A financial data security verification system

Abstract

The present invention relates to the field of financial data security technology, and discloses a financial data security verification system, which includes a data receiving module, a feature analysis module, a pattern recognition module, an indicator calculation module, a parameter adjustment module, and a verification execution module. The data receiving module obtains a financial transaction data set and constructs a multidimensional verification model; the feature analysis module extracts abnormal features to generate a transaction risk feature table; the pattern recognition module decomposes transaction records to identify transaction nodes and factors; the indicator calculation module generates a transaction link credibility assessment value; the parameter adjustment module dynamically updates the transaction risk feature table; and the verification execution module triggers transaction permission operations based on the optimization table. The system is also provided with units such as transaction comparison and time window collection, and has a three-level response mechanism. This system improves the security of financial transactions and the efficiency of verification through multidimensional analysis, dynamic adjustment, and refined response, and is suitable for various financial data security management scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data security, and in particular to a financial data security verification system. Background Art

[0002] Against the backdrop of the rapid development of the digital economy, the security of financial data and the reliability of transactions have become core concerns for businesses and individuals. With the continuous innovation of financial technology, financial transaction scenarios are becoming increasingly complex, with transaction channels diversifying, such as online payments, cross-border transactions, and electronic invoices. At the same time, account types are also becoming increasingly diverse, including corporate accounts, personal savings accounts, and credit card accounts. While these changes improve transaction convenience, they also bring increasingly severe security challenges, and traditional financial data verification methods are gradually exposing numerous flaws.

[0003] Traditional verification systems are mostly based on single-dimensional rule-based settings, such as determining risk solely based on transaction amount thresholds or historical transaction frequency, lacking a multi-dimensional analysis of transaction behavior. Such simplistic rule engines struggle to effectively identify complex, abnormal transaction patterns, such as high-frequency, small-value transfers leading to cumulative cash withdrawals, cross-account linked transactions for money laundering, and other emerging financial crimes. Furthermore, traditional systems' rigid parameter adjustment mechanisms prevent them from dynamically optimizing verification strategies based on real-time transaction data. This results in delayed responses to emerging risks and is prone to misjudgments or omissions. For example, when a new type of fraudulent transaction emerges, traditional systems may be unable to effectively intercept it due to a lack of timely verification metrics, posing a threat to user asset security.

[0004] When it comes to transaction chain analysis, traditional systems lack the ability to deeply explore the connections between transaction nodes. By examining each transaction in isolation and ignoring information such as timing, amount, and account relationships, they struggle to uncover hidden risk chains within complex transaction networks. For example, multiple seemingly independent, low-frequency transactions may form an abnormal capital flow chain through related transaction factors. Traditional systems, unable to identify such connections, can easily overlook such risky transactions.

[0005] At the same time, with the increase in unstructured transaction data, such as natural language instructions and image-based transaction vouchers, traditional systems have insufficient parsing capabilities when processing this data. They are unable to effectively extract key operational elements, which affects the accuracy and efficiency of the verification process. Furthermore, traditional systems lack the flexibility to adapt policies to different transaction scenarios. They are unable to dynamically adjust verification strategies based on changes in the transaction environment (such as operating devices and network IP addresses), making it difficult to meet diverse security needs.

[0006] Furthermore, the existing financial data verification system also has shortcomings in its risk response mechanisms. There's a lack of refined, tiered response strategies for varying degrees of risk, often employing a one-size-fits-all approach. Either all transactions are cleared, leading to potential risks, or all transactions are blocked, impacting normal transaction efficiency. For example, minor anomalous transactions may be directly frozen, causing inconvenience to users, while more serious risky transactions may not be subject to sufficiently stringent measures, leading to escalating risks. Summary of the Invention

[0007] The purpose of the present invention is to provide a financial data security verification system to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solutions: a financial data security verification system, the method comprising:

[0009] The data receiving module is used to obtain the financial transaction data set to be verified and build a multi-dimensional verification model based on account type and transaction channel;

[0010] A feature analysis module, configured to extract abnormal operation features from historical transaction records and generate a transaction risk feature table based on the multi-dimensional verification model, wherein the transaction risk feature table includes verification indicators and authorization parameter correction values;

[0011] a pattern recognition module, configured to decompose the historical transaction records, identify high-frequency transaction nodes and low-frequency transaction nodes, and classify associated transaction factors and independent transaction factors;

[0012] An indicator calculation module, configured to generate a credibility evaluation value of a transaction link based on the associated transaction factor and the independent transaction factor;

[0013] a parameter adjustment module, configured to determine a dynamic adjustment gradient of the transaction risk feature table according to the credibility evaluation value, and update the transaction risk feature table based on the dynamic adjustment gradient to form an optimized transaction risk feature table;

[0014] The verification execution module is used to trigger the blocking or releasing operation of the account transaction authority according to the optimized transaction risk characteristic table.

[0015] Preferably, the system further comprises:

[0016] The transaction comparison unit is used to match the operation type of the current transaction request with the historical transaction record to determine whether the two belong to the same source transaction;

[0017] The time window collection unit is used to record the initiation timestamp of the current request and the completion timestamp of the previous transaction if it is determined to be a same-source transaction;

[0018] A frequency analysis unit is used to calculate the overlap of the time windows of two transactions and compare it with a preset frequency threshold to determine whether the current transaction is allowed to be executed;

[0019] a transaction interception unit, configured to terminate transaction execution and generate a high-frequency operation log if the time window overlap exceeds the preset frequency threshold;

[0020] The permission determination unit is used to allocate an independent verification channel to process the current transaction request if the operation type matching results are inconsistent.

[0021] Preferably, the pattern recognition module includes:

[0022] Arrange all transaction nodes according to transaction time sequence to generate an analysis link, randomly select adjacent transaction node pairs, obtain corresponding transaction amount readings and subsequent transaction amount readings, and calculate the absolute value of the change rate of the amount readings;

[0023] Determine the peak and baseline values ​​of the total transaction data and calculate the overall fluctuation range of the amount;

[0024] Calculating the ratio of the absolute value of the rate of change to the fluctuation range;

[0025] Calculating the transaction correlation deviation between the transaction node pairs;

[0026] Identify the maximum and minimum correlation among all nodes and calculate the global correlation difference range;

[0027] Calculating a ratio of the transaction correlation deviation to the difference range;

[0028] Normalizing the amount ratio and the correlation ratio to obtain a real-time credibility parameter;

[0029] Traverse the remaining transaction node pairs to generate a set of associated transaction factors, and calculate the mean of all real-time credibility parameters as the independent transaction factor.

[0030] Preferably, the indicator calculation module includes:

[0031] Setting safety thresholds and risk thresholds for related-party transaction factors;

[0032] Screening a set of compliant transactions based on the security threshold and screening a set of suspicious transactions based on the risk threshold;

[0033] Calculate the positive deviation between compliant transactions and the security threshold, and arrange them in ascending order of deviation to form a security sorting queue;

[0034] Calculate the negative deviation between the suspicious transactions and the risk threshold, and sort them in descending order of the deviation values ​​to form a risk ranking queue;

[0035] The safe queue and risk queue elements are matched item by item to form credibility pairs, and the credibility evaluation value is calculated based on all credibility pairs.

[0036] Preferably, the indicator calculation module further includes:

[0037] The effective number of statistical credibility pairs is calculated, and the absolute value of the difference between the safety element and the risk element in each group is calculated;

[0038] Identify the median and range of all absolute differences and calculate the distribution equilibrium parameter of all absolute differences;

[0039] The credibility evaluation value is calculated based on the median value, the range value and the distribution balance parameter.

[0040] Preferably, the parameter adjustment module includes:

[0041] Set primary trust thresholds and advanced trust thresholds;

[0042] Configure basic adjustment coefficients, intermediate adjustment coefficients, and enhanced adjustment coefficients;

[0043] The basic adjustment coefficient is enabled when the credibility evaluation value is higher than the advanced credibility threshold, the intermediate adjustment coefficient is enabled when the credibility evaluation value is between the primary and advanced credibility thresholds, and the enhanced adjustment coefficient is enabled when the credibility evaluation value is lower than the primary credibility threshold.

[0044] Preferably, the parameter adjustment module further includes:

[0045] Multiplying the dynamic adjustment gradient by the authorization parameter correction value to generate a final authorization parameter;

[0046] Matching preset permission mapping rules to determine the expected verification indicators corresponding to the final authorization parameters;

[0047] The final authorization parameters and expected verification indicators are integrated to construct the optimized transaction risk feature table.

[0048] Preferably, the data receiving module further includes:

[0049] Real-time integration of transaction flow data, account attribute data, and operating environment data to build a transaction behavior feature map;

[0050] Calculate the abnormal boundary of transaction behavior based on the transaction behavior feature map, and activate the secondary verification process when it is detected that the transaction characteristics exceed the safety boundary;

[0051] Generate risk / compliance matrices for different transaction scenarios through association rule mining, and generate the optimal verification strategy set.

[0052] Preferably, the system divides the response mechanism into three levels according to the degree of abnormal boundary violation: the first level triggers the adjustment of account transaction limit, the second level starts cross-channel transaction verification, and the third level freezes the account fund flow and sends a manual review instruction.

[0053] Preferably, the transaction comparison unit includes: parsing key operating elements in unstructured transaction instructions through text feature extraction technology, storing vectorized templates of historical transaction conflict events and their handling strategies, and when a transaction conflict is detected, retrieving an adaptive handling solution from a policy library through pattern matching, and generating a verification priority adjustment instruction.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] At the data processing level, the data receiving module integrates transaction flow, account attributes, and operating environment data to construct a characteristic map of transaction behavior, capable of capturing abnormal boundaries of transaction behavior in real time. This multi-dimensional data integration approach breaks through the limitations of traditional single-dimensional verification, enabling the system to comprehensively assess transaction risks based on multiple dimensions, including transaction amount, time, channel, and device. For example, if the device IP address of a transaction is detected to be inconsistent with the account's usual login location, and the transaction amount exceeds the historical average, the system will activate a secondary verification process, effectively preventing risks caused by device theft or account information leakage.

[0056] The combination of the feature analysis module and the pattern recognition module enables in-depth mining of historical transaction data. By extracting abnormal operation characteristics and identifying high- and low-frequency transaction nodes, the system can accurately distinguish between correlated and independent transaction factors, thereby generating a transaction risk profile. This ability to analyze transaction correlations enables the system to uncover risk links that are difficult to detect using traditional methods, such as the cumulative transfer of funds through high-frequency, small-value transactions or the use of low-frequency transactions for money laundering. For example, by analyzing the rate of change and correlation deviation between adjacent nodes in a transaction time series, the system can identify seemingly independent but actually connected anomalous transaction combinations, providing timely warnings of potential risks.

[0057] The indicator calculation module categorizes, filters, and sorts transaction data by setting security and risk thresholds to generate a credibility assessment. This quantitative assessment method enables the system to objectively and accurately determine the risk level of a transaction, avoiding the subjectivity and arbitrariness of human judgment. Furthermore, by statistically analyzing the absolute differences between credibility pairs and calculating distribution equilibrium parameters, the system can further refine the assessment dimensions and enhance the reliability of the results. For example, the smaller the positive deviation between a compliant transaction and the security threshold, the safer the transaction, and the system will prioritize its approval. Conversely, the larger the negative deviation between a suspicious transaction and the risk threshold, the higher the risk, and the system will implement more stringent verification measures.

[0058] The dynamic adjustment mechanism of the parameter adjustment module is a major innovation of this invention. By setting different levels of trust thresholds and configuring corresponding adjustment coefficients, the system can automatically adjust the verification indicators and authorization parameters of the transaction risk feature table based on the trustworthiness assessment value. This dynamic optimization capability enables the system to adapt to changes in the trading environment in real time and respond to emerging risks in a timely manner. For example, when the trustworthiness assessment value is higher than the advanced trust threshold, it indicates that the current trading environment is safe. The system will activate the basic adjustment coefficient, appropriately relax the verification conditions, and improve transaction efficiency. When the trustworthiness assessment value is lower than the primary trust threshold, the system will activate the enhanced adjustment coefficient, tighten the verification indicators, and strengthen risk prevention and control.

[0059] New modules, such as the transaction comparison unit and the time window collection unit, further enhance the system's risk prevention and control capabilities. The transaction comparison unit parses unstructured transaction instructions using text feature extraction technology, effectively processing complex data such as natural language instructions and improving verification accuracy. The time window collection unit and frequency analysis unit calculate the overlap of transaction time windows to identify high-frequency operational anomalies, such as multiple initiations of homologous transactions within a short period of time. This allows for timely interception of potential malicious operations and generates logs, providing a basis for subsequent audits. The authority determination unit allocates independent verification channels to ensure that transactions with mismatched operation types are specifically handled to avoid risk omissions.

[0060] The establishment of a three-tiered response mechanism enables refined management of risk events. Depending on the severity of the abnormality breach, the system triggers different levels of response measures, including account transaction limit adjustments, cross-channel transaction verification, freezing account fund flows, and issuing manual review instructions. This effectively mitigates risks while avoiding the impact of excessive interception on normal transactions. For example, for minor abnormal transactions, the system only adjusts transaction limits and reminds users to pay attention to account security. For serious risky transactions, the system immediately freezes fund flows and initiates manual review, maximizing the security of user assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a working principle diagram of the financial data security verification system of the present invention;

[0062] Figure 2 Design diagram for transaction matching and frequency control;

[0063] Figure 3 A design diagram of the processing flow of the pattern recognition module;

[0064] Figure 4 It is a design diagram of the processing flow of the indicator calculation module;

[0065] Figure 5 This is the expanded functional diagram of the data receiving module. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0067] See also Figure 1-Figure 5 The present invention relates to a financial data security verification system, and the specific implementation steps are as follows:

[0068] The system includes a data receiving module, a feature analysis module, a pattern recognition module, an indicator calculation module, a parameter adjustment module and a verification execution module. Each module works together to achieve the security verification of financial data.

[0069] The Data Receiving Module acquires the financial transaction dataset to be verified and constructs a multi-dimensional verification model based on account type (e.g., corporate account, personal account) and transaction channel (e.g., online banking, mobile payment, over-the-counter). For example, different verification dimension weights are assigned to large-value transfers in corporate accounts and small, high-frequency purchases in personal accounts. This module integrates transaction flow data, account attribute data (e.g., account balance, transaction history, and frequency), and operating environment data (e.g., IP address, device model) in real time to construct a transaction behavior feature map. This map calculates the boundaries of transaction behavior anomalies and activates a secondary verification process when transaction characteristics are detected to exceed safety boundaries. Furthermore, through association rule mining, a risk / compliance matrix is ​​generated for different transaction scenarios, forming an optimal set of verification strategies.

[0070] The feature analysis module extracts unusual operational characteristics from historical transaction records, such as large transfers during off-hours and high-frequency transactions logged in from remote IP addresses. This module, combined with a multi-dimensional verification model, generates a transaction risk feature table containing verification indicators (such as transaction amount thresholds and time interval thresholds) and authorization parameter correction values ​​(such as permission level adjustment coefficients). For example, if historical data shows a high fraud rate for large transfers during off-hours for a certain account type, the authorization parameter correction value for that period in the transaction risk feature table will be lowered to improve the stringency of verification indicators.

[0071] The pattern recognition module decomposes historical transaction records and arranges all transaction nodes in a time series to generate an analysis chain. It randomly selects adjacent transaction node pairs, obtains the corresponding transaction amount readings and subsequent transaction amount readings, and calculates the absolute value of the rate of change in the amount readings. It then determines the peak and baseline amounts in the full transaction data and calculates the overall amount fluctuation range. The module then calculates the ratio of the absolute value of the rate of change to the fluctuation range. It also calculates the transaction correlation deviation between transaction node pairs, identifies the maximum and minimum correlations across all nodes, and determines the global correlation variance range. The ratio of the transaction correlation deviation to the variance range is then calculated. The amount ratio and correlation ratio are normalized to obtain a real-time credibility parameter. The module then traverses the remaining transaction node pairs to generate a set of correlated transaction factors. The mean of all real-time credibility parameters is calculated as the independent transaction factor. This process identifies high-frequency transaction nodes (e.g., nodes with dense daily transactions during fixed periods) and low-frequency transaction nodes (e.g., nodes with large quarterly transfers). It then categorizes correlated transaction factors (e.g., pairs of nodes with minimal consecutive fluctuations in transaction amount) and independent transaction factors (e.g., isolated nodes with abnormal amounts).

[0072] Indicator Calculation Module: Generates a credibility assessment value for the transaction chain based on the associated transaction factor and the independent transaction factor. Sets security and risk thresholds for the associated transaction factor, filters the set of compliant transactions based on the security threshold, and filters the set of suspicious transactions based on the risk threshold. Calculates the positive deviation of compliant transactions from the security threshold and arranges them in ascending order of deviation to form a security ranking queue. Calculates the negative deviation of suspicious transactions from the risk threshold and arranges them in descending order of deviation to form a risk ranking queue. Matches each element of the security queue with the risk queue to form credibility pairs. Counts the number of valid credibility pairs, calculates the absolute difference between the security element and the risk element in each group, identifies the median and range of all absolute difference values, and calculates the distribution balance parameter for all absolute difference values. Finally, calculates the credibility assessment value based on the median, range, distribution balance parameter, and credibility pairs.

[0073] Parameter Adjustment Module: Determines the dynamic adjustment gradient of the transaction risk profile based on the credibility assessment value. Sets primary and advanced credibility thresholds, and configures basic, intermediate, and enhanced adjustment coefficients. The basic adjustment coefficient is enabled when the credibility assessment value is above the advanced credibility threshold; the intermediate adjustment coefficient is enabled when the credibility assessment value is between the primary and advanced credibility thresholds; and the enhanced adjustment coefficient is enabled when the credibility assessment value is below the primary credibility threshold. The dynamic adjustment gradient is multiplied by the authorization parameter correction value to generate the final authorization parameter. The final authorization parameter is then matched against the preset permission mapping rules to determine the expected verification indicator corresponding to the final authorization parameter. The final authorization parameter and the expected verification indicator are then integrated to construct an optimized transaction risk profile.

[0074] Verification and Execution Module: This module triggers the blocking or release of account trading permissions based on the optimized transaction risk profile. For example, if the optimized verification indicators indicate that the risk level of a transaction exceeds the preset threshold, the transaction is blocked and a risk warning is generated. If the risk level is within a safe range, the transaction is released and the verification result is recorded.

[0075] The present invention will be further described below in conjunction with Examples 1 to 5: Example 1:

[0076] The specific implementation of the system in this embodiment involves a collaborative working mechanism of a transaction comparison unit, a time window collection unit, a frequency analysis unit, a transaction interception unit and an authority determination unit to achieve refined verification of transaction operations.

[0077] The transaction comparison unit uses text feature extraction technology to analyze key operational elements in unstructured transaction instructions. In practice, unstructured transaction instructions may exist in various forms, such as notes entered by users during transfers or text content converted from voice commands. The system uses natural language processing technology to perform word segmentation, part-of-speech tagging, and named entity recognition on these texts, extracting key information such as the transaction amount, recipient account, and operation type. For example, for the note "Pay last month's payment to XX Company," the system uses named entity recognition technology to identify "XX Company" as the recipient and, through keyword matching, determines "payment" as the operation type.

[0078] At the same time, the system stores vectorized templates of historical transaction conflict events and their handling strategies. Historical transaction conflict events include duplicate payments, transactions initiated after abnormal account logins, etc. Each event and its handling strategy is converted into vector form and stored in the database for fast retrieval and matching. When a new transaction request is received, the system first matches the operation type of the current transaction request with the historical transaction records to determine whether the two belong to the same source transaction. The matching of operation types is based on a pre-defined operation type classification system, such as transfer, payment, query, etc. The system extracts the operation type keywords from the current transaction request and the historical transaction records, and calculates the semantic similarity. If the similarity exceeds the preset threshold, it is determined to be a same-source transaction.

[0079] If a same-source transaction is identified, the time window collection unit begins operation. This unit records the timestamp of the current request and the timestamp of the previous transaction completion. Timestamps are recorded using a high-precision clock to ensure accurate time recording. For example, the system records the exact time the current transaction request arrives at the server and the time the previous same-source transaction completed, accurate to the millisecond level.

[0080] The frequency analysis unit calculates the time window overlap between two transactions based on the timestamps recorded by the time window acquisition unit. This calculation takes into account both the duration and the interval between transactions. For example, if two transactions partially overlap on the timeline, the system calculates the ratio of the overlapping portion to the total transaction duration as the time window overlap. Simultaneously, the system compares these with preset frequency thresholds, which are dynamically adjusted based on account type and transaction scenario. For example, the daily upper limit for the number of similar transactions for a corporate account might be 100, while for a personal account it might be 20. The system regularly adjusts these thresholds based on historical transaction data and risk assessment results.

[0081] If the overlap between time windows exceeds a preset frequency threshold, the transaction interception unit immediately terminates the transaction and generates a high-frequency operation log. This log contains detailed information about the transaction request, such as transaction time, transaction amount, operation type, and initiating device. The log is stored in an encrypted format to ensure data security and integrity. The system also flags high-frequency operation events as suspicious and sends them to the risk monitoring center for further analysis.

[0082] If the operation type matches, the permission determination unit assigns an independent verification channel to process the current transaction request. This independent verification channel utilizes additional verification methods, such as dynamic verification codes and biometrics. For example, for high-risk transactions, the system may require the user to enter a dynamic verification code on their mobile phone for verification; for transactions involving large sums of money, the system may require fingerprint or facial recognition. These additional verification methods enhance transaction security and effectively prevent unauthorized transactions.

[0083] When processing unstructured transaction instructions, the system verifies the validity of key operational elements extracted. For example, when extracting the beneficiary account, the system verifies whether its format complies with bank account specifications and whether there are any discrepancies with the beneficiary accounts in previous transactions. If anomalies are found, the system triggers further verification, such as requiring the user to confirm the beneficiary information.

[0084] The system also regularly updates and maintains vectorized templates for historical transaction conflict events. As new transaction conflict events occur, the system adds them and their resolution strategies to the template library and optimizes and adjusts existing templates. Furthermore, the system categorizes and manages the template library, dividing it by transaction type, risk level, and other factors for rapid retrieval and matching.

[0085] When the time window collection unit operates, the system considers the impact of factors such as network latency on timestamp recording. To ensure timestamp accuracy, the system uses the Network Time Protocol (NTP) to synchronize with an authoritative time server and regularly calibrate the local clock. Furthermore, the system records timestamps at each stage of transaction processing for subsequent analysis and troubleshooting.

[0086] The frequency analysis unit considers transaction cyclical characteristics when calculating time window overlap. For example, some corporate accounts may make batch payments on a fixed date each month, and these transactions exhibit significant cyclical patterns. The system analyzes historical transaction data to identify these cyclical characteristics and makes appropriate adjustments when calculating time window overlap to avoid misjudgments.

[0087] After terminating a transaction, the transaction interception unit will send a clear prompt to the user. This prompt will explain the reason for the transaction interception and provide appropriate solutions. For example, the user may be prompted with "Your transaction frequency is too high. To protect your account security, the transaction has been temporarily intercepted. Please try again later." The system will also record user feedback on the prompt to further optimize the prompt content.

[0088] When assigning independent verification channels, the authority determination unit selects the appropriate verification method based on the transaction's risk level. This risk assessment is based on multiple factors, such as the transaction amount, transaction time, and the recipient's account attributes. For example, large transfers initiated at night will be considered riskier and assigned a more stringent verification channel, requiring the user to undergo multiple verification methods.

[0089] The system also audits and tracks the entire transaction verification process. Audit logs record the processing of transaction requests, including the time, results, and involved processing units of each step. These audit logs can be used for subsequent compliance checks and troubleshooting to ensure that system operations comply with relevant laws, regulations, and internal regulations.

[0090] In practice, the system will flexibly adjust the operating parameters and strategies of each unit based on different business scenarios and user needs. For example, for VIP customers of financial institutions, the system may relax certain verification standards to provide a more convenient transaction experience; while for newly registered users or those with higher risk levels, the system will strengthen verification to ensure account security. Example 2:

[0091] The specific implementation of the system in this embodiment involves the core workflow of the pattern recognition module, which achieves accurate identification and risk assessment of transaction behaviors through steps such as building analysis links of transaction nodes, calculating the transaction amount change rate, analyzing the correlation deviation, and generating real-time credibility parameters.

[0092] To generate an analysis chain by arranging all transaction nodes in chronological order, the system first preprocesses the acquired historical transaction records. Transaction records contain multi-dimensional data such as timestamps, transaction amounts, transaction types, and account information. The system then sorts these transaction nodes chronologically to form a complete transaction timeline. During this sorting process, the system addresses potential timestamp anomalies, such as missing or out-of-order timestamps, and corrects them through interpolation or synchronization with other system times.

[0093] After randomly selecting pairs of adjacent transaction nodes, the system obtains the corresponding transaction amounts and calculates the absolute rate of change. For example, if the previous transaction amount is 5,000 yuan and the subsequent transaction amount is 6,000 yuan, the absolute rate of change is 20%. To ensure the accuracy of the calculation, the system verifies the validity of the transaction amounts to eliminate outliers caused by system failures or data transmission errors. The system also records basic information for each transaction node pair, including transaction time, transaction object, and transaction purpose, for subsequent analysis.

[0094] To determine the peak and benchmark amounts within the full set of transaction data, the system traverses all transaction records and identifies the historical maximum transaction amount as the peak amount. The benchmark value is determined using a combination of statistical methods, such as calculating the mean and median of all transaction amounts, and categorizing the calculations based on transaction type and account attributes. Different benchmark value calculation methods are used for corporate and personal accounts to reflect the transaction characteristics of different account types.

[0095] When calculating the overall fluctuation range, the system takes into account transaction cyclical and seasonal factors. Transaction amounts in certain industries may fluctuate regularly over specific time periods. For example, retail transaction amounts typically increase significantly during holidays. The system uses time series analysis to identify these cyclical fluctuations and makes appropriate adjustments when calculating the fluctuation range.

[0096] After calculating the ratio of the absolute value of the rate of change to the fluctuation range, the system normalizes this ratio and maps it to the range of 0-1 for subsequent analysis with other indicators. Normalization eliminates dimensional differences between different transaction types and accounts, making the indicators comparable.

[0097] When calculating the transaction correlation deviation between pairs of transaction nodes, the system analyzes multiple dimensions. The time dimension considers the length of the time interval between transactions; the amount dimension analyzes the correlation between the two transaction amounts; and the transaction object dimension examines the relationship between the two parties. For example, if the recipient of two transactions is the same account, the time interval between the transactions is short, and the amounts are proportional, then the two transactions are considered highly correlated.

[0098] After identifying the maximum and minimum correlations among all nodes and determining the global correlation range, the system compares the correlation deviation of each pair of transaction nodes with this range and calculates a ratio. This ratio reflects the position of the correlation of the transaction node pair within the overall correlation distribution, helping to identify transactions with abnormal correlations.

[0099] When normalizing the amount ratio and correlation ratio to derive real-time credibility parameters, the system uses a weighted average approach, assigning different weights to the amount ratio and correlation ratio based on different business scenarios and risk appetite. For example, in higher-risk financial transaction scenarios, the correlation ratio may be given a higher weight, as unusual transaction correlations often indicate potential risks.

[0100] As the system traverses the remaining pairs of trading nodes to generate a set of associated transaction factors, it performs the same analysis process for each pair, calculating their real-time credibility parameters. To improve processing efficiency, the system utilizes parallel computing technology to process multiple pairs of trading nodes simultaneously. The resulting set of associated transaction factors includes the credibility parameters of all pairs of trading nodes, which will serve as an important basis for subsequent risk assessments.

[0101] When calculating the mean of all real-time credibility parameters as the independent trading factor, the system handles outliers. Any credibility parameter that significantly deviates from the normal range will be further verified to determine whether it is a data anomaly or a genuine trading risk. When calculating the mean, the system weights the credibility parameters based on the importance and risk level of the trading nodes, ensuring that the credibility parameters of important trading nodes have a greater impact on the independent trading factor.

[0102] The system also provides a visual display of the generated set of associated transaction factors and independent transaction factors. Through charts and reports, it intuitively presents the relationships between transaction nodes and the distribution of risks. This helps risk managers quickly identify potential risk points and take appropriate preventative measures.

[0103] In practice, the system will dynamically adjust the analysis link construction and parameter calculation methods based on different transaction scenarios and business needs. For high-frequency, small-value transactions, the system may pay more attention to the time interval and frequency of transactions; for large-value transfers, the system will focus on analyzing fluctuations in transaction amounts and the risk characteristics of the recipient.

[0104] The system regularly conducts retrospective analysis of historical transaction data to verify the accuracy and effectiveness of the pattern recognition module. By comparing suspicious transactions identified by the system with actual risk events, the system's risk identification capabilities are evaluated, and analysis models and parameters are optimized and adjusted based on the evaluation results.

[0105] To ensure system stability and reliability, the pattern recognition module utilizes a distributed computing architecture. This distributes large amounts of transaction data across multiple computing nodes for parallel processing, improving the system's processing power and fault tolerance. Furthermore, the system incorporates a data backup and recovery mechanism to ensure rapid data and service recovery in the event of a failure.

[0106] During data transmission, the system uses encryption technology to ensure the security and integrity of transaction data. Sensitive information, such as transaction amounts and account information, is encrypted to prevent data from being stolen or tampered with during transmission.

[0107] The system is also integrated with other security systems, such as anti-money laundering and fraud detection systems. Through data sharing and collaborative analysis, it enables comprehensive monitoring and prevention of transaction risks. For example, when the pattern recognition module detects a suspicious transaction, it promptly transmits the relevant information to the anti-money laundering system for further investigation and processing.

[0108] The pattern recognition module's analysis results are fed back to the risk assessment system in real time. Based on these results, the risk assessment system dynamically adjusts the account's risk level and implements appropriate risk control measures. For example, for accounts with higher risk levels, the system may limit their transaction limits or require additional identity verification. Example 3:

[0109] The specific implementation of the system in this embodiment involves the core workflow of the indicator calculation module, which achieves accurate assessment of transaction risks through steps such as statistical analysis of credibility pairs, calculation of absolute values ​​of differences, identification of median and range values, and calculation of distribution equilibrium parameters.

[0110] After generating a credibility pair, the system first counts its valid number. A credibility pair is formed by matching elements in the security and risk ranking queues one by one. Elements in the security ranking queue represent the positive deviations between compliant transactions and the security threshold, sorted in ascending order by deviation value; elements in the risk ranking queue represent the negative deviations between suspicious transactions and the risk threshold, sorted in descending order by deviation value. The system traverses both queues, matching elements in corresponding positions to form credibility pairs. For example, the first element in the security ranking queue forms a pair with the first element in the risk ranking queue, and so on. During the matching process, the system checks the validity of the elements, such as whether they are within a reasonable range and whether there are any conflicts with other data. Only valid element pairs are counted towards the valid number of credibility pairs.

[0111] When calculating the absolute difference between the safety element and the risk element for each group, the system subtracts the safety element (the deviation value for compliant transactions) from the risk element (the deviation value for suspicious transactions) for each credibility pair and takes the absolute value. For example, if the safety element deviation value for a credibility pair is 8% and the risk element deviation value is -12%, the absolute difference between them is 20%. The system performs the same calculation for all credibility pairs, generating a series of absolute difference values.

[0112] When identifying the median value among all absolute difference values, the system sorts them from smallest to largest. If the number of data points is odd, the middle value is the median; if the number of data points is even, the average of the two middle values ​​is the median. The median reflects the central tendency of the data, avoids the influence of extreme values, and provides a more robust statistic.

[0113] The range is calculated by finding the maximum and minimum absolute values ​​of all differences and then subtracting the minimum from the maximum. The range reflects the fluctuation range of the data and can intuitively demonstrate the degree of dispersion of the data. For example, if the maximum absolute value of the difference is 35% and the minimum is 5%, the range is 30%.

[0114] When calculating the distribution balance parameter for all absolute difference values, the system considers the data's distribution. The distribution balance parameter measures how evenly the data is distributed across each interval. The system divides the range of absolute difference values ​​into several intervals, counts the number of data points within each interval, and calculates the variance or standard deviation of these data points. A smaller variance or standard deviation indicates a more evenly distributed data and a higher distribution balance parameter. Conversely, a larger variance or standard deviation indicates a less evenly distributed data and a lower distribution balance parameter.

[0115] The system calculates the credibility assessment value through a weighted summation of the median, range, and distribution balance parameter. The median, range, and distribution balance parameter reflect the data's central tendency, fluctuation range, and distribution uniformity, respectively, and each has varying degrees of importance to the credibility assessment. The system assigns different weights to these three parameters, determined based on historical data training. For example, in some business scenarios, the central tendency of the data may be of greater concern, so the median may be given a higher weight; in other scenarios, the fluctuation range may be more important, so the range may be given a higher weight.

[0116] During the calculation process, the system introduces two formulas to accurately describe this process. The first formula is:

[0117]

[0118] in, represents the average of the absolute values ​​of the differences, represents the effective number of credibility pairs, Indicates the For security elements in the trustworthiness pair (deviation value of compliant transactions), Indicates the For risk elements in the credibility pair (deviation values ​​of suspicious transactions), This formula is used to calculate the average of all absolute differences, providing basic data for subsequent statistical analysis.

[0119] The second formula is:

[0120]

[0121] in, represents the credibility evaluation value, represents the median value, Indicates the extreme value, represents the distribution balance parameter, 、 、 Represent the weights of the median, range and distribution balance parameters respectively, and satisfy This formula comprehensively considers the impact of the median, range, and distribution equilibrium parameters on credibility assessment through weighted summation, ultimately obtaining an assessment value that fully reflects transaction risk.

[0122] The system normalizes the calculated credibility values, mapping them to a range of 0-100 for easier understanding and application. This normalization eliminates differences between different business scenarios and data sources, making credibility values ​​comparable.

[0123] In practice, the system adjusts the weighting parameters in the formula based on different business needs and risk appetite. For businesses with lower risk tolerance, the system may increase the weight of the extreme value to more sensitively capture abnormal fluctuations; for businesses with higher risk tolerance, the system may increase the weight of the distribution balance parameter to focus more on the overall distribution of the data.

[0124] The system regularly conducts retrospective analysis of historical data to verify the accuracy and effectiveness of credibility assessments. By comparing the credibility assessments calculated by the system with actual risk events, the model's predictive capabilities are evaluated and the weighting parameters in the formula are optimized and adjusted based on the evaluation results.

[0125] To improve computing efficiency, the system uses distributed computing technology, which distributes large amounts of computing tasks to multiple computing nodes for simultaneous processing. This not only speeds up computing but also improves the system's fault tolerance and reliability.

[0126] In terms of data storage, the system uses a high-performance database system that can quickly store and retrieve large amounts of transaction data and calculation results. At the same time, the system also has a data backup and recovery mechanism to ensure data security and integrity.

[0127] The system will provide real-time feedback on the credibility assessment value to the risk monitoring system. Based on the assessment value, the risk monitoring system will classify and issue early warnings for transactions. For transactions with low credibility assessments, the system will issue a high-risk warning, prompting risk management personnel to conduct further investigation and action. For transactions with high credibility assessments, the system will consider them low-risk and approve them normally.

[0128] The system also dynamically tracks and analyzes credibility assessments. As transactions continue, the system continuously updates the credibility assessments, promptly identifying potential risk trends. For example, if an account's credibility assessment continues to decline, even if the current transaction hasn't triggered a high-risk alert, the system will increase monitoring of that account to prevent potential risks.

[0129] Through the above implementation methods, the system realizes accurate assessment of transaction risks, can provide a scientific and reliable basis for risk decision-making, effectively prevent financial risks, and ensure the security of user accounts and funds. Example 4:

[0130] The system's implementation in this example revolves around the core functionality of the parameter adjustment module. This module uses credibility assessments to drive dynamic optimization of the transaction risk profile, enabling adaptive adjustments to verification strategies. The following details the entire process, from credibility assessment to strategy implementation, using specific examples from various business scenarios.

[0131] Suppose a business merchant account on an e-commerce platform completes 200 transactions in a month. The system analyzes the compliance and suspiciousness of its transaction chain using the indicator calculation module, resulting in a credibility assessment of 0.85. The parameter adjustment module presets a primary credibility threshold of 0.7 and an advanced credibility threshold of 0.8, with configured basic adjustment coefficients of 1.0, intermediate adjustment coefficients of 1.2, and enhanced adjustment coefficients of 1.5. Because 0.85 is higher than the advanced credibility threshold, the system determines that the account's transactions are highly trustworthy and activates the basic adjustment coefficient.

[0132] In another example, a user initiated transfers to three unknown accounts between 2:00 AM and 5:00 AM, totaling 80,000 yuan. After extracting transaction features, the system calculated a credibility score of 0.62, which falls between the primary credibility threshold (0.7) and the lower risk threshold. Therefore, an intermediate adjustment factor of 1.2 is applied. If an account is involved in multiple, unusually large transfers, the credibility score drops to 0.5, below the primary credibility threshold, triggering an enhanced adjustment factor of 1.5, indicating a significant increase in verification efforts.

[0133] Taking a business merchant account as an example, the authorization parameter correction value in its initial transaction risk characteristic table is 0.9 (the base coefficient for adjusting transaction permissions). Based on the credibility assessment value of 0.85 and the basic adjustment coefficient of 1.0, the dynamic adjustment gradient is , multiply it by the authorization parameter correction value to get the final authorization parameter This parameter indicates that the account's trading permissions can be moderately relaxed, but basic verification efforts must be maintained.

[0134] For personal accounts with high-frequency transfers in the early morning, assuming that its authorization parameter correction value is 0.8 (the system sets a lower initial authority for personal accounts by default), the product of the credibility assessment value of 0.62 and the intermediate adjustment coefficient of 1.2 is , the final authorization parameters are This parameter indicates that transaction risk has increased and permissions need to be tightened. If an account triggers risk rules multiple times, the authorization parameter correction value is 1.0 (initial highest permission), the product of the credibility assessment value of 0.5 and the enhancement adjustment coefficient of 1.5 is 0.75, and the final authorization parameter is , it seems that the value is high, but combined with its risk level, the system will match more stringent verification indicators.

[0135] The system's preset permission mapping rules are constructed based on transaction types, account attributes, and other dimensions. For example:

[0136] In the enterprise merchant batch payment scenario, the final authorization parameter is linked to the single payment limit and the automatic verification ratio. For example, if the final authorization parameter is 0.765, the corresponding expected verification indicators might be "single payment limit adjusted from 100,000 yuan to 120,000 yuan" (a moderate increase due to higher credibility) and "automatic verification ratio increased from 80% to 90%" (reducing manual intervention).

[0137] For cross-border remittances from personal accounts, the final authorization parameter is linked to identity verification complexity and transaction frequency limits. For example, if the parameter is 0.5952, the corresponding indicators might be "face recognition + dynamic token dual verification for each remittance" and "the daily remittance limit is reduced from 3 to 2."

[0138] High-risk suspicious transaction scenarios: When the final authorization parameter falls below 0.6, the rule base enforces the association of indicators such as "manual review beforehand" and "fund flow tracking." For example, if the final authorization parameter for an account is 0.55, the system will require that every transaction over 10,000 yuan must be manually confirmed by risk control personnel before execution.

[0139] The underlying logic of permission mapping rules is based on historical risk data. For example, statistics show that when the final authorization parameter for corporate accounts is above 0.7, the transaction fraud rate is below 0.1%, so a moderate relaxation of the limit is allowed. However, when the parameter for personal accounts is below 0.6, the fraud rate rises to 5%, requiring strengthened verification measures.

[0140] Taking the batch payment scenario of corporate merchants as an example, the optimized transaction risk feature table includes:

[0141] Final authorization parameter: 0.765 (reflecting the credibility of the current transaction link).

[0142] Expected verification indicators:

[0143] Single payment limit: 120,000 yuan (20% increase from the original threshold);

[0144] Automatic verification conditions: When the counterparty is a historical cooperative merchant, manual review is not required;

[0145] Abnormal interception rules: When a single payment amount exceeds 150% of the limit, it will be blocked immediately and an early warning will be triggered.

[0146] In the cross-border remittance scenario of a personal account, the optimized table content is as follows:

[0147] Final authorization parameter: 0.5952;

[0148] Expected verification indicators:

[0149] Two-factor authentication is mandatory: every remittance must pass facial recognition and a dynamic verification code on the mobile phone;

[0150] Transaction time limit: Remittances can only be initiated between 9:00-18:00 on weekdays;

[0151] Cumulative amount monitoring: When the total amount of cross-border remittances exceeds 50,000 yuan within 7 days, proof of fund use must be submitted.

[0152] For high-risk accounts that trigger enhanced adjustment factors (such as a final authorization parameter of 0.55), the optimization table will include extreme control measures:

[0153] Final authorization parameter: 0.55;

[0154] Expected verification indicators: Suspension of non-counter transaction permissions (only counter operations are retained); proof of the recipient's qualifications (such as invoices, contracts) must be provided for each transaction; the risk level is marked as "extremely high risk" and included in the real-time monitoring whitelist.

[0155] Assume that a manufacturing enterprise account initiates two transactions on May 20, 2025:

[0156] ① First transaction: At 10:00 a.m., a payment of RMB 500,000 was made to a long-term supplier via corporate online banking.

[0157] Data receiving module: Identifies corporate accounts and online banking channels, and builds a verification model that includes dimensions such as transaction frequency and the length of historical cooperation with the payee.

[0158] Feature analysis module: Extracts compliance features such as "the supplier's transactions account for 30% in the past three months" and "all transactions are conducted on weekday mornings" to generate an initial risk feature table, with an authorization parameter correction value of 0.9 and a verification indicator of "single transaction limit of 1 million yuan, automatic verification."

[0159] Pattern Recognition Module: This transaction forms a correlation factor with the previous three transactions with the same supplier. The amount fluctuation is less than 5%, indicating a high correlation and generating a real-time credibility parameter of 0.9.

[0160] Indicator calculation module: Combined with other independent trading factors, the calculated credibility assessment value is 0.88, which is higher than the advanced credibility threshold.

[0161] Parameter adjustment module: Enable the basic adjustment coefficient 1.0, and the final authorized parameters are After matching the rules, the single transaction limit is maintained at 1 million yuan, the transaction is automatically verified and released.

[0162] ② The second transaction: At 15:00 on the same day, a payment of RMB 800,000 was made to a newly registered unfamiliar account with the note “Equipment Purchase”.

[0163] Data receiving module: If the payee is detected as a newly opened account, the "unfamiliar transaction" risk dimension is triggered, and a model is constructed that includes dimensions such as account duration and sudden increase in transaction amount.

[0164] Feature analysis module: Extracts abnormal features such as "new payee" and "amount exceeds 4 times the average transaction amount of the account". The initial risk feature table authorization parameter correction value is 0.7, and the verification indicator is "single transaction limit of 500,000 yuan, requiring approval by the financial director."

[0165] Pattern Recognition Module: This transaction has no correlation with historical transactions and is determined to be an independent transaction factor with a real-time credibility parameter of 0.6.

[0166] Indicator calculation module: The credibility assessment value is 0.63, which is between the primary and advanced credibility thresholds.

[0167] Parameter adjustment module: Enable the intermediate adjustment coefficient 1.2, and the final authorized parameters are After matching the rules, the single transaction limit is reduced to 300,000 yuan (70% of the original threshold of 500,000 yuan), and it requires approval from both the financial director and the legal representative.

[0168] Verification and execution module: Because the transaction amount of 800,000 yuan exceeded the adjusted limit, the system automatically blocked the transaction and prompted the user that "the transaction amount exceeds the current account authority, please submit for double approval."

[0169] The system regularly reviews the effectiveness of optimized transaction risk profiles. For example, in scenarios involving high-frequency transfers from personal accounts with intermediate adjustment coefficients enabled, the system observes whether the incidence of risk events for similar transactions decreases after the adjustments (but does not involve specific data verification). If the strategy is found to be ineffective in a particular scenario, such as frequent omissions in verification of unfamiliar transactions from corporate accounts, the system manually adjusts the permission mapping rules to add a "mandatory manual review for first-time transactions by new payees," eliminating the need to rely on credibility assessments to trigger the process. This creates a dual layer of control, encompassing both rules-based and dynamic adjustments.

[0170] Through the above process, the parameter adjustment module realizes the automated mapping from data features to verification strategies, which not only ensures the smoothness of high-credibility transactions, but also prevents potential risks by dynamically tightening permissions, forming an adaptive security protection system covering the entire transaction chain. Example 5:

[0171] The system's implementation in this embodiment revolves around the expanded functionality of the data receiving module. By integrating transaction behavior profiles, anomaly boundary response mechanisms, and conflict event handling strategies, it achieves multi-layered control of transaction risks. The following details the entire process, from data integration to response execution, using specific examples from various business scenarios.

[0172] Assume that corporate customer A of a commercial bank initiates multiple transactions on May 23, 2025. The data receiving module integrates the following data in real time:

[0173] Transaction flow data: 3 million yuan was transferred to an affiliated company at 9:00 a.m., and 5 million yuan was transferred to an unfamiliar account at 2:00 p.m., both through online banking channels.

[0174] Account attribute data: This account is a VIP corporate account with an average daily trading volume of 2 million yuan. Its historical trading counterparties are concentrated in five affiliated companies.

[0175] Operating environment data: The IP address for transactions in the morning was the company's office address, while the IP address for transactions in the afternoon was an overseas region.

[0176] Based on this data, the system constructs a transaction behavior feature map, marking the characteristics of the two transactions:

[0177] Morning transactions: These transactions have the characteristics of “transfers from related companies,” “operations during office hours,” and “IP address compliance,” and fall into the “safe zone” of the graph.

[0178] Afternoon transactions: These include features such as “large transfers from unfamiliar accounts,” “logins from overseas IP addresses,” and “a sudden 150% increase in transaction amounts,” triggering the “abnormal boundary warning” in the graph.

[0179] The graph visualizes the correlations between various characteristics through a visual interface. For example, the correlation between overseas IP addresses and unfamiliar accounts is 80%, and the deviation between large transfers and historical transaction amounts is 150%. Risk managers can intuitively see the distribution of risk characteristics for each account and determine whether to activate the secondary verification process.

[0180] For abnormal boundary breakthroughs triggered by afternoon trading, the system automatically matches the three-level response mechanism according to the degree of breakthrough:

[0181] Level 1 Response (Limit Adjustment): If the account's abnormal characteristic is "login from an overseas IP address" but the transaction amount does not exceed 100% of the historical average, the system triggers a Level 1 Response, adjusting the remaining transaction limit for the day from 8 million yuan to 5 million yuan. For example, if a retail business account logs in from a new device during off-hours and initiates a small transfer, the system will determine the risk is low and only adjust the limit without blocking the transaction.

[0182] Level 2 Response (Cross-Channel Verification): If a transaction contains both the "unknown account" and "amount suddenly increased by 50%" characteristics, such as Customer A's 5 million yuan transfer, the system initiates a Level 2 response. After the user submits the transfer request, the system automatically sends a text message verification code to the registered mobile phone number and requires it to be entered within 10 minutes. If the user fails to complete the verification in time, the transaction will be temporarily suspended.

[0183] Level 3 Response (Fund Freeze and Manual Review): If a transaction involves the triple anomalies of "overseas IP address," "unknown account," and "amount exceeding historical peak" (e.g., an account suddenly transfers 10 million yuan to an unknown overseas account, far exceeding its historical maximum transaction amount of 5 million yuan), the system immediately freezes the account's funds and sends a manual review instruction to the risk control department. Risk control personnel must verify the transaction contract, the recipient's qualifications, and other documents within one hour before lifting the freeze.

[0184] In another example, user B transferred funds to four different accounts via mobile banking within an hour, totaling 100,000 yuan (their historical daily average was 20,000 yuan). The system detected "high-frequency trading" and "dispersed transfers across multiple accounts," calculating the anomaly boundary breach level as Level 2 and initiating cross-channel verification, requiring the user to pass both facial and fingerprint recognition before continuing.

[0185] When a user submits an unstructured transaction instruction, such as filling in "Urgent payment, please prioritize" in the transfer note, the transaction comparison unit analyzes key operation elements using text feature extraction technology:

[0186] Keyword extraction: Identify words such as "urgent payment" and "priority processing" to determine whether the transaction may have time-sensitive requirements.

[0187] Historical conflict matching: The system searches the historical transaction conflict event database and finds that phishing attacks have occurred in similar "urgent payment" scenarios. Attackers use urgent rhetoric to trick users into ignoring the verification process.

[0188] Strategy library call: Based on the vectorized template, the system matches the "urgent transactions require additional manual review" solution from the strategy library and generates a verification priority adjustment instruction, raising the verification level of the transaction from "automatic verification" to "manual priority verification."

[0189] In another case, User C initiated a transaction via voice command: "Transfer 5,000 yuan to Zhang San for payment of goods." The system interpreted "Zhang San" as a non-historical recipient and "payment of goods" as a new purpose, triggering a conflict event: "non-historical counterparty + new transaction purpose." The system immediately searched the policy library and discovered that this scenario required "verification of the recipient's identity and confirmation of the transaction's authenticity." Therefore, it sent a message to the user: "We have detected that you are transferring funds to a new recipient. Please confirm that this is you and provide the last four digits of the recipient's ID number for verification."

[0190] The transaction process of Account D of a technology company on May 24, 2025 is as follows:

[0191] During a normal transaction, a payment of 1 million RMB is made to Supplier E at 10:00 AM, with the note "Material Payment, May 2025." The system interprets the instruction as "Supplier E," a historical partner, and "Material Payment," a standard transaction. This transaction is automatically approved without additional verification, as it is considered a homologous transaction.

[0192] Anomaly triggering phase: At 3:30 PM, a 2 million yuan transfer was made to personal account F, with the note "equipment purchase deposit." The data receiving module detected features such as "the recipient was a personal account," "the transaction amount exceeded the company's regular purchase amount by 150%," and "the purpose deviated from the company's business scope." It constructed an anomaly feature map and calculated the abnormal boundary violation level as Level 3. The system immediately froze the account's funds and sent a warning text message to the company's financial director: "The 2 million yuan transfer from your account to personal account F presents an abnormal risk. The funds have been frozen. Please log in to the system as soon as possible and submit the transaction receipt for manual review."

[0193] During the manual review phase, the financial manager logged into the system and uploaded documents such as the equipment purchase contract and security deposit receipt for individual account F. Risk control personnel verified that Party A of the contract was a technology company, and Party B was the sole proprietorship of individual F. While the transaction was legitimate, it fell outside the system's pre-set risk model. Based on the review results, the system adjusted the account's transaction profile, adding "self-employed business purchases" to the compliance scenario, unfreezing the funds, and updating the risk / compliance matrix to prevent similar transactions from triggering a Level 3 response again.

[0194] The system regularly reviews historical transaction conflicts and updates them into quantized templates and handling strategies. For example, after discovering that a certain type of "cross-border e-commerce refund" transaction frequently triggered false interceptions, the system marked the "cross-border e-commerce platform + refund" feature combination as a low-risk scenario and adjusted the handling strategy to "automatic verification + amount limit" (e.g., a single refund cannot exceed 50,000 yuan). When new fraudulent methods emerge (such as emergency transfers in the name of a forged government agency), the risk control department manually adds the "government agency + emergency transfer + unfamiliar account" feature combination and configures the "mandatory manual verification + funds flow tracking" strategy, eliminating the need to rely on historical data training.

[0195] The system supports customizable risk / compliance matrices for different industries. For example, "drug purchase payment" transactions in healthcare accounts are typically large and directed to fixed suppliers, so the system sets a separate verification strategy for these transactions: "Transactions with similar suppliers are automatically approved, while transactions with new suppliers require a purchase contract." Meanwhile, "technical service fee" transfers in internet industry accounts may be more frequent and involve a variety of recipients. Therefore, the system relaxes verification standards and only manually reviews transactions exceeding 500,000 yuan.

[0196] Throughout the entire process, the system implements encrypted storage and transmission measures for sensitive data. For example, parsed information such as the recipient's ID number and scanned copies of transaction contracts are encrypted using the AES-256 algorithm, and transmitted using the SSL / TLS protocol to prevent eavesdropping. The operation audit module records the timestamp and operator of each feature parsing, policy matching, and response execution, creating an unalterable audit log for regulatory compliance checks.

[0197] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0198] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A financial data security verification system, characterized in that: include: The data receiving module is used to obtain the financial transaction data set to be verified and build a multi-dimensional verification model based on account type and transaction channel; A feature analysis module is configured to extract abnormal operation features from historical transaction records and generate a transaction risk feature table based on the multi-dimensional verification model, wherein the transaction risk feature table includes verification indicators and authorization parameter correction values; the authorization parameter correction values ​​are benchmark coefficients used to adjust transaction permissions; a pattern recognition module, configured to decompose the historical transaction records, identify high-frequency transaction nodes and low-frequency transaction nodes, and classify associated transaction factors and independent transaction factors; An indicator calculation module, configured to generate a credibility evaluation value of a transaction link based on the associated transaction factor and the independent transaction factor; a parameter adjustment module, configured to determine a dynamic adjustment gradient of the transaction risk characteristic table according to the credibility evaluation value, and multiply the dynamic adjustment gradient by an authorization parameter correction value to generate a final authorization parameter; Matching preset permission mapping rules to determine expected verification indicators corresponding to the final authorization parameters; integrating the final authorization parameters and the expected verification indicators to construct an optimized transaction risk feature table; and updating the transaction risk feature table based on the dynamic adjustment gradient to form an optimized transaction risk feature table; A verification execution module, configured to trigger blocking or releasing of account transaction permissions based on the optimized transaction risk characteristic table; The pattern recognition module includes: Arrange all transaction nodes according to transaction time sequence to generate an analysis link, randomly select adjacent transaction node pairs, obtain corresponding transaction amount readings and subsequent transaction amount readings, and calculate the absolute value of the change rate of the amount readings; Determine the peak and baseline values ​​of the total transaction data and calculate the overall fluctuation range of the amount; Calculating the ratio of the absolute value of the rate of change to the fluctuation range; Calculating the transaction correlation deviation between the transaction node pairs; Identify the maximum and minimum correlation among all nodes and calculate the global correlation difference range; Calculating a ratio of the transaction correlation deviation to the difference range; Normalizing the ratio of the absolute value of the change rate to the fluctuation range and the ratio of the transaction correlation deviation to the difference range to obtain a real-time credibility parameter; Traverse the remaining transaction node pairs to generate a set of associated transaction factors, and calculate the mean of all real-time credibility parameters as the independent transaction factor. The associated transaction factor set includes the real-time credibility parameters of all transaction node pairs. The indicator calculation module includes: Setting safety thresholds and risk thresholds for related-party transaction factors; Screening a set of compliant transactions based on the security threshold and screening a set of suspicious transactions based on the risk threshold; Calculate the positive deviation between compliant transactions and the security threshold, and arrange them in ascending order of deviation to form a security sorting queue; Calculate the negative deviation between the suspicious transactions and the risk threshold, and sort them in descending order of the deviation values ​​to form a risk ranking queue; The safe queue and risk queue elements are matched item by item to form credibility pairs, and the credibility evaluation value is calculated based on all credibility pairs.

2. The financial data security verification system according to claim 1, characterized in that: Also includes: The transaction comparison unit is used to match the operation type of the current transaction request with the historical transaction record to determine whether the two belong to the same source transaction; The time window collection unit is used to record the initiation timestamp of the current request and the completion timestamp of the previous transaction if it is determined to be a same-source transaction; A frequency analysis unit is used to calculate the overlap of the time windows of two transactions and compare it with a preset frequency threshold to determine whether the current transaction is allowed to be executed; a transaction interception unit, configured to terminate transaction execution and generate a high-frequency operation log if the time window overlap exceeds the preset frequency threshold; The permission determination unit is used to allocate an independent verification channel to process the current transaction request if the operation type matching results are inconsistent.

3. The financial data security verification system according to claim 1, characterized in that: The indicator calculation module also includes: The effective number of statistical credibility pairs is calculated, and the absolute value of the difference between the safety element and the risk element in each group is calculated; Identify the median and range of all absolute differences and calculate the distribution equilibrium parameter of all absolute differences; The credibility evaluation value is calculated based on the median value, the range value and the distribution balance parameter.

4. The financial data security verification system according to claim 1, characterized in that: The parameter adjustment module includes: Set primary trust thresholds and advanced trust thresholds; Configure basic adjustment coefficients, intermediate adjustment coefficients, and enhanced adjustment coefficients; The basic adjustment coefficient is enabled when the credibility evaluation value is higher than the advanced credibility threshold, the intermediate adjustment coefficient is enabled when the credibility evaluation value is between the primary and advanced credibility thresholds, and the enhanced adjustment coefficient is enabled when the credibility evaluation value is lower than the primary credibility threshold.

5. The financial data security verification system according to claim 1, characterized in that: The data receiving module also includes: Real-time integration of transaction flow data, account attribute data, and operating environment data to build a transaction behavior feature map; Calculate the abnormal boundary of transaction behavior based on the transaction behavior feature map, and activate the secondary verification process when it is detected that the transaction characteristics exceed the safety boundary; Through association rule mining, risk matrices or compliance matrices for different transaction scenarios are generated, and the optimal verification strategy set is generated.

6. The financial data security verification system according to claim 5, characterized in that: A three-level response mechanism is implemented based on the degree of abnormal boundary breach: Level 1 triggers account transaction limit adjustment, Level 2 initiates cross-channel transaction verification, and Level 3 freezes account fund flow and sends manual review instructions.

7. The financial data security verification system according to claim 2, characterized in that: The transaction comparison unit includes: parsing key operation elements in unstructured transaction instructions through text feature extraction technology, storing vectorized templates of historical transaction conflict events and their handling strategies, and when a transaction conflict is detected, retrieving an adaptive handling solution from a policy library through pattern matching, and generating a verification priority adjustment instruction.

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