Intelligent risk control system and method for cross-border e-commerce digitization

Abstract

The invention discloses an intelligent risk control system and method for cross-border e-commerce digitization. Comprising an identity verification module, a transaction monitoring and abnormal behavior detection module, a credit assessment and risk quantification module, an anti-fraud and anti-money laundering check module, a compliance auditing module, a user behavior analysis module, a risk control rule engine module and a comprehensive risk assessment module. The problems of how to make a risk control system capable of meeting individual requirements and having cost benefits for small and micro enterprises and how to effectively evaluate and improve the ability of salesmen in the aspect of risk control are solved, and the overall risk control efficiency and accuracy are further improved. Therefore, personalized and intelligent setting of risk control is realized, a visual portrait of the ability of a salesman is provided, intelligent assessment and standard improvement of the ability of risk control personnel are promoted, risk control optimization is realized on the whole, the risk management level of cross-border e-commerce is improved, and enterprises can develop steadily in a complex and changeable international trade environment.

Description

Technical Field

[0001] The present invention relates to the field of cross-border e-commerce risk control technology, and in particular to a digital intelligent risk control system and method for cross-border e-commerce. Background Art

[0002] With the development of globalization and the popularization of the Internet, cross-border e-commerce transactions have shown a rapid growth trend. This provides consumers with more choices and more convenient shopping methods, and also provides merchants with a broader market and more business opportunities. However, cross-border e-commerce transactions also face a series of risks and challenges, especially for small and micro enterprises, which have limited funds, insufficient risk control experience, and difficulty in bearing the high cost of complex risk control systems, which puts them at a disadvantage in risk management. These enterprises tend to use traditional manual risk control methods, but this method seems to be unable to cope with the high complexity and rapid changes in the international market.

[0003] The primary technical contradiction is that small and micro enterprises need to achieve efficient risk control management with limited resources, but the existing risk control systems are generally complex in design and costly, making them difficult to tailor for small and micro enterprises. When using risk control systems, small and micro enterprises often need to face general solutions that do not match their actual business scenarios, which leads to inconvenience in actual operations and poor risk control effects.

[0004] Another technical contradiction arises in that salesmen of small and micro enterprises often aim to complete transactions and lack sufficient knowledge and skills in risk control. However, enterprises have limited methods for evaluating employees' business capabilities and risk control capabilities, and are unable to effectively identify and cultivate talents with high-level risk control skills.

[0005] How to ensure that the risk control system can run smoothly in the unique business environment of small and micro enterprises, while maintaining agility and adaptability in the ever-changing international market, is also an urgent problem to be solved. In addition, it is necessary to explore a method that allows management to generate intuitive portraits through the business data of salesmen, so as to efficiently grasp the performance and potential of each salesman to guide more accurate business decisions and talent training strategies. These technical issues are interrelated and all point to how to create a sustainable risk control environment for small and micro enterprises in the complex and ever-changing cross-border e-commerce market. Summary of the invention

[0006] In order to solve the above-mentioned technical problems, the present invention provides a cross-border e-commerce digital intelligent risk control system and method.

[0007] The technical solution of the present invention is achieved in this way:

[0008] A cross-border e-commerce digital intelligent risk control system, including an identity authentication module, a transaction monitoring and abnormal behavior detection module, a credit assessment and risk quantification module, an anti-fraud and anti-money laundering inspection module, a compliance review module, a user behavior analysis module, a risk control rule engine module and a comprehensive risk assessment module;

[0009] The identity verification module is used to implement knowledge verification, ownership verification and biometric verification to confirm the legitimacy and accuracy of the user's identity;

[0010] The transaction monitoring and abnormal behavior detection module is used to track user transaction activities in real time, detect abnormal behaviors such as sudden high-value transactions or frequent small-value transactions, trigger risk alerts and take corresponding measures; establish a model based on historical data, detect abnormal behaviors and communicate with users for secondary verification to increase transaction security;

[0011] The credit assessment and risk quantification module is used to perform credit assessment based on the user's historical transaction data and credit information, and use the credit scoring model to quantify the user's credit risk, provide a basis for risk management, adopt different risk control strategies for users with different credit levels, and improve transaction security;

[0012] The anti-fraud and anti-money laundering check module is used to check whether the user is on the international sanctions list, prevent fraud and money laundering in transactions, including circular transactions, and improve transaction security; verify whether the user's identity is legal through data comparison, prevent fraud, check the user's transaction background, prevent money laundering activities, and ensure compliance;

[0013] The compliance review module is used to automatically review whether the transaction complies with domestic and foreign laws and regulations on taxation, export control, and import restrictions to ensure transaction compliance; update regulatory changes in real time and adjust review standards according to business types to ensure transaction compliance;

[0014] The user behavior analysis module is used to build a user behavior analysis model, record and analyze the user's operation behavior on the website or application, identify abnormal behavior patterns, and improve risk identification capabilities; establish user behavior portraits, identify user habitual behaviors and abnormal behaviors, and improve risk identification accuracy;

[0015] The risk control rule engine module is used to automatically apply the rules formulated by company policies and regulations based on user behavior analysis and credit assessment results to determine whether further risk control processes are needed; trigger the risk control process when abnormal behavior is found to ensure the safety of transactions;

[0016] The comprehensive risk assessment module is used to comprehensively consider currency exchange risks, international logistics risks, and information on differences in laws and regulations to conduct a comprehensive risk assessment to ensure the security and compliance of cross-border transactions; dynamically adjust risk prevention and control strategies based on the assessment results to ensure the security and compliance of cross-border transactions.

[0017] Preferably, the identity verification module includes an implementation knowledge verification unit, a ownership verification unit and a biometric verification unit;

[0018] The implementation knowledge verification unit is used to require the user to provide specific personal information or answer security questions to confirm the identity; require the user to provide information such as account number, password, security questions, etc. to confirm the identity, set up an account lock mechanism, temporarily prohibit the user from logging in in the event of multiple failures, and provide two-factor authentication, requiring the user to provide additional verification information in addition to the password;

[0019] The ownership verification unit is used to authenticate the identity of a user through a specific item, device or information owned by the user; including verification code verification: sending a text message verification code or an email confirmation code to the user's registered mobile phone or mailbox; also includes hardware verification: using a USB key, smart card or other hardware device, the user must insert or connect these devices to their computer or mobile device to prove that they own these devices; also includes the use of digital signature technology, the user's private key signs data to prove identity;

[0020] The biometric verification unit is used to ensure the user's identity by using fingerprint recognition and facial recognition biometric verification technologies; integrating fingerprint recognition technology to verify the user's identity through fingerprints, using facial recognition technology to verify by scanning the user's facial image, exploring iris recognition, voiceprint recognition and other biometric verification technologies to improve the accuracy and security of identity verification.

[0021] Preferably, the transaction monitoring and abnormal behavior detection module includes a real-time monitoring unit, an abnormal behavior analysis unit and a secondary verification unit;

[0022] The real-time monitoring unit is used to monitor the frequency and amount of user transaction behavior, detect whether the user's login location is regular, prevent remote login, analyze user operation behavior patterns, and identify abnormal behaviors including frequent login and abnormal device login;

[0023] The abnormal behavior analysis unit is used to establish a behavior analysis model, identify abnormal behavior patterns based on historical data, set thresholds and rules, issue an alarm when user behavior exceeds the specified range, monitor user account fund changes and transfer records in real time, and discover abnormal transactions in a timely manner;

[0024] The secondary verification unit is used to send a text message verification code or an email confirmation code to the user, requesting confirmation of transaction intention, requiring the user to provide additional information or identity verification information, confirming the legitimacy of the account, using manual review, and manually intervening to confirm abnormal transactions or operations to ensure transaction security.

[0025] Preferably, the credit assessment and risk quantification module includes a user credit scoring unit and a risk quantification unit;

[0026] The user credit scoring unit is used to integrate user credit reports and historical transaction record data to establish a credit scoring model, evaluate users based on user repayment records and income indicators, and set credit score ranges corresponding to different credit levels to facilitate risk quantification;

[0027] The risk quantification unit is used to quantify the user's credit score into risk levels, including low risk, medium risk, and high risk, and set risk prevention and control strategies corresponding to different risk levels, including limiting transaction amounts and strengthening monitoring, and taking corresponding risk management measures according to the risk level to reduce transaction risks.

[0028] Preferably, the anti-fraud and anti-money laundering inspection module includes a data comparison and verification unit, an anti-money laundering detection unit and a compliance review unit;

[0029] The data comparison and verification unit is used to verify the authenticity of the personal information provided by the user through the KYC principle, compare the user information with the public database or third-party data source, confirm the legal identity, implement real-name authentication, and ensure that the information provided by the user is consistent with the actual identity;

[0030] The anti-money laundering detection unit is used to analyze user transaction behaviors, identify abnormal or suspicious transaction patterns, confirm the legitimacy of the counterparty's identity, prevent the inflow of illegal money, set rules according to anti-money laundering regulations, and conduct additional reviews of high-risk transactions;

[0031] The compliance audit unit is used to automatically review whether transactions comply with local and international regulations, including AML regulations and electronic transaction regulations, update regulatory changes in real time and adjust audit standards to ensure transaction compliance, initiate internal investigation procedures when suspicious transactions are found, and cooperate with relevant legal agencies to conduct compliance reviews.

[0032] Preferably, the comprehensive risk assessment module includes a currency exchange risk assessment unit, an international logistics risk assessment unit, a legal and regulatory risk assessment unit, a risk assessment report generation unit, a dynamic adjustment prevention and control strategy management unit, and a risk assessment result tracking and feedback unit;

[0033] The currency exchange risk assessment unit is used to assess the risks brought by the exchange of different currencies and formulate appropriate risk prevention and control measures;

[0034] The international logistics risk assessment unit is used to assess the risks existing in international logistics and ensure the safety of cargo transportation;

[0035] The legal and regulatory risk assessment unit is used to assess risks involving laws and regulations and ensure transaction compliance;

[0036] The risk assessment report generating unit is used to generate a report based on the comprehensive risk assessment results to provide a reference for management decisions;

[0037] The dynamic control strategy adjustment management unit is used to dynamically adjust the control strategy according to the risk assessment results to improve the risk prevention effect;

[0038] The risk assessment result tracking and feedback unit is used to track changes in risk assessment results and provide timely feedback to relevant departments to ensure transaction security and compliance.

[0039] An intelligent risk control method for cross-border e-commerce digitization includes the following steps:

[0040] S1. If the user attempts a transaction, customer identity verification is performed, including knowledge verification, ownership verification and biometric verification, through which the user's identity is confirmed;

[0041] S2. Based on the user's confirmed identity, the transaction monitoring system tracks their transaction activities in real time. If abnormal behavior is detected, including sudden high-value transactions or frequent small-value transactions, a risk alert will be triggered;

[0042] S3. When a risk alert is triggered, the system automatically retrieves the user's historical transaction data and credit information for credit assessment and quantifies the user's credit risk using the established credit scoring model;

[0043] S4. The system uses an anti-money laundering and anti-fraud module to check whether the user is on the international sanctions list and whether there are signs of fraud or money laundering in the transaction, including circular transactions;

[0044] S5. After obtaining the transaction details, the system conducts a compliance review and automatically analyzes whether the transaction complies with relevant domestic and foreign laws and regulations on taxation, export control and import restrictions;

[0045] S6. Through data analysis and risk modeling, the system builds a user behavior analysis model to record and analyze user operations on websites or applications to identify more detailed abnormal behavior patterns;

[0046] S7. Use a policy and rule engine to automatically apply rules set by company policies and regulations based on user behavior analysis results and credit assessment scores to determine whether further risk control processes are needed;

[0047] S8. If the system determines that a transaction has potential risks, it will assign a corresponding risk value score to the transaction and decide whether to accept the transaction, require further review, or reject the transaction directly based on the score;

[0048] S9. Taking into account the unique factors of cross-border transactions, the system conducts a comprehensive risk assessment through operational behavior analysis and risk value scoring, combined with currency exchange risk, international logistics risk, and information on differences in laws and regulations.

[0049] Preferably, S3 comprises the following sub-steps:

[0050] S301. Based on the credit history data provided by the borrower, a statistical analysis method is used to obtain the historical debt payment information, including the number of overdue payments and the overdue amount. Through these data, the borrower's past loan repayment behavior pattern and credit status are obtained;

[0051] S302. Using the borrower's credit account information, including account type, account age, number of credit inquiries, and number of settled and unsettled accounts, determine the borrower's credit account diversity and credit activity, and thus assess credit risk;

[0052] S303. Obtain the borrower's personal information, including age, marital status, and education level, and analyze this information to determine the borrower's social and economic stability and possible changes in repayment ability;

[0053] S304. Obtain the current debt burden level of the borrower and judge his / her ability to repay the loan in the future through the debt situation of the borrower, including the total debt amount, the proportion of different types of debt, and the ratio of debt to income;

[0054] S305. Use the borrower's asset data, including real estate and vehicle information, investment and savings account information, to obtain the borrower's asset status and assess their ability to provide collateral and their ability to cope with financial difficulties;

[0055] S306. Obtain the borrower's financial behavior data, including credit card usage habits, savings habits and investment risk tolerance, to determine the borrower's financial management ability and potential credit risk;

[0056] S307. By examining the borrower's employment status, the economic conditions of the region where he is located, and the economic factors of the industry, we can obtain the stability and expected changes of the borrower's income and analyze the extent to which his ability to repay the loan is affected by the external economic environment;

[0057] S308. Based on the borrower's consumption habits, payment habits and financial management capabilities, obtain the borrower's behavior pattern and assess the borrower's credit risk;

[0058] S309. Integrate the credit history, personal information, debt and asset status, financial behavior, economic factors and behavior pattern data obtained in S301-S308, and use credit scoring algorithms, including logistic regression, decision trees and neural networks, to calculate and generate the credit score of the borrower;

[0059] S310. Through the borrower's credit score, its credit risk level is judged to obtain the basis for loan approval, and the interest rate and credit limit of the credit product are determined to provide support for loan decision-making and credit management.

[0060] Preferably, S5 comprises the following sub-steps:

[0061] S501. Obtain the transaction ID based on the initial information of the transaction initiation;

[0062] S502. Generate an audit timestamp using the current time, record the specific time point when each transaction audit is completed, determine whether the transaction is audited within a reasonable time frame, and retain time evidence;

[0063] S503. Obtain relevant party information, including identity information, historical transaction behavior and risk assessment of both parties to the transaction, and obtain the necessary data basis for initial risk assessment;

[0064] S504. By applying the embedded audit rules, the automated compliance system analyzes whether the transaction behavior conforms to the normal pattern. The rules include transaction frequency, amount, transaction object and geographical location. After determining whether the transaction conforms to the rules, it makes a preliminary compliance judgment;

[0065] S505. After obtaining the transaction details, including the transaction amount, currency type, transaction date and transaction channel, the system further analyzes the specific circumstances of the transaction to determine whether the transaction is beyond the scope of normal business, including whether the amount is abnormal, whether the date is reasonable, and whether it is completed through an unusual channel;

[0066] S506. Based on the transaction behavior pattern and historical data, the system automatically evaluates the risk level; if the transaction differs greatly from previous behavior, or matches a known risk behavior pattern, the transaction is marked as high risk;

[0067] S507. After obtaining the risk level, determine whether additional risk control measures are needed;

[0068] S508. By comparing the transaction behavior with the abnormal indicators, determine whether the transaction has potential violation types, including money laundering and fraud; if the transaction behavior triggers one or some preset abnormal indicators, the system marks the transaction as non-compliant;

[0069] S509. After obtaining the results of the automated compliance review, if it is not passed, the system provides recommended actions, including further monitoring, freezing funds, and reporting to relevant regulatory agencies, to assist the compliance team to take immediate action when problems are found;

[0070] S510. Determine whether the transaction passes the automated compliance review based on the review status of the review result;

[0071] S511. If the result is that manual review is required, the transaction will be marked and transferred to the manual review queue to ensure that no abnormal transaction will be missed;

[0072] S512. Obtain audit process tracking information to ensure the transparency and traceability of the entire audit process, track each step of the operation, and ensure that all compliance decisions are clearly recorded and reasonably explained;

[0073] S513. Based on the audit results and all relevant information, the auditor or system adds comments to provide additional explanations for the audit decision or to explain special circumstances of the transaction.

[0074] Preferably, S6 comprises the following sub-steps:

[0075] S601. Obtain a preliminary user feature framework based on user identity information, including user ID, role, position, and department;

[0076] S602. Use timestamp and geographic location information collection tools to obtain context information, and obtain the context of user behavior by recording the user's access time, duration, access location, IP address and physical location;

[0077] S603. Obtain user access behavior data through access logs and user activity monitoring systems, including user access frequency, accessed systems or applications, data types, and access permission levels;

[0078] S604. Determine whether there are abnormal patterns in the user's access behavior, including high-frequency access to sensitive data or unauthorized access attempts;

[0079] S605. Obtain the operations performed by the user according to the user's interaction cloud behavior log, including downloading, uploading, editing and deleting. At the same time, track the input behavior and communication behavior to determine whether there is any abnormal instruction input or unusual information transmission mode;

[0080] S606. Use historical behavior analysis tools to obtain past access history and historical abnormal behavior data, and gain insights into user behavior changes by comparing current activities with past behavior patterns, and capture sudden behavior pattern changes or periodic changes;

[0081] S607. Obtain detailed information about data transmission through network traffic analysis and data monitoring systems, including data size, type, transmission destination, and encryption status;

[0082] S608. Determine whether there is any abnormality in data transmission, including the burst transmission of a large amount of sensitive data or an abnormal destination;

[0083] S609. Obtain the user's task relevance data based on the user's task assignment and role responsibilities, and analyze the user's behavioral purpose, including repeated access to specific data;

[0084] S610. Determine whether the user's behavior is consistent with their daily duties, thereby inferring potential malicious intent or misoperation;

[0085] S611. Use psychological analysis and social network analysis tools to obtain the user's psychological state and social relationships, and combine external intelligence and internal reports to determine the external influence or pressure that the user may be subject to, so as to identify suspicious behaviors triggered by social engineering;

[0086] S612. Using the data collected above, use machine learning model statistical analysis, cluster analysis or classification algorithm to obtain the pattern of normal behavior, and continuously improve the model through the algorithm training process to obtain a prediction model that can identify abnormal behavior;

[0087] S613. Based on the output of the user behavior prediction model, combined with business processes and security policies, determine whether the behavior is abnormal and decide whether to take further security measures, including alarming, restricting user access or further investigation.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] The present invention discloses a digital intelligent risk control system and method for cross-border e-commerce, comprising an identity authentication module, a transaction monitoring and abnormal behavior detection module, a credit assessment and risk quantification module, an anti-fraud and anti-money laundering inspection module, a compliance review module, a user behavior analysis module, a risk control rule engine module and a comprehensive risk assessment module. The technical problems to be solved include how to tailor a set of risk control systems for small and micro enterprises that can meet their personalized needs and are cost-effective, and how to effectively evaluate and improve the ability of salesmen in risk control, thereby improving the overall risk control efficiency and accuracy; therefore, the present invention provides a risk control solution suitable for small and micro enterprises, realizes personalized and intelligent settings of risk control, provides an intuitive portrait of the ability of salesmen, promotes intelligent evaluation and standard improvement of the ability of risk control personnel, realizes overall risk control optimization, improves the risk management level of cross-border e-commerce, and enables enterprises to develop steadily in a complex and changeable international trade environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a schematic diagram of the framework of a digital intelligent risk control system for cross-border e-commerce of the present invention;

[0091] Figure 2 This is the main flow chart of a digital intelligent risk control method for cross-border e-commerce of the present invention;

[0092] Figure 3 This is one of the sub-flow charts of a cross-border e-commerce digital intelligent risk control method of the present invention;

[0093] Figure 4 This is the second sub-flow chart of a cross-border e-commerce digital intelligent risk control method of the present invention;

[0094] Figure 5 This is the third sub-flow chart of the intelligent risk control method for digital cross-border e-commerce of the present invention. DETAILED DESCRIPTION

[0095] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices consistent with some aspects of the present disclosure as detailed in the appended claims.

[0096] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0097] Embodiment 1:

[0098] like Figure 1 As shown, an embodiment of the present invention provides a cross-border e-commerce digital intelligent risk control system, including an identity authentication module, a transaction monitoring and abnormal behavior detection module, a credit assessment and risk quantification module, an anti-fraud and anti-money laundering inspection module, a compliance review module, a user behavior analysis module, a risk control rule engine module and a comprehensive risk assessment module;

[0099] The identity verification module is used to implement knowledge verification, ownership verification and biometric verification to confirm the legitimacy and accuracy of the user's identity;

[0100] The transaction monitoring and abnormal behavior detection module is used to track user transaction activities in real time, detect abnormal behaviors such as sudden high-value transactions or frequent small-value transactions, trigger risk alerts and take corresponding measures;

[0101] The credit assessment and risk quantification module is used to perform credit assessment based on the user's historical transaction data and credit information, and use a credit scoring model to quantify the user's credit risk, providing a basis for risk management and control;

[0102] The anti-fraud and anti-money laundering check module is used to check whether the user is on the international sanctions list, prevent fraud and money laundering in transactions, including circular transactions, and improve transaction security;

[0103] The compliance review module is used to automatically review whether the transaction complies with domestic and foreign laws and regulations on taxation, export control, and import restrictions to ensure transaction compliance;

[0104] The user behavior analysis module is used to build a user behavior analysis model, record and analyze the user's operation behavior on the website or application, identify abnormal behavior patterns, and improve risk identification capabilities;

[0105] The risk control rule engine module is used to automatically apply the rules set by company policies and regulations based on user behavior analysis and credit assessment results to determine whether further risk control processes are needed;

[0106] The comprehensive risk assessment module is used to comprehensively consider currency exchange risks, international logistics risks, and information on differences in laws and regulations to conduct a comprehensive risk assessment to ensure the security and compliance of cross-border transactions.

[0107] Preferably, the identity verification module includes an implementation knowledge verification unit, a ownership verification unit and a biometric verification unit;

[0108] The implementation knowledge verification unit is used to require the user to provide specific personal information or answer security questions to confirm the identity;

[0109] The ownership verification unit is used to verify the identity of the user through a specific item, device or information owned by the user;

[0110] The biometric verification unit is used to ensure the user's identity using fingerprint recognition and facial recognition biometric verification technology.

[0111] Preferably, the transaction monitoring and abnormal behavior detection module includes a real-time monitoring unit, an abnormal behavior analysis unit and a secondary verification unit;

[0112] The real-time monitoring unit is used to monitor the frequency and amount of user transaction behavior, detect whether the user's login location is regular, prevent remote login, analyze user operation behavior patterns, and identify abnormal behaviors including frequent login and abnormal device login;

[0113] The abnormal behavior analysis unit is used to establish a behavior analysis model, identify abnormal behavior patterns based on historical data, set thresholds and rules, issue an alarm when user behavior exceeds the specified range, monitor user account fund changes and transfer records in real time, and discover abnormal transactions in a timely manner;

[0114] The secondary verification unit is used to send a text message verification code or an email confirmation code to the user, requesting confirmation of transaction intention, requiring the user to provide additional information or identity verification information, confirming the legitimacy of the account, using manual review, and manually intervening to confirm abnormal transactions or operations to ensure transaction security.

[0115] Preferably, the credit assessment and risk quantification module includes a user credit scoring unit and a risk quantification unit;

[0116] The user credit scoring unit is used to integrate user credit reports and historical transaction record data to establish a credit scoring model, evaluate users based on user repayment records and income indicators, and set credit score ranges corresponding to different credit levels to facilitate risk quantification;

[0117] The risk quantification unit is used to quantify the user's credit score into risk levels, including low risk, medium risk, and high risk, and set risk prevention and control strategies corresponding to different risk levels, including limiting transaction amounts and strengthening monitoring, and taking corresponding risk management measures according to the risk level to reduce transaction risks.

[0118] Preferably, the anti-fraud and anti-money laundering inspection module includes a data comparison and verification unit, an anti-money laundering detection unit and a compliance review unit;

[0119] The data comparison and verification unit is used to verify the authenticity of the personal information provided by the user through the KYC principle, compare the user information with the public database or third-party data source, confirm the legal identity, implement real-name authentication, and ensure that the information provided by the user is consistent with the actual identity;

[0120] The anti-money laundering detection unit is used to analyze user transaction behaviors, identify abnormal or suspicious transaction patterns, confirm the legitimacy of the counterparty's identity, prevent the inflow of illegal money, set rules according to anti-money laundering regulations, and conduct additional reviews of high-risk transactions;

[0121] The compliance audit unit is used to automatically review whether transactions comply with local and international regulations, including AML regulations and electronic transaction regulations, update regulatory changes in real time and adjust audit standards to ensure transaction compliance, initiate internal investigation procedures when suspicious transactions are found, and cooperate with relevant legal agencies to conduct compliance reviews.

[0122] Preferably, the compliance audit module includes an automatic audit system update unit, a regulatory change monitoring and update unit, a business type audit standard maintenance unit, an automatic audit process design unit, a log recording and audit unit, and an abnormal transaction processing mechanism unit;

[0123] The automatic audit system update is used to ensure that the audit system is consistent with the latest laws and regulations, avoiding compliance issues caused by changes in regulations;

[0124] The regulatory change monitoring and updating unit is used to monitor domestic and foreign regulatory changes in real time, update the review standards in a timely manner, and ensure that platform transactions comply with regulations;

[0125] The business type review standard maintenance unit is used to maintain specific review standards for different business types to ensure that all types of transactions comply with regulations;

[0126] The automatic audit process design unit is used to formulate an automatic audit process, improve audit efficiency, and reduce manual intervention;

[0127] The logging and auditing unit is used to record the operation logs during the audit process, facilitate audit tracing, and ensure compliance with the audit process;

[0128] The abnormal transaction processing mechanism unit is used to design a corresponding processing mechanism for abnormal transaction situations to ensure the compliance of the entire transaction process.

[0129] Preferably, the user behavior analysis module includes a user behavior data collection and storage unit, a behavior analysis algorithm design unit, an abnormal activity identification and risk warning unit, a user portrait establishment and maintenance unit, a behavior analysis report generation unit and a user operation log recording unit;

[0130] The user behavior data collection and storage unit is used to collect and store the user's operation behavior data on the platform to provide data support for subsequent analysis;

[0131] The behavior analysis algorithm design unit is used to design an analysis algorithm for user behavior and identify user habitual behavior and abnormal behavior;

[0132] The abnormal activity identification and risk warning unit is used to monitor user operation behaviors in real time through analysis algorithms, identify abnormal activities in a timely manner and issue risk warnings;

[0133] The user profile creation and maintenance unit is used to create a user behavior profile based on user behavior data to improve the accuracy of risk identification;

[0134] The behavior analysis report generating unit is used to generate a report based on the user behavior analysis results to provide a reference for risk control;

[0135] The user operation log recording unit is used to record the user's operation log on the platform for analyzing user behavior and tracking risks.

[0136] Preferably, the risk control rule engine module includes a user credit scoring system integration unit, a risk control rule formulation and update unit, a trigger risk control process design unit, a real-time risk monitoring and prompting unit, an automatic execution risk control strategy unit and a risk control rule engine log record;

[0137] The user credit scoring system integration unit is used to integrate the user credit scoring system with the platform risk control rule engine for risk assessment;

[0138] The risk control rule formulation and updating unit is used to formulate various risk control rules and update the rules in a timely manner according to actual conditions to improve the risk prevention and control effect;

[0139] The trigger risk control process design unit is used to design a risk control trigger mechanism to trigger the risk control process in a timely manner to ensure the safety of transactions;

[0140] The real-time risk monitoring and prompting unit is used to monitor user behavior and transaction status in real time, prompt risks in time, and reduce potential losses;

[0141] The automatic execution of risk control strategy unit is used to automatically execute corresponding control strategies according to risk control rules to ensure transaction security;

[0142] The risk control rule engine log recording unit is used to record logs during the execution of the risk control rule engine to facilitate analysis and tracing.

[0143] Preferably, the comprehensive risk assessment module includes a currency exchange risk assessment unit, an international logistics risk assessment unit, a legal and regulatory risk assessment unit, a risk assessment report generation unit, a dynamic adjustment prevention and control strategy management unit, and a risk assessment result tracking and feedback unit;

[0144] The currency exchange risk assessment unit is used to assess the risks brought by the exchange of different currencies and formulate appropriate risk prevention and control measures;

[0145] The international logistics risk assessment unit is used to assess the risks existing in international logistics and ensure the safety of cargo transportation;

[0146] The legal and regulatory risk assessment unit is used to assess risks involving laws and regulations and ensure transaction compliance;

[0147] The risk assessment report generating unit is used to generate a report based on the comprehensive risk assessment results to provide a reference for management decisions;

[0148] The dynamic control strategy adjustment management unit is used to dynamically adjust the control strategy according to the risk assessment results to improve the risk prevention effect;

[0149] The risk assessment result tracking and feedback unit is used to track changes in risk assessment results and provide timely feedback to relevant departments to ensure transaction security and compliance.

[0150] The embodiment of the present invention discloses a digital intelligent risk control system for cross-border e-commerce, including an identity authentication module, a transaction monitoring and abnormal behavior detection module, a credit assessment and risk quantification module, an anti-fraud and anti-money laundering inspection module, a compliance review module, a user behavior analysis module, a risk control rule engine module and a comprehensive risk assessment module. The technical problems to be solved include how to tailor a set of risk control systems for small and micro enterprises that can meet their personalized needs and are cost-effective, and how to effectively evaluate and improve the ability of salesmen in risk control, thereby improving the overall risk control efficiency and accuracy; therefore, the present invention provides a risk control solution suitable for small and micro enterprises, realizes personalized and intelligent settings of risk control, provides an intuitive portrait of the ability of salesmen, promotes intelligent evaluation and standard improvement of the ability of risk control personnel, realizes overall risk control optimization, improves the risk management level of cross-border e-commerce, and enables enterprises to develop steadily in a complex and changeable international trade environment.

[0151] Embodiment 2:

[0152] like Figure 2-5 As shown, an embodiment of the present invention provides a cross-border e-commerce digital intelligent risk control method, comprising the following steps:

[0153] S1. If the user attempts a transaction, customer identity verification is performed, including knowledge verification, ownership verification and biometric verification, through which the user's identity is confirmed;

[0154] S2. Based on the user's confirmed identity, the transaction monitoring system tracks their transaction activities in real time. If abnormal behavior is detected, including sudden high-value transactions or frequent small-value transactions, a risk alert will be triggered;

[0155] S3. When a risk alert is triggered, the system automatically retrieves the user's historical transaction data and credit information for credit assessment and quantifies the user's credit risk using the established credit scoring model;

[0156] S4. The system uses an anti-money laundering and anti-fraud module to check whether the user is on the international sanctions list and whether there are signs of fraud or money laundering in the transaction, including circular transactions;

[0157] S5. After obtaining the transaction details, the system conducts a compliance review and automatically analyzes whether the transaction complies with relevant domestic and foreign laws and regulations on taxation, export control and import restrictions;

[0158] S6. Through data analysis and risk modeling, the system builds a user behavior analysis model to record and analyze user operations on websites or applications to identify more detailed abnormal behavior patterns;

[0159] S7. Use a policy and rule engine to automatically apply rules set by company policies and regulations based on user behavior analysis results and credit assessment scores to determine whether further risk control processes are needed;

[0160] S8. If the system determines that a transaction has potential risks, it will assign a corresponding risk value score to the transaction and decide whether to accept the transaction, require further review, or reject the transaction directly based on the score;

[0161] S9. Taking into account the unique factors of cross-border transactions, the system conducts a comprehensive risk assessment through operational behavior analysis and risk value scoring, combined with currency exchange risk, international logistics risk, and information on differences in laws and regulations.

[0162] The specific working process of the embodiment of the present invention is as follows:

[0163] A user attempts to make a transaction worth $10,000. The system first confirms the user's identity through multiple authentication methods such as knowledge verification, ownership verification, and biometric verification. After these verifications, the transaction monitoring system tracks the transaction in real time and finds that the user's previous average transaction amount is usually $500. Therefore, the system detects this unusual high-value transaction and triggers a risk alert. After the risk alert is triggered, the system automatically retrieves the user's transaction history data for the past 12 months and finds that the user's credit score is 720 points (out of a total of 850 points). Using the credit scoring model, the system quantifies the user's credit risk and calculates the user's credit risk coefficient as 3 (the coefficient ranges from 0 to 1, and the lower the risk, the lower the risk). At the same time, the system uses the Anti-Money Laundering (AML) and Anti-Fraud modules to check whether the user appears on the international sanctions list and analyze whether the transaction pattern is consistent with money laundering behavior. For example, the system finds that the user has made multiple small transactions of $200 between two accounts in a short period of time, showing the suspicion of circular transactions. After obtaining the transaction details, the system conducts a compliance review and automatically analyzes whether the transaction complies with The system analyzes the tax regulations and finds that the transaction may involve a cross-border tariff of 20%, that is, a tax of US$2,000 is required to comply with relevant international trade regulations. Through data analysis and risk modeling, the system builds a user behavior analysis model and records that the average login time of users on the website is 5 minutes and the average number of pages browsed at a time is 20 pages. The system analysis finds that the user's login time before this transaction was 2 minutes and the number of pages browsed was only 3 pages, which is significantly different from the normal mode. Based on the user behavior analysis results and credit score, the policy and rule engine determines that the transaction requires further risk control process. The system assigns a risk value score of 75 points to the transaction (the full score is 100 points, and the higher the score, the greater the risk). Finally, considering the unique factors of cross-border transactions, such as the currency exchange risk assessment, the current exchange rate fluctuation is ±3%, and the international logistics risk assessment shows that the delay risk is 10%. Combined with the analysis of differences in laws and regulations, the system conducts a comprehensive risk assessment and finally marks a comprehensive risk coefficient of 65 for the transaction (the closer the coefficient is to 1, the higher the risk). Therefore, the system decides to put the transaction in a pending review state and wait for manual review.

[0164] Preferably, S3 comprises the following sub-steps:

[0165] S301. Based on the credit history data provided by the borrower, a statistical analysis method is used to obtain the historical debt payment information, including the number of overdue payments and the overdue amount. Through these data, the borrower's past loan repayment behavior pattern and credit status are obtained;

[0166] S302. Using the borrower's credit account information, including account type, account age, number of credit inquiries, and number of settled and unsettled accounts, determine the borrower's credit account diversity and credit activity, and thus assess credit risk;

[0167] S303. Obtain the borrower's personal information, including age, marital status, and education level, and analyze this information to determine the borrower's social and economic stability and possible changes in repayment ability;

[0168] S304. Obtain the current debt burden level of the borrower and judge his / her ability to repay the loan in the future through the debt situation of the borrower, including the total debt amount, the proportion of different types of debt, and the ratio of debt to income;

[0169] S305. Use the borrower's asset data, including real estate and vehicle information, investment and savings account information, to obtain the borrower's asset status and assess their ability to provide collateral and their ability to cope with financial difficulties;

[0170] S306. Obtain the borrower's financial behavior data, including credit card usage habits, savings habits and investment risk tolerance, to determine the borrower's financial management ability and potential credit risk;

[0171] S307. By examining the borrower's employment status, the economic conditions of the region where he is located, and the economic factors of the industry, we can obtain the stability and expected changes of the borrower's income and analyze the extent to which his ability to repay the loan is affected by the external economic environment;

[0172] S308. Based on the borrower's consumption habits, payment habits and financial management capabilities, obtain the borrower's behavior pattern and assess the borrower's credit risk;

[0173] S309. Integrate the credit history, personal information, debt and asset status, financial behavior, economic factors and behavior pattern data obtained in S301-S308, and use credit scoring algorithms, including logistic regression, decision trees and neural networks, to calculate and generate the credit score of the borrower;

[0174] S310. Through the borrower's credit score, its credit risk level is judged to obtain the basis for loan approval, and the interest rate and credit limit of the credit product are determined to provide support for loan decision-making and credit management.

[0175] For example: Collect historical debt payment data from credit reports, including "number of overdue payments" and "overdue amount"; for example, if a borrower has three overdue payment records in the past two years, with a total overdue amount of $500, these are important factors in assessing credit risk; use credit account information to determine credit account diversity and activity; the borrower may have 3 credit card accounts (credit card, student loan, car loan), with an average account age of 5 years, 2 credit inquiries in the past year, 1 settled account, and 2 unsettled accounts; analyze personal information, such as a borrower who is 35 years old, married, and has a college degree; this information indicates that the borrower may be socially and economically stable; by analyzing the borrower's debt situation, such as a total debt of $30,000, 50% of which is credit card debt, 30% of which is car loans, and 20% of which is student loans, and an annual income of $60,000, with a debt-to-income ratio of 5; assess asset status, such as a borrower owning a house worth $200,000 The borrower has no property, no vehicle, an investment account balance of $10,000, and a savings account balance of $5,000. Financial behavior data shows that the borrower's monthly credit card expenses account for 30% of his income, and he has regular deposits but no investment behavior, indicating that he prefers low-risk investments. The borrower works in the financial industry, has a relatively stable income, and lives in a city with stable economic growth, all of which help to assess the stability of his income. Spending habit analysis may show that the borrower usually uses credit cards to pay for most of his daily expenses and repays his credit card debts in full and on time, showing good financial management skills. Use a logistic regression model to calculate the borrower's credit score with the above data as input variables. For example, by analyzing historical data, we can determine the weight of each variable on the risk of default and build a model accordingly. Based on the calculated credit score, assuming the score range is 300-850, the borrower scores 700, which is a low credit risk level. Banks may offer lower loan interest rates and set higher credit limits for borrowers based on this score.

[0176] Preferably, S5 comprises the following sub-steps:

[0177] S501. Obtain the transaction ID based on the initial information of the transaction initiation;

[0178] S502. Generate an audit timestamp using the current time, record the specific time point when each transaction audit is completed, determine whether the transaction is audited within a reasonable time frame, and retain time evidence;

[0179] S503. Obtain relevant party information, including identity information, historical transaction behavior and risk assessment of both parties to the transaction, and obtain the necessary data basis for initial risk assessment;

[0180] S504. By applying the embedded audit rules, the automated compliance system analyzes whether the transaction behavior conforms to the normal pattern. The rules include transaction frequency, amount, transaction object and geographical location. After determining whether the transaction conforms to the rules, it makes a preliminary compliance judgment;

[0181] S505. After obtaining the transaction details, including the transaction amount, currency type, transaction date and transaction channel, the system further analyzes the specific circumstances of the transaction to determine whether the transaction is beyond the scope of normal business, including whether the amount is abnormal, whether the date is reasonable, and whether it is completed through an unusual channel;

[0182] S506. Based on the transaction behavior pattern and historical data, the system automatically evaluates the risk level; if the transaction differs greatly from previous behavior, or matches a known risk behavior pattern, the transaction is marked as high risk;

[0183] S507. After obtaining the risk level, determine whether additional risk control measures are needed;

[0184] S508. By comparing the transaction behavior with the abnormal indicators, determine whether the transaction has potential violation types, including money laundering and fraud; if the transaction behavior triggers one or some preset abnormal indicators, the system marks the transaction as non-compliant;

[0185] S509. After obtaining the results of the automated compliance review, if it is not passed, the system provides recommended actions, including further monitoring, freezing funds, and reporting to relevant regulatory agencies, to assist the compliance team to take immediate action when problems are found;

[0186] S510. Determine whether the transaction passes the automated compliance review based on the review status of the review result;

[0187] S511. If the result is that manual review is required, the transaction will be marked and transferred to the manual review queue to ensure that no abnormal transaction will be missed;

[0188] S512. Obtain audit process tracking information to ensure the transparency and traceability of the entire audit process, track each step of the operation, and ensure that all compliance decisions are clearly recorded and reasonably explained;

[0189] S513. Based on the audit results and all relevant information, the auditor or system adds comments to provide additional explanations for the audit decision or to explain special circumstances of the transaction.

[0190] For example: the system uses the SHA-256 hash algorithm combined with the unique attributes of the transaction (such as transaction time, transaction amount and participant ID) to generate the transaction ID "e3b0c44298fc1c149afbf4c8996fb92427ae41855"; the system generates a timestamp based on Coordinated Universal Time (UTC), such as "2023-04-01T10:15:00Z"; the system retrieves the KYC (Know Your Customer) database of the transaction participants, such as using Thomson Reuters World-Check or LexisNexis Risk Solutions to assess customer risk; the system uses algorithms such as Anomaly Detection Algorithm (ADA) to identify unusual transaction patterns, such as if the transaction frequency exceeds 3 standard deviations (3-sigma If the transaction amount is $10,000, the currency type is USD, the transaction date is 2023-04-01, and the transaction channel is SWIFT, the system may use a risk scoring model, such as FICO Score, to assign a score of 0-100 to the transaction, for example, a score of 85 or above indicates high risk. The system sets a rule that if the transaction amount exceeds 10 times the average customer transaction and the destination is a high-risk country, the system will mark it as a potential money laundering behavior. If the system detects non-compliant behavior, such as a transaction triggering a preset "money laundering indicator", the system may recommend setting the transaction status to "Suspicious" and notify the compliance department. If the transaction requires manual review, the system may use a labeling system, such as "Review". Required" label to add it to the manual review queue; the system records the user ID and timestamp of each operation, for example, "User123" completed the risk assessment step at "2023-04-01T10:20:00Z"; the auditor adds notes to the system, such as "the transaction exceeds the average customer transaction volume and the destination is a country on the regulatory list, recommending further investigation; in reality, the specific implementation and names of these steps and technologies will vary depending on the compliance software and internal processes used; in practice, companies may use multiple data sources, risk assessment tools, and monitoring systems to ensure transaction compliance, such as FICO, SAS FraudManagement, ACI Worldwide, etc.

[0191] Preferably, S6 comprises the following sub-steps:

[0192] S601. Obtain a preliminary user feature framework based on user identity information, including user ID, role, position, and department;

[0193] S602. Use timestamp and geographic location information collection tools to obtain context information, and obtain the context of user behavior by recording the user's access time, duration, access location, IP address and physical location;

[0194] S603. Obtain user access behavior data through access logs and user activity monitoring systems, including user access frequency, accessed systems or applications, data types, and access permission levels;

[0195] S604. Determine whether there are abnormal patterns in the user's access behavior, including high-frequency access to sensitive data or unauthorized access attempts;

[0196] S605. Obtain the operations performed by the user according to the user's interaction cloud behavior log, including downloading, uploading, editing and deleting. At the same time, track the input behavior and communication behavior to determine whether there is any abnormal instruction input or unusual information transmission mode;

[0197] S606. Use historical behavior analysis tools to obtain past access history and historical abnormal behavior data, and gain insights into user behavior changes by comparing current activities with past behavior patterns, and capture sudden behavior pattern changes or periodic changes;

[0198] S607. Obtain detailed information about data transmission through network traffic analysis and data monitoring systems, including data size, type, transmission destination, and encryption status;

[0199] S608. Determine whether there is any abnormality in data transmission, including the burst transmission of a large amount of sensitive data or an abnormal destination;

[0200] S609. Obtain the user's task relevance data based on the user's task assignment and role responsibilities, and analyze the user's behavioral purpose, including repeated access to specific data;

[0201] S610. Determine whether the user's behavior is consistent with their daily duties, thereby inferring potential malicious intent or misoperation;

[0202] S611. Use psychological analysis and social network analysis tools to obtain the user's psychological state and social relationships, and combine external intelligence and internal reports to determine the external influence or pressure that the user may be subject to, so as to identify suspicious behaviors triggered by social engineering;

[0203] S612. Using the data collected above, use machine learning model statistical analysis, cluster analysis or classification algorithm to obtain the pattern of normal behavior, and continuously improve the model through the algorithm training process to obtain a prediction model that can identify abnormal behavior;

[0204] S613. Based on the output of the user behavior prediction model, combined with business processes and security policies, determine whether the behavior is abnormal and decide whether to take further security measures, including alarming, restricting user access or further investigation.

[0205] For example: Use identity management systems such as LDAP (Lightweight Directory Access Protocol) or Active Directory to obtain basic information about users; Use GPS tracking or Wi-Fi positioning technology to record the user's physical location; Use NTP (Network Time Protocol) servers to synchronize device timestamps; Use SIEM (Security Information and Event Management) tools such as Splunk or LogRhythm to collect and analyze access logs; Use DLP (Data Loss Prevention) solutions such as Symantec DLP to monitor and record file transfer activities; Introduce User and Entity Behavior Analysis (UEBA) tools such as Exabeam or Gurucul to identify behaviors that are inconsistent with historical patterns; Use network traffic monitoring software such as Wireshark to capture and analyze data packets; Track user task completion through project management tools such as JIRA or Asana; Use social network analysis tools such as NodeXL to analyze employee interaction patterns on social media; Use psychological assessment tools such as DISC personality assessment to understand the psychological state of employees; Apply clustering algorithms such as K-means to identify patterns of normal behavior; Use classification algorithms such as Random Forest (Random Forest) or support vector machine (SVM) to train abnormal behavior recognition models; establish rule engines, such as Drools, combined with business processes to automatically trigger security alerts; when applying these tools and technologies, specific thresholds and parameters need to be set. For example, a UEBA system may be configured to issue an alert when a user attempts to log in unsuccessfully more than 10 times within 5 minutes. Similarly, network traffic analysis may set a threshold, such as a single IP address within 60 seconds The data transmission volume exceeds 100MB, which is marked as suspicious activity.

[0206] The foregoing examples are merely illustrative and are used to explain some features of the method of the present invention. The appended claims are intended to claim the widest possible range that can be conceived, and the embodiments presented herein are only illustrations of selected implementations according to the combination of all possible embodiments. Therefore, it is the applicant's intention that the appended claims are not limited by the selection of examples that illustrate the features of the present invention. Some numerical ranges used in the claims also include sub-ranges within them, and changes in these ranges should also be interpreted as being covered by the appended claims where possible. It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data and displayed data, etc.) involved in this application are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

Claims

1. A digital intelligent risk control system for cross-border e-commerce, characterized by: It includes identity authentication module, transaction monitoring and abnormal behavior detection module, credit assessment and risk quantification module, anti-fraud and anti-money laundering inspection module, compliance review module, user behavior analysis module, risk control rule engine module and comprehensive risk assessment module; The identity verification module is used to implement knowledge verification, ownership verification and biometric verification to confirm the legitimacy and accuracy of the user's identity; The transaction monitoring and abnormal behavior detection module is used to track user transaction activities in real time, detect abnormal behaviors such as sudden high-value transactions or frequent small-value transactions, trigger risk alerts and take corresponding measures; establish a model based on historical data, detect abnormal behaviors and communicate with users for secondary verification to increase transaction security; The credit assessment and risk quantification module is used to perform credit assessment based on the user's historical transaction data and credit information, and use the credit scoring model to quantify the user's credit risk, provide a basis for risk management, adopt different risk control strategies for users with different credit levels, and improve transaction security; The anti-fraud and anti-money laundering check module is used to check whether the user is on the international sanctions list, prevent fraud and money laundering in transactions, including circular transactions, and improve transaction security; verify whether the user's identity is legal through data comparison, prevent fraud, check the user's transaction background, prevent money laundering activities, and ensure compliance; The compliance review module is used to automatically review whether the transaction complies with domestic and foreign laws and regulations on taxation, export control, and import restrictions to ensure transaction compliance; update regulatory changes in real time and adjust review standards according to business types to ensure transaction compliance; The user behavior analysis module is used to build a user behavior analysis model, record and analyze the user's operation behavior on the website or application, identify abnormal behavior patterns, and improve risk identification capabilities; establish user behavior portraits, identify user habitual behaviors and abnormal behaviors, and improve risk identification accuracy; The risk control rule engine module is used to automatically apply the rules formulated by company policies and regulations based on user behavior analysis and credit assessment results to determine whether further risk control processes are needed; trigger the risk control process when abnormal behavior is found to ensure the safety of transactions; The comprehensive risk assessment module is used to comprehensively consider currency exchange risks, international logistics risks, and information on differences in laws and regulations to conduct a comprehensive risk assessment to ensure the security and compliance of cross-border transactions; dynamically adjust risk prevention and control strategies based on the assessment results to ensure the security and compliance of cross-border transactions.

2. According to claim 1, a cross-border e-commerce digital intelligent risk control system is characterized by: The identity verification module includes an implementation knowledge verification unit, a ownership verification unit and a biometric verification unit; The implementation knowledge verification unit is used to require the user to provide specific personal information or answer security questions to confirm the identity; require the user to provide account number, password, and security question information to confirm the identity, set up an account lock mechanism, temporarily prohibit the user from logging in in the event of multiple failures, and provide two-factor authentication, requiring the user to provide additional verification information in addition to the password; The ownership verification unit is used to verify the identity of the user through a specific item, device or information owned by the user; This includes verification code verification: sending a text message verification code or email confirmation code to the user's registered mobile phone or mailbox; hardware verification: using USB keys, smart cards or other hardware devices, users must insert or connect these devices to their computers or mobile devices to prove that they own these devices; and using digital signature technology, where users' private keys sign data to prove their identity; The biometric verification unit is used to ensure the user's identity by using fingerprint recognition and facial recognition biometric verification technologies; integrating fingerprint recognition technology to verify the user's identity through fingerprints, using facial recognition technology to verify by scanning the user's facial image, exploring iris recognition, voiceprint recognition and other biometric verification technologies to improve the accuracy and security of identity verification.

3. According to claim 2, a cross-border e-commerce digital intelligent risk control system is characterized by: The transaction monitoring and abnormal behavior detection module includes a real-time monitoring unit, an abnormal behavior analysis unit and a secondary verification unit; The real-time monitoring unit is used to monitor the frequency and amount of user transaction behavior, detect whether the user's login location is regular, prevent remote login, analyze user operation behavior patterns, and identify abnormal behaviors including frequent login and abnormal device login; The abnormal behavior analysis unit is used to establish a behavior analysis model, identify abnormal behavior patterns based on historical data, set thresholds and rules, issue an alarm when user behavior exceeds the specified range, monitor user account fund changes and transfer records in real time, and discover abnormal transactions in a timely manner; The secondary verification unit is used to send a text message verification code or an email confirmation code to the user, requesting confirmation of transaction intention, requiring the user to provide additional information or identity verification information, confirming the legitimacy of the account, using manual review, and manually intervening to confirm abnormal transactions or operations to ensure transaction security.

4. According to claim 3, a cross-border e-commerce digital intelligent risk control system is characterized by: The credit assessment and risk quantification module includes a user credit scoring unit and a risk quantification unit; The user credit scoring unit is used to integrate user credit reports and historical transaction record data to establish a credit scoring model, evaluate users based on user repayment records and income indicators, and set credit score ranges corresponding to different credit levels to facilitate risk quantification; The risk quantification unit is used to quantify the user's credit score into risk levels, including low risk, medium risk, and high risk, and set risk prevention and control strategies corresponding to different risk levels, including limiting transaction amounts and strengthening monitoring, and taking corresponding risk management measures according to the risk level to reduce transaction risks.

5. According to claim 4, a cross-border e-commerce digital intelligent risk control system is characterized by: The anti-fraud and anti-money laundering inspection module includes a data comparison and verification unit, an anti-money laundering detection unit and a compliance review unit; The data comparison and verification unit is used to verify the authenticity of the personal information provided by the user through the KYC principle, compare the user information with the public database or third-party data source, confirm the legal identity, implement real-name authentication, and ensure that the information provided by the user is consistent with the actual identity; The anti-money laundering detection unit is used to analyze user transaction behaviors, identify abnormal or suspicious transaction patterns, confirm the legitimacy of the counterparty's identity, prevent the inflow of illegal money, set rules according to anti-money laundering regulations, and conduct additional reviews of high-risk transactions; The compliance audit unit is used to automatically review whether transactions comply with local and international regulations, including AML regulations and electronic transaction regulations, update regulatory changes in real time and adjust audit standards to ensure transaction compliance, initiate internal investigation procedures when suspicious transactions are found, and cooperate with relevant legal agencies to conduct compliance reviews.

6. According to claim 5, a cross-border e-commerce digital intelligent risk control system is characterized by: The comprehensive risk assessment module includes a currency exchange risk assessment unit, an international logistics risk assessment unit, a legal and regulatory risk assessment unit, a risk assessment report generation unit, a dynamic adjustment prevention and control strategy management unit, and a risk assessment result tracking and feedback unit; The currency exchange risk assessment unit is used to assess the risks brought by the exchange of different currencies and formulate appropriate risk prevention and control measures; The international logistics risk assessment unit is used to assess the risks existing in international logistics and ensure the safety of cargo transportation; The legal and regulatory risk assessment unit is used to assess risks involving laws and regulations and ensure transaction compliance; The risk assessment report generating unit is used to generate a report based on the comprehensive risk assessment results to provide a reference for management decisions; The dynamic control strategy adjustment management unit is used to dynamically adjust the control strategy according to the risk assessment results to improve the risk prevention effect; The risk assessment result tracking and feedback unit is used to track changes in risk assessment results and provide timely feedback to relevant departments to ensure transaction security and compliance.

7. An intelligent risk control method for cross-border e-commerce digitization, characterized by: The following steps are involved: S1. If the user attempts a transaction, customer identity verification is performed, including knowledge verification, ownership verification and biometric verification, through which the user's identity is confirmed; S2. Based on the user's confirmed identity, the transaction monitoring system tracks their transaction activities in real time. If abnormal behavior is detected, including sudden high-value transactions or frequent small-value transactions, a risk alert will be triggered; S3. When a risk alert is triggered, the system automatically retrieves the user's historical transaction data and credit information for credit assessment and quantifies the user's credit risk using the established credit scoring model; S4. The system uses an anti-money laundering and anti-fraud module to check whether the user is on the international sanctions list and whether there are signs of fraud or money laundering in the transaction, including circular transactions; S5. After obtaining the transaction details, the system conducts a compliance review and automatically analyzes whether the transaction complies with relevant domestic and foreign laws and regulations on taxation, export control and import restrictions; S6. Through data analysis and risk modeling, the system builds a user behavior analysis model to record and analyze user operations on websites or applications to identify more detailed abnormal behavior patterns; S7. Use a policy and rule engine to automatically apply rules set by company policies and regulations based on user behavior analysis results and credit assessment scores to determine whether further risk control processes are needed; S8. If the system determines that a transaction has potential risks, it will assign a corresponding risk value score to the transaction and decide whether to accept the transaction, require further review, or reject the transaction directly based on the score; S9. Taking into account the unique factors of cross-border transactions, the system conducts a comprehensive risk assessment through operational behavior analysis and risk value scoring, combined with currency exchange risk, international logistics risk, and information on differences in laws and regulations.

8. According to claim 7, a digital intelligent risk control method for cross-border e-commerce is characterized by: The S3 comprises the following sub-steps: S301. Based on the credit history data provided by the borrower, a statistical analysis method is used to obtain the historical debt payment information, including the number of overdue payments and the overdue amount. Through these data, the borrower's past loan repayment behavior pattern and credit status are obtained; S302. Using the borrower's credit account information, including account type, account age, number of credit inquiries, and number of settled and unsettled accounts, determine the borrower's credit account diversity and credit activity, and thus assess credit risk; S303. Obtain the borrower's personal information, including age, marital status, and education level, and analyze this information to determine the borrower's social and economic stability and possible changes in repayment ability; S304. Obtain the current debt burden level of the borrower and judge his / her ability to repay the loan in the future through the debt situation of the borrower, including the total debt amount, the proportion of different types of debt, and the ratio of debt to income; S305. Use the borrower's asset data, including real estate and vehicle information, investment and savings account information, to obtain the borrower's asset status and assess their ability to provide collateral and their ability to cope with financial difficulties; S306. Obtain the borrower's financial behavior data, including credit card usage habits, savings habits and investment risk tolerance, to determine the borrower's financial management ability and potential credit risk; S307. By examining the borrower's employment status, the economic conditions of the region where he is located, and the economic factors of the industry, we can obtain the stability and expected changes of the borrower's income and analyze the extent to which his ability to repay the loan is affected by the external economic environment; S308. Based on the borrower's consumption habits, payment habits and financial management capabilities, obtain the borrower's behavior pattern and assess the borrower's credit risk; S309. Integrate the credit history, personal information, debt and asset status, financial behavior, economic factors and behavior pattern data obtained in S301-S308, and use credit scoring algorithms, including logistic regression, decision trees and neural networks, to calculate and generate the credit score of the borrower; S310. Through the borrower's credit score, its credit risk level is judged to obtain the basis for loan approval, and the interest rate and credit limit of the credit product are determined to provide support for loan decision-making and credit management.

9. According to claim 8, a digital intelligent risk control method for cross-border e-commerce is characterized by: The S5 comprises the following sub-steps: S501. Obtain the transaction ID based on the initial information of the transaction initiation; S502. Generate an audit timestamp using the current time, record the specific time point when each transaction audit is completed, determine whether the transaction is audited within a reasonable time frame, and retain time evidence; S503. Obtain relevant party information, including identity information, historical transaction behavior and risk assessment of both parties to the transaction, and obtain the necessary data basis for initial risk assessment; S504. By applying the embedded audit rules, the automated compliance system analyzes whether the transaction behavior conforms to the normal pattern. The rules include transaction frequency, amount, transaction object and geographical location. After determining whether the transaction conforms to the rules, it makes a preliminary compliance judgment; S505. After obtaining the transaction details, including the transaction amount, currency type, transaction date and transaction channel, the system further analyzes the specific circumstances of the transaction to determine whether the transaction is beyond the scope of normal business, including whether the amount is abnormal, whether the date is reasonable, and whether it is completed through an unusual channel; S506. Based on the transaction behavior pattern and historical data, the system automatically evaluates the risk level; If a transaction deviates significantly from past behavior or matches a known pattern of risky behavior, the transaction is marked as high risk; S507. After obtaining the risk level, determine whether additional risk control measures are needed; S508. By comparing the transaction behavior with the abnormal indicators, determine whether the transaction has potential violation types, including money laundering and fraud; if the transaction behavior triggers one or some preset abnormal indicators, the system marks the transaction as non-compliant; S509. After obtaining the results of the automated compliance review, if it is not passed, the system provides recommended actions, including further monitoring, freezing funds, and reporting to relevant regulatory agencies, to assist the compliance team to take immediate action when problems are found; S510. Determine whether the transaction passes the automated compliance review based on the review status of the review result; S511. If the result is that manual review is required, the transaction will be marked and transferred to the manual review queue to ensure that no abnormal transaction will be missed; S512. Obtain audit process tracking information to ensure the transparency and traceability of the entire audit process, track each step of the operation, and ensure that all compliance decisions are clearly recorded and reasonably explained; S513. Based on the audit results and all relevant information, the auditor or system adds comments to provide additional explanations for the audit decision or to explain special circumstances of the transaction.

10. The cross-border e-commerce digital intelligent risk control method according to claim 9, characterized in that: The S6 comprises the following sub-steps: S601. Obtain a preliminary user feature framework based on user identity information, including user ID, role, position, and department; S602. Use timestamp and geographic location information collection tools to obtain context information, and obtain the context of user behavior by recording the user's access time, duration, access location, IP address and physical location; S603. Obtain user access behavior data through access logs and user activity monitoring systems, including user access frequency, accessed systems or applications, data types, and access permission levels; S604. Determine whether there are abnormal patterns in the user's access behavior, including high-frequency access to sensitive data or unauthorized access attempts; S605. Obtain the operations performed by the user according to the user's interaction cloud behavior log, including downloading, uploading, editing and deleting. At the same time, track the input behavior and communication behavior to determine whether there is any abnormal instruction input or unusual information transmission mode; S606. Use historical behavior analysis tools to obtain past access history and historical abnormal behavior data, and gain insights into user behavior changes by comparing current activities with past behavior patterns, and capture sudden behavior pattern changes or periodic changes; S607. Obtain detailed information about data transmission through network traffic analysis and data monitoring systems, including data size, type, transmission destination, and encryption status; S608. Determine whether there is any abnormality in data transmission, including the burst transmission of a large amount of sensitive data or an abnormal destination; S609. Obtain the user's task relevance data based on the user's task assignment and role responsibilities, and analyze the user's behavioral purpose, including repeated access to specific data; S610. Determine whether the user's behavior is consistent with their daily duties, thereby inferring potential malicious intent or misoperation; S611. Use psychological analysis and social network analysis tools to obtain the user's psychological state and social relationships, and combine external intelligence and internal reports to determine the external influence or pressure that the user may be subject to, so as to identify suspicious behaviors triggered by social engineering; S612. Using the data collected above, use machine learning model statistical analysis, cluster analysis or classification algorithm to obtain the pattern of normal behavior, and continuously improve the model through the algorithm training process to obtain a prediction model that can identify abnormal behavior; S613. Based on the output of the user behavior prediction model, combined with business processes and security policies, determine whether the behavior is abnormal and decide whether to take further security measures, including alarming, restricting user access or further investigation.

Images