over the past three months, the limit can be restricted to approximately 130 million won by applying an LTV of 65%. As such, the system is configured to clearly differentiate loan limits based on the credit risk structure, even with identical collateral.

[0136] The loan limit calculation step (S150) may also include a function to propose a more conservative limit than the basic formula if necessary, by reflecting short-term cash flow risk. For example, even for a shipper with a credit rating of AA, a collateral value of 200 million won, and an LTV of 75%, for which a limit of 150 million won has been calculated, if the forecast result of the net cash flow over the past 30 days has consistently been negative and settlement delays are accumulating, the system may suggest a downward adjustment of the actual recommended limit to 120 million won or 130 million won based on internal risk rules. This adjustment reflects a situation where "documentary collateral and credit are good, but there is a burden on short-term liquidity," and contributes to sound loan operations in practice.

[0137] Consequently, the loan limit calculation stage (S150) is a hybrid structure combining collateral-based and credit-based evaluations. It applies LTV based on credit ratings to the inventory collateral value while simultaneously providing safeguards to curb excessive leverage through cash conversion cycles and cash flow data. From the perspective of financial institutions, the screening process is formulated and automated, accelerating processing speeds while ensuring consistency in evaluation as limits are calculated based on objective criteria. For shippers, a structure is established that allows them to clearly understand how their logistics performance and credit status are linked to their loan limits, providing an environment favorable for transparent financial transactions and the formation of long-term credit relationships.

[0138] Detailed explanation of the alarm output step (S160)

[0139] The alarm output stage (S160) is a stage that monitors the shipper's logistics performance data and inventory status in real time after the loan is executed, and immediately generates an alarm signal if signs of risk are detected. This stage operates based on abnormal patterns detected by the risk monitoring unit (160) and can be designed to not only provide simple notifications but also suggest specific directions for action, such as "credit re-evaluation required," "collateral re-evaluation required," or "collateral value downward adjustment required." The alarm output stage (S160) performs a key function of reducing losses by early detection of potential defaults during the post-loan management stage following the loan review.

[0140] The alarm output step (S160) operates based on a predefined alarm trigger for each indicator. For example, if the shipment volume drops sharply to less than 50% of the average of the last 7 days, the system generates an alarm related to the risk of sales decline and outputs a signal for action recommending credit reassessment or re-examination of the repayment plan. More specifically, if a shipper whose usual daily average shipment volume was 1,000 units has decreased to an average of 400 units over the last 7 days, the alarm output step (S160) can record a "shipment volume drop event" and send an alarm message to a financial institution representative and an internal risk management system. At this time, the message may include not only simple numerical values ​​but also summary information such as "last 7-day average shipment volume = 400 units, 40% of the standard."

[0141] Another example is the occurrence of a surge in inventory. If the total inventory, including long-term inventory, increases rapidly to a level exceeding 200% of the average over the past 7 days, this may indicate a slowdown in demand or an accumulation of inventory due to excessive purchasing. For instance, if a shipper whose inventory value usually remains at around 500 million won increases to 1.2 billion won in a short period, the alarm output stage (S160) classifies this as an "inventory surge event" and outputs an alarm requiring collateral revaluation and a review of purchasing strategies. At this time, the system can also present which product group has experienced a concentrated increase in inventory and which SKUs have seen a sharp drop in turnover, enabling a more precise risk diagnosis.

[0142] The alarm output step (S160) also generates a separate alarm for a decline in the margin rate and an increase in long-term inventory. For example, if the overall average margin rate falls from the existing 20% ​​level to the 8% level and the long-term inventory ratio exceeds 30% of the total inventory, this may be a signal that price discount pressure and inventory deterioration are occurring simultaneously. In such cases, the alarm output step (S160) recognizes this as a "complex event of a decline in the margin rate and an increase in long-term inventory" and generates a notification to simultaneously review a credit rating downgrade and a downward adjustment of the collateral value. Through this, the financial institution can proactively respond not only to a simple decrease in sales but also to risks arising from the deterioration of profitability and inventory quality.

[0143] Finally, an alert regarding delays in sales channel settlement payments also plays an important role. If settlement payments are not deposited by a specific channel for more than 14 days from the scheduled settlement date, the alert output step (S160) classifies this as a "settlement delay event" and immediately generates a notification. For example, if a settlement payment of 30 million won from Platform A, which accounts for 40% of monthly sales, remains unpaid for more than two weeks past the scheduled date, this alert points out a situation that goes beyond a simple accounting delay and has the potential to lead to recovery risk. Through this alert, financial institutions can re-examine the reliability of the relevant channel and, if necessary, promptly discuss follow-up measures such as adjusting loan conditions, requiring additional collateral, or adjusting the repayment timing. In this way, the alert output step (S160) comprehensively monitors shipment volume, inventory, margin rate, and settlement status, and by converting risk indicators into "information leading to immediate action," it performs a key role in enabling the system of the present invention to function as a full-cycle risk management platform beyond simple loan approval automation.

[0144] As explained above, the present invention can fundamentally resolve the time lag problem inherent in conventional financial statement-based loan assessments. Previously, reliance on quarterly or annual financial data resulted in a discrepancy between actual business performance and credit evaluation results; however, the present invention enables credit evaluation that reflects a company's current business status by collecting incoming, outgoing, and inventory data from a logistics center WMS in real time and analyzing it immediately. Consequently, it can immediately reflect rapid sales fluctuations or seasonal effects, effectively improving the problem of evaluation distortion that occurred in conventional technology.

[0145] This invention resolves the problem of screening delays by moving away from the conventional collateral valuation structure that relied on physical inventory verification. In the existing method, collateral valuation took several weeks due to on-site visits and manual verification; however, this invention automatically analyzes inventory holding periods, turnover rates, return rates, and product conditions to immediately select eligible inventory for collateral. Through this, collateral valuation can be completed in real-time or within a short period, thereby significantly shortening the loan approval time.

[0146] According to the present invention, the rapid inventory turnover characteristics of the e-commerce industry are precisely reflected in the calculation of collateral value. Conventional technology had limitations in that it evaluated high-turnover inventory and long-term inventory equally by applying only the total inventory amount or a uniform discount rate, but the present invention quantitatively reflects inventory risk by comprehensively applying a turnover coefficient, a seasonality coefficient, and an obsolescence coefficient. As a result, the problem of overvaluing or undervaluing collateral value is reduced, and the collateral risk management of financial institutions is strengthened.

[0147] This invention enables the proactive identification of liquidity risks, which were previously recognized only retrospectively, through a cash flow forecasting function. Previously, it was difficult to immediately detect deterioration in cash flow caused by delayed accounts receivable collection or increased purchases; however, this invention allows for the early detection of short-term liquidity risks by estimating daily net cash flow through the linkage of settlement cycles and inbound / outbound data. This provides a foundation for e-commerce companies to effectively respond to the highly volatile operating environment.

[0148] According to the present invention, credit evaluation based on a prediction model becomes possible, overcoming the limitations of relying on conventional empirical judgment. Unlike the method of evaluation based solely on past average performance, the present invention utilizes an LSTM time series model to predict future sales trends and reflects this in the credit evaluation, thereby enabling the pre-inclusion of growth or decline phases. Consequently, it is possible to simultaneously reduce opportunity losses caused by excessive loan approvals or conservative evaluations.

[0149] This invention provides significant improvements over conventional technology in terms of risk management following loan execution. Previously, responses to risk signals were delayed due to reliance on periodic inspections or manual monitoring; however, this invention enables immediate credit or collateral reassessment by detecting events such as a sharp decline in shipments, a surge in inventory, a drop in profit margins, and settlement delays in real time. This allows for the prevention of potential non-performing loans.

[0150] Overall, this invention improves upon the inefficiencies, time delays, and reduced evaluation accuracy issues inherent in conventional technologies by automating the entire process from data collection to credit evaluation, collateral valuation, loan limit calculation, and post-evaluation monitoring. It reduces costs by eliminating reliance on physical inspections and manual labor, expands financial accessibility by enabling the evaluation of companies without comprehensive financial statements based on logistics performance, and provides financial institutions with the effect of structurally enhancing their risk management capabilities.

[0151] The above detailed description of the present invention describes only specific embodiments thereof. However, it should be understood that the present invention is not limited to the specific forms mentioned in the detailed description, but rather should be understood to include all variations, equivalents, and substitutions within the spirit and scope of the invention as defined by the appended claims.

[0152] In other words, the present invention is not limited to the specific embodiments and descriptions described above, and any person skilled in the art to which the present invention pertains can make various modifications without departing from the essence of the invention as claimed in the claims, and such modifications fall within the scope of protection of the present invention. Explanation of the symbols

[0153] 100: Real-time credit evaluation and automated inventory-backed loan approval system based on logistics performance data 110: Data Collection Unit 111: Incoming data 112: Shipment data 113: Inventory Asset Data 114: Sales Channel Data 115: Profitability Data 116: Cash Flow Data 117: Product Market Data 120: Data processing unit 130: Credit Rating Department Sales stability rating 25% Profitability assessment 20% Inventory health assessment 20% Business diversification valuation 10% Growth potential assessment 5% 140: Inventory Valuation Department 150: Loan Review Department 160: Risk Monitoring Department S100: Logistics Data-Based Loan Screening Method S110: Data collection stage S120: Cash conversion cycle calculation stage S130: Credit rating determination stage S140: Collateral Value Calculation Stage S150: Loan limit calculation stage S160: Alarm output stage

Claims

Claim 1 A data collection unit (110) that collects incoming quantity, incoming unit price, outgoing quantity, selling unit price, inventory quantity, and inventory turnover rate in real time from a logistics center WMS; a data processing unit (120) that calculates daily expected sales amount, cash conversion cycle, and inventory health indicators from the collected data; a credit evaluation unit (130) that calculates a credit rating by summing the scores of six evaluation items—sales stability, profitability, inventory health, cash flow, business diversification, and growth potential—based on the calculated indicators; an inventory evaluation unit (140) that selects inventory with a holding period of 90 days or less and a turnover rate of 80% or more of the industry average, and calculates collateral value by applying a discount rate to the lower of the purchase price and market price of the selected inventory; and a loan screening unit (150) that automatically calculates a loan limit based on the calculated credit rating and collateral value. The system includes a risk monitoring unit (160) that detects abnormal fluctuations in the outgoing volume, inventory volume, and margin rate in real time and generates an alarm after the loan is executed; and the data collected by the data collection unit (110) includes: receiving data (111) including the incoming volume, incoming unit price, incoming cycle, number of suppliers, supplier concentration, and payment conditions; outgoing data (112) including the outgoing volume, sales unit price, outgoing cycle, order cancellation rate, and return rate; inventory asset data (113) including the inventory quantity and total amount by product, inventory turnover rate, average inventory holding period, long-term inventory ratio, and inventory obsolescence; sales channel data (114) including the number of sales channels, sales proportion by sales channel, settlement cycle by channel, new channel entry history, and channel concentration; profitability data (115) including the sales margin rate by product, average margin rate, average margin trend, logistics cost proportion, and net profit margin estimate; and daily expected sales, daily purchase amount, scheduled settlement date by channel, estimated settlement amount by channel, and cash conversion cycle. Cash flow data (116) including; and product market data (117) including product category, product life cycle, seasonality index, market sales price trend and similar product price benchmark; and the credit evaluation unit (130) includes a sales stability evaluation value based on data related to the shipment volume growth rate, shipment volume fluctuation coefficient and seasonally adjusted sales trend;A logistics performance data-based real-time credit evaluation and automated inventory-backed loan assessment system characterized by determining a credit rating by synthesizing the following: a profitability evaluation value based on data regarding the average margin rate, the trend of the average margin rate, and whether the margin rate exceeds the industry average; an inventory health evaluation value based on data regarding the value obtained by dividing the inventory turnover rate by the industry average inventory turnover rate and the long-term inventory ratio; a cash flow evaluation value based on data regarding the 30-day net cash flow forecast, assigning a higher evaluation value as the cash conversion cycle (CCC) is shorter; a business diversification evaluation value based on data regarding the number of sales channels and product category diversity; and a growth potential evaluation value based on data regarding the growth rate compared to the previous month and the expansion of new channels. Claim 2 delete Claim 3 delete Claim 4 A logistics performance data-based real-time credit evaluation and inventory collateral loan automatic screening system, wherein the credit evaluation unit (130) predicts the daily shipment volume for the next 30 days from the shipment volume data of the past 90 days using an LSTM time series model and calculates the expected sales revenue by multiplying the predicted shipment volume by the sales unit price. Claim 5 In paragraph 4, the above-mentioned inventory valuation unit (140) is characterized by differentially applying a coefficient ranging from 0.7 to 1.1 according to the inventory turnover rate, seasonality, and inventory holding period when calculating the collateral value of the inventory, in a logistics performance data-based real-time credit evaluation and inventory collateral loan automatic screening system.

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