A cross-border bulk commodity e-commerce transaction risk assessment management system

Abstract

The application discloses a kind of cross-border bulk commodity e-commerce transaction risk assessment management systems, it is related to e-commerce technical field, including transaction risk management center, data acquisition integration module, knowledge graph module, risk trend analysis module, risk early warning module, coping measures recommendation module and risk assessment report generation module are connected with communication in the transaction risk management center, wherein, each module is connected with electric signal.The application is constructed risk identification model, risk knowledge and association in the knowledge graph of transaction behavior are combined, and the early, middle and late stages of transaction behavior are respectively identified, and at the same time, corresponding risk early warning threshold is set for different periods of transaction behavior, to trigger early warning signal, so that enterprises can take measures before or in the early stage of risk, effectively avoid or reduce the loss caused by risk, improve the response capability of enterprise to risk.

Description

Technical Field

[0001] This invention relates to the field of e-commerce technology, specifically to a risk assessment and management system for cross-border bulk commodity e-commerce transactions. Background Technology

[0002] With the advancement of global economic integration, the scale of cross-border commodity trading is constantly expanding. Commodities are characterized by high value, large transaction volume, and frequent price fluctuations. Since cross-border commodity trade involves multiple links, including procurement, transportation, warehousing, and settlement, and the transaction process is relatively long, e-commerce transactions are highly dependent on the Internet and information technology. Cybersecurity has become one of the important challenges facing cross-border commodity e-commerce transactions.

[0003] For example, Chinese Patent Publication No. CN109670736A describes a risk management method for e-commerce transactions. This method involves setting up a risk management system for e-commerce transactions for unified management, using an e-commerce app based on the risk management system, and establishing a secure data channel between transaction users and the system server to achieve unified management of e-commerce transactions.

[0004] In existing technologies, risk management systems based on e-commerce transaction behavior are used to uniformly manage and supervise e-commerce transactions, solving the problem of chaotic e-commerce transactions leading to uncontrollable risk management. However, since transaction risks exist not only after the transaction but also before and during the transaction, passive assessment after the transaction occurs lacks an effective early warning mechanism, affecting the security of the transaction behavior. Therefore, how to combine risk knowledge and correlations from knowledge graphs to assess risk trends in the pre-, mid-, and post-transaction stages of the transaction behavior, identify potential risk signs, and issue early warnings so that countermeasures can be taken is the problem that this invention aims to solve. To this end, a risk assessment and management system for cross-border bulk commodity e-commerce transactions is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a risk assessment and management system for cross-border bulk commodity e-commerce transactions to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A risk assessment and management system for cross-border bulk commodity e-commerce transactions includes a transaction risk management center, which is communicatively connected to a data acquisition and integration module, a knowledge graph module, a risk trend analysis module, a risk early warning module, a response measure recommendation module, and a risk assessment report generation module, wherein the modules are electrically connected to each other.

[0008] The transaction risk management center is responsible for coordinating communication and data interaction between various modules to ensure close cooperation among all links and improve the coordination and efficiency of the entire system operation.

[0009] The data acquisition and integration module is used to collect data related to cross-border bulk commodity transactions from multiple data sources, and to preprocess and integrate the collected data to obtain a basic transaction dataset.

[0010] The knowledge graph module is used to construct a knowledge graph of transaction behavior, integrate various types of information involved in the transaction, including suppliers, buyers, logistics providers, market conditions and historical transaction records, and filter out risk characteristics related to transaction risk assessment to form a risk characteristic sequence list;

[0011] The risk trend analysis module is used to construct a risk identification model by combining the risk characteristics in the risk characteristic sequence table, identify risks in the early, middle and late stages of the transaction behavior, and analyze the risk trend of the transaction behavior period based on the deviation of each risk characteristic from its preset benchmark value.

[0012] The risk warning module is used to assess the risk of trading behavior based on the results of risk identification and risk trend analysis, and to set corresponding risk warning thresholds for different periods of trading behavior to trigger warning signals and issue risk warning information.

[0013] The response recommendation module is used to combine a pre-established response strategy library containing various risk response strategies, and based on the risk warning information issued by the risk warning module, combine transaction stakeholder information and industry knowledge in the knowledge graph to intelligently recommend appropriate response measures from the response strategy library.

[0014] The risk assessment report generation module is used to automatically generate risk assessment reports for cross-border bulk commodity e-commerce transactions according to a set cycle.

[0015] A further improvement to the technical solution of the present invention is that the data acquisition and integration module specifically includes:

[0016] It integrates multiple interfaces to collect data related to cross-border bulk commodity transactions from various data sources, including cross-border e-commerce platforms, third-party payment platforms, logistics companies, and external market data. This data includes, but is not limited to, order information, payment information, logistics information, supplier and buyer credit data, and market price fluctuation data. Specifically, it captures order information from cross-border e-commerce platforms, obtains payment information from third-party payment platforms, and connects with logistics company systems to collect logistics information. At the same time, it obtains supplier and buyer credit data and market price fluctuation data from external market data sources and internal credit assessment systems to ensure comprehensive data coverage of all aspects of cross-border bulk commodity transactions.

[0017] The collected data related to cross-border bulk commodity transactions are preprocessed, including data cleaning, outlier handling, and standardization.

[0018] Different types of preprocessed data are linked and integrated according to the transaction order number field, and order information, payment information, logistics information, credit data and market price fluctuation data are merged to form a complete and coherent transaction record. Through data mapping and transformation, a basic transaction dataset is constructed.

[0019] A further improvement to the technical solution of this invention is that the knowledge graph module specifically includes:

[0020] The basic transaction dataset is obtained from the data acquisition and integration module. Natural language processing technology is used to parse the text data involved. Combined with the rule engine, based on the preset rule template, entities in the basic transaction dataset are identified and extracted, including supplier names, buyer information, logistics tracking numbers, etc., and the relationship between entities is established. The scattered data points are integrated into a structured information network. At the same time, attribute information in the data is extracted to construct a preliminary knowledge graph structure.

[0021] Based on the actual needs of risk assessment in cross-border bulk commodity transactions, risk characteristics related to transaction risk assessment are selected from the integrated information. The transaction behavior is divided into three periods: early stage, middle stage, and late stage. The key risk characteristics of each stage of the transaction behavior are clarified. Among them, the risk characteristics of the early stage of the transaction behavior include counterparty credit score, historical transaction default rate, and supply and demand change rate; the risk characteristics of the middle stage of the transaction behavior include price volatility, cargo transportation delay rate, and number of days of delayed fund arrival; and the risk characteristics of the late stage of the transaction behavior include the number of transaction disputes, product delivery quality pass rate, and accounts receivable collection cycle.

[0022] The selected risk features are categorized and organized according to the early, middle and late stages of the transaction behavior to form a risk feature sequence table. The risk feature sequence table clearly lists the risk features of each stage, assigns a corresponding identifier and attribute description to each risk feature, and maps the risk feature sequence table to a knowledge graph to form a subgraph structure containing risk feature nodes and relationships.

[0023] A further improvement of the technical solution of the present invention is that: the risk trend analysis module includes a risk identification model construction unit and a risk identification unit;

[0024] The risk identification model construction unit is used to construct a risk identification model based on a neural network model by utilizing historical transaction data stored in the knowledge graph and combining it with risk features related to transaction risk assessment in the risk feature sequence table.

[0025] The risk identification unit is used to identify risks at different stages of trading behavior using the constructed risk identification model, analyze the deviation of each risk characteristic from its respective benchmark value, calculate the early risk trend index, the mid-term risk trend index, and the late risk trend index, and clarify the risk trend at each stage of trading behavior.

[0026] A further improvement to the technical solution of the present invention is that the risk identification model construction unit specifically includes:

[0027] Historical transaction data related to transaction risk assessment is extracted from the knowledge graph. Based on the risk feature types covered by the risk feature sequence table, risk feature data related to transaction risk assessment is selected. The features are normalized, and numerical features are scaled to the range of 0 to 1 to eliminate differences in units. At the same time, based on user needs and historical transaction data, a benchmark value for each risk feature is preset, and risk features that deviate from the benchmark value are marked, and integrated into a comprehensive feature dataset.

[0028] Based on the characteristics and needs of risk assessment in cross-border bulk commodity transactions, a neural network architecture was selected to build a multilayer perceptron (MLP) network model, which includes an input layer, multiple hidden layers, and an output layer.

[0029] The prepared feature dataset is divided into a training set and a validation set. The neural network model is trained using the training set. By setting hyperparameters such as the learning rate and the number of iterations, the model training process is adjusted. During training, the backpropagation algorithm is used to calculate the loss function, and the network weights are adjusted to minimize the loss function value, enabling the model to learn the mapping relationship between risk features and transaction risks. During training, the model is validated using the validation set to evaluate its performance on unseen data and prevent overfitting. By adjusting hyperparameters and optimizing the network structure, the model achieves good performance on the validation set, enabling it to accurately identify risks in cross-border commodity transactions. This process continues until the model achieves satisfactory performance, resulting in the final risk identification model.

[0030] A further improvement to the technical solution of the present invention is that the risk identification unit specifically includes:

[0031] The preprocessed risk characteristic data is input into the pre-built risk identification model and compared with the preset benchmark value. Then, the risk of the current stage of trading behavior is identified, and the risk characteristics that deviate from the benchmark value of the current stage are identified, that is, the risk of trading behavior in the current stage is determined.

[0032] In the early stages of a transaction, the input risk characteristics, including counterparty credit scores, historical transaction default rates, and supply and demand change rates, are compared with the preset benchmark values ​​of the risk characteristics to determine the risk characteristics that deviate from the benchmark values, and then the early risk trend index is calculated.

[0033] During the middle of the transaction, the input mid-term risk characteristics, including price volatility, cargo transportation delay rate and number of days of fund arrival delay, are compared with the preset benchmark values ​​of mid-term risk characteristics to determine the mid-term risk characteristics that deviate from the benchmark values, and then the mid-term risk trend index is calculated.

[0034] In the later stages of the transaction, the input risk characteristics, including the number of transaction disputes, the product delivery quality pass rate, and the accounts receivable collection cycle, are compared with the preset benchmark values ​​of the later risk characteristics to determine the later risk characteristics that deviate from the benchmark values, and then the later risk trend index is calculated.

[0035] After the risk identification model completes the analysis, it obtains the risk trend analysis results of the current stage of trading behavior, and then records the risk trend analysis results in the system's risk identification log, which is associated with the detailed information of the trading behavior.

[0036] A further improvement to the technical solution of this invention lies in the following: the calculation process of the prior risk tendency index is as follows:

[0037] Extract prior risk characteristic data including counterparty credit score, historical transaction default rate and supply and demand change rate, and obtain preset counterparty credit score benchmark value, historical transaction default rate benchmark value and supply and demand change rate benchmark value, and calculate the difference ratio between each prior risk characteristic and its benchmark value;

[0038] Squaring each calculated difference ratio, summing the squared results, taking the square root of the sum, and multiplying by 1 / 3, finally yields the previous risk trend index.

[0039] The calculation process for the medium-term risk trend index is as follows:

[0040] Extract medium-term risk characteristic data including price volatility, cargo transportation delay rate and fund arrival delay days, and obtain preset benchmark values ​​for price volatility, cargo transportation delay rate and fund arrival delay days, and calculate the logarithmic ratio of each medium-term risk characteristic to its benchmark value;

[0041] The logarithmic ratios of each calculated value are summed to obtain a total. The total is divided by 3 to obtain the average value. The average value is then taken as a negative exponential function value to obtain the medium-term risk trend index.

[0042] The calculation process for the later-stage risk tendency index is as follows:

[0043] Extract post-trade risk characteristic data including the number of transaction disputes, product delivery quality pass rate, and accounts receivable collection cycle, and obtain preset benchmark values ​​for the number of transaction disputes, product delivery quality pass rate, and accounts receivable collection cycle, and calculate the ratio of each post-trade risk characteristic to its benchmark value;

[0044] Square each of the calculated ratios, sum all the squared results to get a sum, then take the cube root of the sum to obtain the later risk tendency index.

[0045] A further improvement to the technical solution of the present invention is that the risk warning module specifically includes:

[0046] The risk identification and risk trend analysis results of trading behavior are obtained from the risk trend analysis module. The identification and analysis results are then preliminarily sorted out to extract the early risk trend index, the mid-term risk trend index, and the late risk trend index.

[0047] Based on the risk trend index obtained at each stage, a risk assessment is conducted on the trading behavior, and corresponding risk warning thresholds are set for the early, middle and late stages of the trading behavior. The risk warning thresholds are set based on historical data, industry experience and user needs.

[0048] The system monitors the comparison between the risk trend index and the set risk warning threshold in real time. When the risk trend index exceeds the preset threshold for the corresponding stage, a warning signal is triggered, generating risk warning information that includes detailed transaction behavior information, risk characteristic description, and warning suggestions. The risk warning information is then sent to relevant users through preset notification channels such as SMS, email, or system messages, so that users can quickly take measures to deal with potential risks and reduce possible losses.

[0049] A further improvement to the technical solution of the present invention is that the countermeasure recommendation module specifically includes:

[0050] The response recommendation module receives risk warning information from the risk warning module, analyzes the warning information, and preliminarily determines the direction of recommended response measures based on the risk characteristics and stage in the warning information.

[0051] By combining transaction-related information and industry knowledge from the knowledge graph, the system analyzes the background, historical transaction records, and market conditions of both parties to the transaction, identifies the root causes and scope of impact of the risks, and selects appropriate coping strategies from a pre-established strategy library that match the current risk characteristics and stage of the transaction. The system then matches suitable coping strategies for different risks and transaction stages, and presents the recommendations to users in a clear and easy-to-understand manner.

[0052] A further improvement to the technical solution of the present invention is that the risk assessment report generation module specifically includes:

[0053] The risk assessment report generation module collects data from various modules of the system according to a set cycle, obtains risk assessment results before, during and after the transaction, acquires risk trend analysis data, organizes risk warning data, records warning time, content and trend, and then summarizes the recommended countermeasures and their implementation.

[0054] Based on the compiled risk warning data, and in accordance with the preset report template and format, a risk assessment report for cross-border bulk commodity e-commerce transactions is automatically generated.

[0055] The generated risk assessment reports will be distributed through pre-set channels such as email and system messages to ensure that relevant users can obtain the report content in a timely manner. At the same time, the risk assessment reports will be stored in the system's database for users to review later and compare and analyze historical data. The risk assessment reports will also be archived in chronological order to establish a complete risk assessment file.

[0056] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0057] 1. This invention provides a risk assessment and management system for cross-border bulk commodity e-commerce transactions. By constructing a risk identification model and combining risk knowledge and relationships in a knowledge graph, it identifies risks in the pre-, mid-, and post-transaction stages of the transaction. At the same time, it sets corresponding risk warning thresholds for different stages of the transaction to trigger warning signals, enabling enterprises to take measures before or in the early stages of risks, effectively avoid or mitigate losses caused by risks, and improve the enterprise's ability to respond to risks.

[0058] 2. This invention provides a risk assessment and management system for cross-border bulk commodity e-commerce transactions. Combining a pre-established response strategy library, based on the early warning information issued by the risk warning module, and combined with transaction-related party information and industry knowledge in the knowledge graph, it intelligently recommends appropriate response measures from the response strategy library. It can match suitable response strategies according to different risks and transaction stages, providing enterprises with reasonable decision support, improving the efficiency and accuracy of enterprises in dealing with risks, and reducing decision-making costs.

[0059] 3. This invention provides a risk assessment and management system for cross-border bulk commodity e-commerce transactions, which manages the entire process of risk assessment for cross-border bulk commodity e-commerce transactions. From pre-transaction risk assessment to real-time monitoring during the transaction and post-transaction summary and analysis, each link works closely together to ensure the comprehensiveness and continuity of risk management. The full-process management helps enterprises to discover and respond to potential risks in a timely manner, reduce risk losses, and improve the security and stability of transactions. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0061] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0062] Figure 2 This is a schematic diagram of the system functional modules of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Example 1, as Figure 1 , Figure 2 As shown, the present invention provides a risk assessment and management system for cross-border bulk commodity e-commerce transactions, including a transaction risk management center. The transaction risk management center is communicatively connected to a data acquisition and integration module, a knowledge graph module, a risk trend analysis module, a risk early warning module, a response measure recommendation module, and a risk assessment report generation module, wherein the modules are electrically connected to each other.

[0065] The Transaction Risk Management Center is responsible for coordinating communication and data interaction between various modules. It can receive basic transaction datasets from the data acquisition and integration module, send instructions to the knowledge graph module to update the knowledge graph content, receive risk identification and trend analysis results from the risk trend analysis module, control the risk warning module to issue warning signals, and integrate suggestions from the response recommendation module and content from the risk assessment report generation module. This enables full-process control of risk assessment for cross-border bulk commodity e-commerce transactions, ensuring close cooperation among all links and improving the coordination and efficiency of the entire system operation.

[0066] The data acquisition and integration module is used to collect data related to cross-border bulk commodity transactions from multiple data sources, including but not limited to order information, payment information, logistics information, supplier and buyer credit data, and market price fluctuation data. Data sources include cross-border e-commerce platforms, third-party payment platforms, logistics companies, and external market data. The module preprocesses and integrates the collected data to obtain a basic transaction dataset. It integrates multiple interfaces to collect data related to cross-border bulk commodity transactions from multiple data sources, including cross-border e-commerce platforms, third-party payment platforms, logistics companies, and external market data. Specifically, it retrieves order information from cross-border e-commerce platforms, covering product details, transaction time, and quantity; obtains payment information from third-party payment platforms, including payment method, amount, and status; and integrates data with logistics companies. The system integrates with the company to collect logistics information, including transportation routes and delivery status. Simultaneously, it obtains supplier and buyer credit data and market price fluctuation data from external market data sources and the internal credit assessment system, ensuring comprehensive data coverage of all aspects of cross-border bulk commodity transactions. The collected cross-border bulk commodity transaction-related data undergoes preprocessing, including data cleaning, outlier handling, and standardization. Data cleaning removes records with excessive duplicates, errors, and missing values. Standardization is performed on data of different formats to unify data formats. Outliers are identified and corrected, correcting data that significantly deviates from market prices. The preprocessed data of different types is then linked and integrated according to the transaction order number field, merging order information, payment information, logistics information, credit data, and market price fluctuation data to form a complete and coherent transaction record. Finally, through data mapping and transformation, a basic transaction dataset is constructed.

[0067] The knowledge graph module is used to construct a knowledge graph of transaction behavior, integrating various information involved in transactions, including suppliers, buyers, logistics providers, market conditions, and historical transaction records. It filters out risk characteristics related to transaction risk assessment, forming a risk characteristic sequence list. Basic transaction datasets are obtained from the data acquisition and integration module. Natural language processing technology is used to parse the text data involved. Combined with a rule engine, based on preset rule templates, entities in the basic transaction dataset are identified and extracted, including supplier names, buyer information, and logistics tracking numbers. Relationships between entities are established, integrating scattered data points into a structured information network. Simultaneously, attribute information is extracted from the data to construct a preliminary knowledge graph structure. Based on the actual needs of risk assessment in cross-border commodity transactions, risk characteristics relevant to transaction risk assessment were selected from the integrated information. The transaction process was divided into three stages: pre-transaction, mid-transaction, and post-transaction. Key risk characteristics for each stage were clearly defined. Pre-transaction risk characteristics include counterparty credit scores, historical default rates, and supply-demand change rates. Mid-transaction risk characteristics include price volatility, cargo transportation delay rates, and delays in fund arrival. Post-transaction risk characteristics include the number of transaction disputes, product delivery quality pass rates, and accounts receivable collection cycles. Counterparty credit scores directly reflect the credit reliability of counterparties; the lower the score, the greater the likelihood of counterparty default. The higher the transaction risk, the greater the risk. The historical transaction default rate is the proportion of transactions in which the counterparty defaulted within the past year in cross-border commodity e-commerce transactions. The supply and demand change rate is the magnitude of changes in the market supply and demand of the target commodity in the two months prior to the transaction. Supply and demand changes directly affect commodity price trends. A large supply and demand change rate indicates market instability and increased price volatility risk, thus affecting the profit expectations and risk level of the transaction. Price volatility is the magnitude of price fluctuations of the target commodity between adjacent days during the transaction. Price volatility reflects the degree of market price volatility; higher price volatility indicates greater market price instability, and the transacting parties may face greater price risk. The freight delay rate is the percentage of transactions where the actual delivery time exceeds the estimated delivery time. Freight delays affect delivery dates, and a higher delay rate indicates greater risk in the logistics process. The fund arrival delay days are calculated from the agreed payment date and represent the difference between the actual arrival time and the agreed-upon time. Fund arrival delays affect the smooth progress of transactions, and a longer delay indicates greater cash flow risk, potentially negatively impacting the transaction. The number of transaction disputes refers to the number of disputes between the buyer and seller after the transaction is completed, arising from issues such as product quality, quantity, and delivery time. A high number of disputes indicates potential problems with the transaction, potentially affecting future cooperation and the company's reputation.The product delivery quality pass rate is calculated as the ratio of qualified products to the total number of delivered products, indicating whether the quality of delivered bulk commodities meets the standards stipulated in the contract. A low pass rate means increased costs and losses for the company. The accounts receivable collection period is the time taken from the completion of a transaction to the actual collection of all accounts receivable, measured in days. A longer collection period increases capital occupation costs and bad debt risk, affecting the company's cash flow and financial condition. The selected risk characteristics are categorized and organized according to the early, middle, and late stages of the transaction, forming a risk characteristic sequence table. This table clearly lists the risk characteristics at each stage, assigning each risk characteristic a corresponding identifier and attribute description. The risk characteristic sequence table is then mapped onto a knowledge graph, forming a subgraph structure containing risk characteristic nodes and their relationships.

[0068] The risk trend analysis module is used to construct a risk identification model by combining the risk characteristics in the risk characteristic sequence table, to identify risks in the early, middle and late stages of trading behavior, and to analyze the risk trend of the trading behavior period based on the deviation of each risk characteristic from its respective preset benchmark value. The risk trend analysis module includes a risk identification model construction unit and a risk identification unit.

[0069] The risk identification model construction unit utilizes historical transaction data stored in the knowledge graph and combines it with risk features related to transaction risk assessment in the risk feature sequence table to construct a risk identification model based on a neural network model. It extracts historical transaction data related to transaction risk assessment from the knowledge graph and, based on the risk feature types covered in the risk feature sequence table, filters out risk feature data related to transaction risk assessment. The features are normalized, scaling numerical features to the range of 0 to 1 to eliminate dimensional differences. Simultaneously, based on user needs and historical transaction data, a baseline value for each risk feature is preset, and risk features deviating from the baseline value are marked, integrating them into a comprehensive feature dataset. According to the characteristics and needs of cross-border bulk commodity transaction risk assessment, a neural network architecture is selected to build a multilayer perceptron (MLP) network model, including an input layer, multiple hidden layers, and an output layer. The number of neurons in the input layer matches the number of extracted risk features, with each neuron corresponding to one risk feature. The processed feature data is input into the model. The hidden layer consists of several neurons, which are activated... The live function introduces nonlinear factors, enabling the model to learn complex combinations and relationships of risk features. Setting an appropriate number of hidden layer neurons increases the model's expressive power and flexibility. The connection weights and biases of the hidden layers are adjusted, and the backpropagation algorithm is used to update the model during training to adapt to the risk identification task, outputting risk features that deviate from the baseline. The prepared feature dataset is divided into training and validation sets. The neural network model is trained using the training set. Hyperparameters such as learning rate and iteration count are adjusted to optimize the training process. During training, the backpropagation algorithm is used to calculate the loss function, and network weights are adjusted to minimize the loss function value, allowing the model to learn the mapping relationship between risk features and transaction risks. During training, the validation set is used to validate the model, evaluating its performance on unseen data and preventing overfitting. By adjusting hyperparameters and optimizing the network structure, the model achieves good performance on the validation set, enabling it to accurately identify risks in cross-border commodity transactions until satisfactory performance is achieved, resulting in the final risk identification model.

[0070] The risk identification unit utilizes a constructed risk identification model to identify risks at different stages of a transaction, analyzes the deviation of each risk characteristic from its respective benchmark value, and calculates early-stage, mid-stage, and late-stage risk trend indices to clarify the risk trends at each stage of the transaction. Preprocessed risk characteristic data is input into the constructed risk identification model and compared with preset benchmark values ​​to identify risks in the current stage of the transaction, thus determining the presence of risk in that stage. In the early stages of a transaction, based on input early-stage risk characteristics including counterparty credit scores, historical transaction default rates, and supply-demand change rates, the unit compares these with preset early-stage risk characteristic benchmark values ​​to determine the early-stage risk that deviates from the benchmark values. The system identifies risk characteristics and calculates the early-stage risk trend index. In the middle stage of the transaction, it compares the input mid-stage risk characteristics, including price volatility, cargo transportation delay rate, and fund arrival delay days, with the preset mid-stage risk characteristic benchmark to determine the mid-stage risk characteristics that deviate from the benchmark and calculate the mid-stage risk trend index. In the later stage of the transaction, it compares the input late-stage risk characteristics, including the number of transaction disputes, product delivery quality pass rate, and accounts receivable collection cycle, with the preset late-stage risk characteristic benchmark to determine the late-stage risk characteristics that deviate from the benchmark and calculate the late-stage risk trend index. After the risk identification model completes the analysis, it obtains the risk trend analysis results of the current stage of the transaction and records the risk trend analysis results in the system's risk identification log, which is associated with the detailed information of the transaction.

[0071] Furthermore, the calculation process for the risk directional index in the early stages is as follows:

[0072] Extract the previous risk characteristic data, including counterparty credit score, historical transaction default rate and supply and demand change rate, and obtain the preset counterparty credit score benchmark value, historical transaction default rate benchmark value and supply and demand change rate benchmark value. Calculate the difference ratio between each previous risk characteristic and its benchmark value, square each difference ratio, add the square results to get a sum, take the square root of the sum and multiply by 1 / 3 to finally obtain the previous risk trend index.

[0073] The expression for the prior risk tendency index is:

[0074]

[0075] In the formula, R Q C is the risk trend index for the previous period. s For the current counterparty's credit score, C b，s H is the preset counterparty credit score benchmark value. d H represents the current historical transaction default rate. b，dS is the preset historical transaction default rate benchmark. r S represents the current rate of change in supply and demand. b，r As a preset benchmark value for the rate of change in supply and demand, when C s The closer to C b，s H d The closer to H b，d S r The closer to S b，r At that time, R Q A value approaching 0 indicates that the current transaction's risk trend in the previous period was similar to the benchmark situation, and the risk was relatively stable. s H d S r The greater the deviation from the corresponding benchmark value, the more R... Q An increase indicates a significant change in the previous risk trend, and a higher degree of risk deviating from the baseline situation;

[0076] The calculation process for the medium-term risk trend index is as follows:

[0077] Extract medium-term risk characteristic data including price volatility, cargo transportation delay rate, and fund arrival delay days, and obtain preset benchmark values ​​for price volatility, cargo transportation delay rate, and fund arrival delay days. Calculate the logarithmic ratio of each medium-term risk characteristic to its benchmark value, sum the calculated logarithmic ratios of each, divide the sum by 3 to obtain the average value, and then take the negative exponential function value of this average value to finally obtain the medium-term risk trend index.

[0078] The expression for the medium-term risk trend index is:

[0079]

[0080] In the formula, R Z P is a medium-term risk trend index. v Let P be the current price volatility. b，v G is the preset benchmark value for price volatility. d G represents the current freight transport delay rate. b，d F is the preset benchmark value for cargo transportation delay rate. a F represents the number of days the funds are delayed in arriving. b，a The preset base value for the number of days of delay in fund arrival is P. v >P b，v G d >G b，d F a >F b，a When price volatility, freight delay rate, and fund arrival delay days are all higher than the benchmark value, R Z A decrease in the value indicates that the medium-term risk is deviating significantly from the baseline, and the level of risk is increasing.

[0081] The calculation process for the later-stage risk tendency index is as follows:

[0082] Extract post-risk characteristic data including the number of transaction disputes, product delivery quality pass rate, and accounts receivable collection cycle, and obtain preset benchmark values ​​for the number of transaction disputes, product delivery quality pass rate, and accounts receivable collection cycle. Calculate the ratio of each post-risk characteristic to its benchmark value, square each calculated ratio, sum all the squared results to obtain a sum, and then take the cube root of the sum to finally obtain the post-risk trend index.

[0083] The expression for the later-stage risk tendency index is:

[0084]

[0085] In the formula, R H T is a risk directional index for the later stages. d T represents the current number of transaction disputes. b，d Q is a preset benchmark value for the number of transaction disputes. q Q represents the current product delivery quality pass rate. b，q A is the preset benchmark value for product delivery quality pass rate. c Given the current accounts receivable collection cycle, A b，c The pre-set accounts receivable collection period benchmark value, when T d The closer to T b，d Q q The closer to Q b，q A c The closer to A b，c At that time, R H A value approaching 0 indicates that the risk trend of the current transaction in the later stages is consistent with the benchmark situation, and the risk is relatively stable. d Q q A c The greater the deviation from the corresponding benchmark value, the more R... H An increase indicates a significant change in the trend of risk in the later stages, and a higher degree of risk.

[0086] The risk warning module is used to assess the risk of trading behavior based on the results of risk identification and risk trend analysis. It sets corresponding risk warning thresholds for different periods of trading behavior to trigger warning signals and issue risk warning information.

[0087] The response recommendation module is used to combine a pre-established response strategy library containing various risk response strategies, and based on the risk warning information issued by the risk warning module, combined with transaction stakeholder information and industry knowledge in the knowledge graph, to intelligently recommend appropriate response measures from the response strategy library;

[0088] The risk assessment report generation module is used to automatically generate risk assessment reports for cross-border bulk commodity e-commerce transactions according to a set cycle. These reports include risk assessment results before, during, and after the transaction, risk trend analysis, risk warnings, recommended countermeasures, and implementation status.

[0089] Example 2, as Figure 1 , Figure 2 As shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, the risk warning module specifically includes:

[0090] The system obtains risk identification and trend analysis results of trading behavior from the risk trend analysis module, and performs preliminary sorting of the identification and analysis results. It extracts the early-stage risk trend index, mid-stage risk trend index, and late-stage risk trend index. Based on the obtained risk trend indices for each stage, the system conducts a risk assessment of the trading behavior and sets corresponding risk warning thresholds for the early, mid, and late stages of the trading behavior. The risk warning thresholds are set based on historical data, industry experience, and user needs. The system monitors the comparison between the risk trend index and the set risk warning thresholds in real time. When the risk trend index exceeds the preset threshold for the corresponding stage, a warning signal is triggered, generating a risk warning message containing detailed information about the trading behavior, a description of risk characteristics, and warning suggestions. The risk warning message is then sent to relevant users through the system's preset notification channels, such as SMS, email, or system messages, so that users can quickly take measures to deal with potential risks and reduce possible losses.

[0091] The response recommendation module specifically includes:

[0092] The response recommendation module receives risk warning information from the risk warning module and analyzes it. Based on the risk characteristics and current stage in the warning information, it initially determines the direction of recommended response measures. Combining transaction-related party information and industry knowledge from the knowledge graph, it analyzes the background, historical transaction records, and market conditions of both parties to clarify the root cause and scope of impact of the risk. It then selects response measures that match the current risk characteristics and current stage from a pre-established response strategy library. For different risks and transaction stages, it matches appropriate response measures and presents the recommendation results to the user in a clear and easy-to-understand way. For example, for counterparties with high credit risk, it suggests providing guarantees or early payment; for price volatility risk, it suggests hedging or adjusting transaction terms.

[0093] The risk assessment report generation module specifically includes:

[0094] The risk assessment report generation module collects data from various modules of the system according to a set cycle, obtains risk assessment results before, during, and after the transaction, acquires risk trend analysis data, organizes risk warning data, records warning time, content, and trends, and summarizes recommended countermeasures and their implementation. Based on the organized risk warning data, and following a preset report template and format, it automatically generates a risk assessment report for cross-border bulk commodity e-commerce transactions. The report details the risk status before, during, and after the transaction, describes the risk trend analysis results through charts and text, records risk warning situations, including warning time, risk characteristics, and recommended measures, demonstrates the recommended countermeasures and their implementation effect evaluation, and proposes improvement suggestions. The generated risk assessment report is distributed through preset output channels such as email and system messages to ensure that relevant users can receive the report content in a timely manner. At the same time, the risk assessment report is stored in the system's database for users to review later and compare and analyze historical data. The risk assessment reports are archived in chronological order to establish a complete risk assessment archive.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A cross-border commodity e-commerce transaction risk assessment management system comprising a transaction risk management center, characterized in that: The transaction risk management center is in communication connection with a data collection and integration module, a knowledge graph module, a risk trend analysis module, a risk early warning module, a countermeasure recommendation module and a risk assessment report generation module, wherein the modules are in electrical signal connection; The data collection and integration module is configured to collect data related to cross-border commodity transactions from multiple data sources, and to preprocess and integrate the collected data to obtain a basic transaction data set; The knowledge graph module is configured to build a knowledge graph of transaction behavior, filter out risk features related to transaction risk assessment, and form a risk feature sequence list, specifically including: The basic transaction data set is obtained from the data collection and integration module, and natural language processing technology is used to analyze the text data involved therein, and in combination with a rule engine, entities in the basic transaction data set are identified and extracted according to a preset rule template, and the associated relationships between the entities are established, and the scattered data points are integrated into a structured information network, and at the same time, the attribute information in the data is extracted to construct a preliminary knowledge graph structure; According to the actual needs of cross-border commodity transaction risk assessment, risk features related to transaction risk assessment are selected from the integrated information, and the transaction behavior is divided into three periods of early, middle and late, and the key risk features of each stage of the early, middle and late periods of the transaction behavior are determined, wherein the early risk features of the transaction behavior include counterparty credit score, historical transaction default rate and supply and demand change rate, the middle risk features of the transaction behavior include price fluctuation rate, goods transportation delay rate and fund arrival delay days, and the late risk features of the transaction behavior include the number of transaction disputes, product delivery quality qualification rate and accounts receivable recovery cycle; The selected risk features are classified and arranged according to the early, middle and late periods of the transaction behavior to form a risk feature sequence list, each risk feature is given a corresponding identifier and attribute description, and the risk feature sequence list is mapped into the knowledge graph to form a subgraph structure containing risk feature nodes and associated relationships; The risk trend analysis module is configured to combine the risk features in the risk feature sequence list to construct a risk identification model, to identify the risks of the early, middle and late periods of the transaction behavior, and to analyze the risk trend of the period of the transaction behavior according to the deviation of each risk feature from the respective preset reference value; The risk early warning module is configured to assess the risk of the transaction behavior according to the results of risk identification and risk trend analysis, trigger an early warning signal, and issue risk early warning information; The countermeasure recommendation module is configured to combine a pre-established countermeasure strategy library, and intelligently recommend appropriate countermeasures from the countermeasure strategy library according to the risk early warning information; The risk assessment report generation module is configured to automatically generate a cross-border commodity e-commerce transaction risk assessment report.

2. The cross-border commodity e-commerce transaction risk assessment management system according to claim 1, characterized in that: The data collection and integration module specifically includes: The integrated multiple interfaces collect cross-border bulk commodity transaction related data from multiple data sources including cross-border e-commerce platforms, third-party payment platforms, logistics companies, and external market data, wherein order information is scraped from cross-border e-commerce platforms, payment information is obtained from third-party payment platforms, logistics information is collected by interfacing with logistics company systems, and supplier and buyer credit data, market price fluctuation data are obtained from external market data sources and internal credit evaluation systems; The collected cross-border bulk commodity transaction related data is preprocessed, including data cleaning, outlier processing and standardization processing; The preprocessed different types of data are associated and integrated according to the field of the transaction order number, the order information, payment information, logistics information, credit data and market price fluctuation data are fused to form complete and coherent transaction records, and the basic transaction data set is constructed through data mapping and conversion.

3. The cross-border commodity e-commerce transaction risk assessment management system according to claim 1, characterized in that: The risk trend analysis module includes a risk identification model construction unit and a risk identification unit. The risk identification model construction unit is configured to construct a risk identification model based on a neural network model by using historical transaction data stored in the knowledge graph and combining risk features related to transaction risk assessment in the risk feature list. The risk identification unit is configured to use the constructed risk identification model to identify risks in different periods of transaction behavior, analyze the deviation of each risk feature from the respective baseline value, calculate the early risk trend index, the medium-term risk trend index and the late risk trend index, and determine the risk trend of each period of transaction behavior.

4. The cross-border commodity e-commerce transaction risk assessment management system according to claim 3, characterized in that: The risk identification model construction unit specifically includes: Extract historical transaction data related to transaction risk assessment from the knowledge graph, filter out risk feature data related to transaction risk assessment according to the risk feature types covered by the risk feature list, normalize the features, and at the same time, preset the baseline values of each risk feature according to the user's needs and historical transaction data, mark the risk features deviating from the baseline values, and integrate them into a comprehensive feature data set; According to the characteristics and needs of cross-border bulk commodity transaction risk assessment, a multi-layer perceptron network model is built by selecting a neural network architecture, including an input layer, multiple hidden layers and an output layer; The prepared feature data set is divided into a training set and a validation set, the training set is used to train the built neural network model, and the validation set is used to verify the model, and the final risk identification model is obtained.

5. The cross-border commodity e-commerce transaction risk assessment management system according to claim 3, characterized in that: The risk identification unit specifically includes: The preprocessed risk feature data is input into the constructed risk identification model and compared with the preset baseline value to identify the risk of the current stage of transaction behavior, and the risk features deviating from the baseline value of the current stage are identified, i.e., the risk of the current stage of transaction behavior is determined; In the early stage of transaction behavior, the early risk features including the credit score of the transaction counterparty, the historical transaction default rate and the supply and demand change rate are compared with the preset baseline value of the early risk features to determine the early risk features deviating from the baseline value, and the early risk trend index is calculated. In the middle stage of the transaction behavior, according to the inputted middle stage risk characteristics including price fluctuation rate, goods transportation delay rate and fund arrival delay days, the middle stage risk characteristics deviating from the benchmark values of preset middle stage risk characteristics are determined, and then the middle stage risk trend index is calculated; In the late stage of the transaction behavior, according to the inputted late stage risk characteristics including transaction dispute quantity, product delivery quality qualified rate and accounts receivable recovery cycle, the late stage risk characteristics deviating from the benchmark values of preset late stage risk characteristics are determined, and then the late stage risk trend index is calculated; After the risk identification model completes the analysis, the risk trend analysis result of the current stage of the transaction behavior is obtained, and then the risk trend analysis result is recorded in the risk identification log of the system and is associated with the detailed information of the transaction behavior.

6. The cross-border commodity e-commerce transaction risk assessment management system according to claim 5, characterized in that: The calculation process of the early stage risk trend index is as follows: The early stage risk characteristic data including transaction counterparty credit score, historical transaction default rate and supply and demand change rate are extracted, and the preset transaction counterparty credit score benchmark value, historical transaction default rate benchmark value and supply and demand change rate benchmark value are obtained, and the difference ratios of each early stage risk characteristic and the corresponding benchmark value are calculated; Each of the calculated difference ratios is squared, then the square results are added to obtain a sum, and the square root of the sum is taken and multiplied by 1 / 3, and finally the early stage risk trend index is obtained; The calculation process of the middle stage risk trend index is as follows: The middle stage risk characteristic data including price fluctuation rate, goods transportation delay rate and fund arrival delay days are extracted, and the preset price fluctuation rate benchmark value, goods transportation delay rate benchmark value and fund arrival delay days benchmark value are obtained, and the logarithmic ratios of each middle stage risk characteristic and the corresponding benchmark value are calculated; Each of the calculated logarithmic ratios is added to obtain a total, and the total is divided by 3 to obtain an average value, and then the negative exponential function value of the average value is taken, and finally the middle stage risk trend index is obtained; The calculation process of the late stage risk trend index is as follows: The late stage risk characteristic data including transaction dispute quantity, product delivery quality qualified rate and accounts receivable recovery cycle are extracted, and the preset transaction dispute quantity benchmark value, product delivery quality qualified rate benchmark value and accounts receivable recovery cycle benchmark value are obtained, and the ratios of each late stage risk characteristic and the corresponding benchmark value are calculated; Each of the calculated ratios is squared, and all the square results are added to obtain a sum, and then the cubic root of the sum is taken, and finally the late stage risk trend index is obtained.

7. The cross-border commodity e-commerce transaction risk assessment management system according to claim 3, characterized in that: The risk early warning module specifically includes: The risk identification result and the risk trend analysis result of the transaction behavior are obtained from the risk trend analysis module, and the identification and analysis results are preliminarily sorted, and the early stage risk trend index, the middle stage risk trend index and the late stage risk trend index are extracted; According to the obtained risk trend indexes of each stage, the risk of the transaction behavior is evaluated, and corresponding risk early warning thresholds are set for the early stage, the middle stage and the late stage of the transaction behavior, respectively. The risk trend index is compared with a set risk warning threshold in real time, and when the risk trend index exceeds the preset threshold of the corresponding stage, a warning signal is triggered, risk warning information including detailed transaction behavior information, risk characteristic description and warning suggestions is generated, and the risk warning information is sent to relevant users through a notification channel preset by the system.

8. The cross-border commodity e-commerce transaction risk assessment management system according to claim 7, characterized in that: The countermeasure recommendation module specifically includes: The countermeasure recommendation module receives risk warning information from the risk warning module, analyzes the warning information, and preliminarily determines the direction of the recommended countermeasures according to the risk characteristics in the warning information and the stage; In combination with the transaction-related party information and industry knowledge in the knowledge graph, the backgrounds, historical transaction records and market quotations of the transaction parties are analyzed to clarify the root cause and influence range of the risk, and the countermeasures matched with the current risk characteristics and the stage are screened out from the pre-established countermeasure library, the matched countermeasures are matched for different risks and transaction stages, and then the recommended results are presented to the user.

9. The cross-border commodity e-commerce transaction risk assessment management system according to claim 1, characterized in that: The risk assessment report generation module specifically includes: The risk assessment report generation module collects data from each module of the system according to a set period, obtains risk assessment results in the pre-, middle- and post-transaction stages, obtains risk trend analysis data, sorts risk warning data, and then summarizes the countermeasure recommendation and execution situation; According to the sorted risk warning data, a cross-border bulk commodity e-commerce transaction risk assessment report is automatically generated according to a preset report template and format; The generated risk assessment report is distributed through the output channels of the system preset email and system message, and the risk assessment report is stored in the database of the system and archived in chronological order to establish a complete risk assessment file.

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