A method and system for operational risk governance

Abstract

The application discloses a kind of about the method and system of operation risk governance, method includes: according to the preset period acquisition store operation data, data is preprocessed to obtain structured risk information;Quantitative evaluation is carried out based on dynamic quantile benchmark algorithm, the deviation degree of business index relative to space-time dynamic benchmark value is calculated, the abnormality of core index is determined and risk grade is output;According to risk grade and core index abnormality, generate store risk label, build risk transmission network and generate targeted management scheme;Finally, through closed loop verification, output risk management report.The application realizes the dynamic quantitative evaluation and accurate targeted management of operation risk, through the intelligent adjustment of space-time dynamic benchmark value and the construction of risk transmission network, the accuracy of risk identification and management efficiency are significantly improved, while reducing the cost of manual intervention.

Description

Technical Field

[0001] This invention relates to the field of operational risk management technology, and more specifically to a method and system for operational risk governance. Background Technology

[0002] In retail store operations management, risk governance is a crucial aspect of ensuring business compliance and operational efficiency. Traditional risk monitoring primarily relies on manual inspections or single-point data verification, identifying problems through isolated analysis of specific scenarios (such as transaction anomalies or inventory discrepancies). As chain stores expand and business complexity increases, this decentralized governance model has gradually revealed drawbacks such as delayed response and inefficient resource allocation. Especially in multi-regional, multi-format operating environments, the risk performance of different stores often exhibits significant dynamic changes due to differences in geographical location, customer characteristics, and business volume, while existing technologies struggle to establish a unified and flexible risk assessment system. Furthermore, the lack of systematic integration and analysis of operational data prevents traditional methods from effectively identifying risk transmission paths, leading to a reactive, piecemeal approach to governance. Therefore, achieving a shift from localized risk control to comprehensive, dynamic governance has become a key challenge in improving the effectiveness of operational risk management. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method and system for operational risk governance, which quantifies the degree of risk deviation through a dynamic quantile benchmark algorithm and constructs a transmission network to achieve precise targeted governance and solve the problems of low efficiency and scattered resources in traditional single-point monitoring.

[0004] To achieve the above objectives, in a first aspect, this application provides a method for operational risk governance, comprising:

[0005] Store operation data is collected according to a preset cycle. The store operation data includes work behavior logs, transaction records, and system operation trajectory.

[0006] Preprocessing of store operation data yields structured risk information. Preprocessing includes data cleaning, field standardization, and operational process reconstruction.

[0007] The structured risk information is quantitatively assessed based on the dynamic quantile benchmark algorithm. The quantitative assessment includes:

[0008] Calculate the deviation of each business indicator from the spatiotemporal dynamic benchmark value, which is dynamically generated through a three-dimensional weight matrix of region, time period, and business type.

[0009] The degree of abnormality of core indicators is determined based on the degree of deviation, and the risk level is output in combination with the preset risk ladder model;

[0010] Store risk labels are generated based on risk level and the anomaly of core indicators.

[0011] A risk transmission network is constructed based on store risk labels, and targeted governance solutions are generated based on the risk transmission network.

[0012] The targeted governance plan is validated in a closed loop with real-time operational data, and a risk governance report is output, which includes an assessment of the treatment effect and suggestions for plan optimization.

[0013] Furthermore, preprocessing of store operation data yields structured risk information. Preprocessing includes data cleaning, field standardization, and operational workflow reconstruction, including:

[0014] The operation behavior log is subjected to abnormal operation identification and compliance verification, and records of illegal operation are removed and suspicious behavior fragments are marked to obtain the preprocessed operation behavior log;

[0015] The transaction log records are subjected to integrity verification and amount threshold filtering. Missing transaction fields are repaired and abnormal transactions exceeding the preset amount threshold are removed to obtain the preprocessed transaction log records.

[0016] The system operation trajectory is reconstructed in time and the operation chain is parsed to restore the complete operation path and identify discontinuous operation segments, thus obtaining the preprocessed system operation trajectory.

[0017] The pre-processed job behavior logs, transaction records, and system operation trajectories are aligned according to a unified time base and then standardized transformation is performed to obtain structured risk information.

[0018] Furthermore, the deviation of each business indicator from the spatiotemporal dynamic benchmark value is calculated. The spatiotemporal dynamic benchmark value is dynamically generated through a three-dimensional weight matrix of region, time period, and business type, including:

[0019] The area where the store is located is gridded to generate regional weight coefficients, which are dynamically adjusted based on the distribution density of historical risk events.

[0020] The system classifies business peak periods by operating hours and generates time period weighting coefficients, which are dynamically configured based on transaction volume fluctuation characteristics.

[0021] Risk levels are assigned to business types, and weight coefficients for each business type are generated. These weight coefficients are based on preset benchmark values ​​according to regulatory requirements.

[0022] A three-dimensional weight matrix is ​​constructed based on regional weight coefficients, time period weight coefficients, and business type weight coefficients.

[0023] Extract current business metrics from structured risk information and match them with corresponding three-dimensional weight matrices;

[0024] Calculate the deviation of the current business indicator value from the historical benchmark value of the same region, time period, and business type.

[0025] Furthermore, the abnormality of core indicators is determined based on the deviation, and the risk level is output in conjunction with a preset risk ladder model, including:

[0026] The deviation is normalized and converted into an anomaly score in the range of 0-100;

[0027] Based on the type of business, the anomaly score is divided into three dimensions: transaction security, operational compliance, and system stability.

[0028] Set transaction security risk thresholds, operational compliance risk thresholds, and system stability risk thresholds;

[0029] The anomaly scores of each dimension are matched with the transaction security risk threshold, operational compliance risk threshold, and system stability risk threshold to generate a dimension risk level.

[0030] The risk levels of each dimension are weighted and aggregated based on a pre-defined risk ladder model to output the final risk level, which includes four levels: normal, attention, warning, and severe.

[0031] Furthermore, based on a pre-defined risk ladder model, the dimensional risk levels are weighted and aggregated to output the final risk level, including:

[0032] Establish a dimensional risk mapping relationship and convert the anomaly scores of transaction security, operational compliance and system stability into standard risk equivalents respectively;

[0033] Construct a risk transmission matrix and quantify the mutual influence coefficients among the standard risk equivalents of each dimension;

[0034] A dynamic weighted algorithm is used to automatically adjust the weight ratio of risk equivalent in each dimension based on the characteristics of the current business scenario, including:

[0035] Real-time data collection of transaction volume volatility coefficient, operational behavior dispersion, and system load rate as scenario characteristic parameters;

[0036] The scene feature parameters are input into the weight response function to calculate the dynamic weights of each dimension, and the sum of the weights is constrained to be 1 and within a preset range to obtain the weighted standard risk equivalent.

[0037] The weighted standard risk equivalents are nonlinearly superimposed to obtain the aggregated result.

[0038] Set risk level determination rules and determine the final risk level based on the risk range in which the aggregation result is located;

[0039] Furthermore, when the standard risk equivalent in any dimension is detected to exceed the critical threshold, a cross-dimensional risk reassessment mechanism is triggered.

[0040] Furthermore, store risk labels are generated based on risk level and the degree of anomaly of core indicators, including:

[0041] Based on the risk level, a risk level identifier is obtained, and the anomaly degree of the core indicators is decomposed in multiple dimensions to extract transaction security anomaly factors, operational compliance deviation factors and system stability fluctuation factors, and generate an indicator anomaly feature vector.

[0042] Construct a risk label mapping rule base, combine and match risk level identifiers with indicator anomaly feature vectors, and output initial risk labels through preset label generation logic;

[0043] The initial risk labels are validated in context, and the confidence level of the labels is adjusted by combining the store's historical risk records and industry risk profiles. The final store risk labels are then generated and linked to the store risk profile library.

[0044] Furthermore, the targeted governance solution includes real-time interception strategies for key risk nodes, early warning escalation mechanisms for potential transmission paths, and dynamic adjustment rules based on feedback from historical handling effects.

[0045] A risk transmission network is constructed based on store risk tags, and targeted governance solutions are generated based on this network, including:

[0046] Topological analysis is performed on store risk labels to extract risk source nodes, transmission path nodes, and risk convergence points, and a three-dimensional risk transmission network map is constructed.

[0047] Based on the risk transmission network graph, key risk nodes are identified, and real-time interception strategies including forced blocking, secondary verification, and manual review are generated according to the key risk nodes and real-time business scenario characteristics.

[0048] Potential transmission paths can be identified through path sensitivity analysis, and an early warning escalation mechanism linked to risk levels can be established.

[0049] A feedback loop of historical handling effects is introduced to dynamically evaluate the interception success rate and path warning accuracy of each node. Based on the evaluation results, the risk weight coefficient and handling response threshold are adjusted to generate dynamic adjustment rules.

[0050] Furthermore, the targeted governance plan will be validated in a closed loop with real-time operational data, including:

[0051] Using risk transmission network maps and targeted governance solutions as inputs, a closed-loop verification model is established, which includes a strategy execution module, an effect evaluation module, and a solution optimization module.

[0052] The strategy execution module injects real-time interception strategies into the risk control system and outputs strategy hit records and false interception analysis reports.

[0053] The effect evaluation module collects early warning handling logs and outputs an early warning response timeliness matrix and a false alarm / missed alarm statistics table.

[0054] The scheme optimization module receives policy hit records, false interception analysis reports, early warning response time matrix, and false alarm / missed alarm statistics table, and outputs dynamically adjusted risk transmission network parameters and governance strategy weight coefficients.

[0055] Furthermore, the output includes a risk governance report that includes an assessment of the effectiveness of the intervention and recommendations for optimizing the plan, including:

[0056] Based on the strategy hit record, a risk interception effectiveness analysis is generated, and the interception success rate, false judgment rate and average response time of key risk nodes are output as the first governance information.

[0057] An early warning response timeliness matrix is ​​used to construct an early warning and handling evaluation model, which outputs the early warning accuracy, handling timeliness deviation and escalation mechanism trigger frequency of each transmission path, and is recorded as the second governance information.

[0058] Based on the false alarm and missed alarm statistics table, strategy sensitivity analysis is performed, and false alarm rate curves and missed alarm risk heat maps under different risk levels are output, which are denoted as third governance information.

[0059] Based on the dynamically adjusted risk transmission network parameters and governance strategy weight coefficients, the system outputs strategy optimization suggestions that include node importance ranking and path sensitivity change trends, which are denoted as the fourth governance information.

[0060] The first, second, third, and fourth governance information are integrated into a risk governance report.

[0061] In a second aspect, the present invention also provides a system for operational risk management, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for operational risk management according to any one of the present invention.

[0062] Unlike existing technologies, the above technical solution provides a method and system for operational risk governance. The method includes: collecting store operational data according to a preset cycle; preprocessing the data to obtain structured risk information; performing quantitative assessment based on a dynamic quantile benchmark algorithm, calculating the deviation of business indicators from spatiotemporal dynamic benchmark values, determining the anomaly degree of core indicators, and outputting risk levels; generating store risk labels based on risk levels and the anomaly degree of core indicators, constructing a risk transmission network, and generating targeted governance solutions; and finally, outputting a risk governance report through closed-loop verification. This invention achieves dynamic quantitative assessment and precise targeted governance of operational risks. Through intelligent adjustment of spatiotemporal dynamic benchmark values ​​and the construction of a risk transmission network, it significantly improves the accuracy of risk identification and governance efficiency, while reducing the cost of manual intervention.

[0063] The above description of the invention is merely an overview of the technical solution of this application. In order to enable those skilled in the art to better understand the technical solution of this application and to implement it based on the description and drawings, and to make the above-mentioned objectives and other objectives, features and advantages of this application easier to understand, the following description is provided in conjunction with the specific embodiments and drawings of this application. Attached Figure Description

[0064] The accompanying drawings are only used to illustrate the principles, implementation methods, applications, features, and effects of specific embodiments of the present invention and other related contents, and should not be considered as limitations on this application.

[0065] In the accompanying drawings of the instruction manual:

[0066] Figure 1 This is a flowchart illustrating steps S101 to S105 of the method described in the specific implementation.

[0067] Figure 2 This is a flowchart illustrating steps S201 to S204 of the method described in a specific embodiment.

[0068] Figure 3 This is a flowchart illustrating steps S301 to S306 of the method described in a specific implementation.

[0069] Figure 4 This is a flowchart illustrating steps S401 to S405 of the method described in a specific embodiment.

[0070] Figure 5 The method steps S501 to S504 are shown in the figure for specific implementation of the method. Detailed Implementation

[0071] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this application in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this application and are therefore intended to limit the scope of protection of this application.

[0072] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0073] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit this application.

[0074] In the description of this application, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" logical relationship.

[0075] In this application, terms such as “first” and “second” are used only to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy or order relationship between these entities or operations.

[0076] Without further limitations, the use of terms such as “comprising,” “including,” “having,” or other similar open-ended expressions in this application is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0077] As understood in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceeding" are understood to exclude the stated number; expressions such as "above," "below," and "within" are understood to include the stated number. Furthermore, in the description of the embodiments in this application, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise explicitly specified.

[0078] In the description of the embodiments of this application, the space-related expressions used, such as "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "vertical," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiments or drawings. They are only for the purpose of describing the specific embodiments of this application or for the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0079] The processor described in the embodiments of this application can be implemented by hardware, firmware, software, or a combination thereof. It can be a circuit, one or more of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, or a microprocessor. It also includes other physical, biological, or chemical structures that can implement the same or equivalent functions as the processors listed above, such as biological neurons, quantum computing units, DNA computing units, etc., so that the processor can execute some or all of the steps in the computer program or method involved in the various embodiments of this application, or any combination of the steps mentioned therein.

[0080] The computer program involved in the embodiments can be stored in a computer device readable storage medium, which includes, but is not limited to, disks, magnetic tapes, magnetic cards, floppy disks, flash memory, optical disks, optical cards, read-only memory (ROM), random access memory (RAM), erasable programmable ROM (EPROM), and electrically erasable programmable ROM (EEPROM), etc., and also includes other biological, physical, or chemical structures that can achieve the same or equivalent functions as the storage media listed above, such as DNA, RNA, proteins, and other units with information storage capabilities. In specific embodiments, the storage medium involved can be one of the above-mentioned media types, or a combination of the above-mentioned media types. In different embodiments, the computer program involved in the embodiments can be centrally stored in a single medium, or distributed and stored in multiple media. The memory containing the computer device readable storage medium can be non-volatile memory or random access memory. These computer device readable storage media can be built into the device, or can be connected to the device involved in the embodiments as an external device or part of an external device. In some embodiments, the memory having a computer device readable storage medium is deployed locally; in other embodiments, the memory may be deployed remotely from the processor, for example, as a network-attached memory accessed via RF circuitry or an external port and a communication network, wherein the communication network may be the Internet, one or more intranets, a local area network (LAN), a wide area network (WLAN), a storage area network (SAN), or a suitable combination thereof, as long as computer device access to the memory is enabled. Furthermore, the computer program involved in the embodiments may be stored in plaintext / ciphertext form, or it may be designed as training data, integrated and recombined through model training and implicitly stored in the parameter states of a deep neural network or other machine learning model.

[0081] Please see Figure 1 In a first aspect, this embodiment provides a method for operational risk governance, including:

[0082] S101. Collect store operation data according to the preset cycle. Store operation data includes work behavior logs, transaction records and system operation trajectory.

[0083] S102. Preprocess store operation data to obtain structured risk information. Preprocessing includes data cleaning, field standardization, and operation link reconstruction.

[0084] S103. Quantitatively assess structured risk information based on the dynamic quantile benchmark algorithm. The quantitative assessment includes:

[0085] Calculate the deviation of each business indicator from the spatiotemporal dynamic benchmark value, which is dynamically generated through a three-dimensional weight matrix of region, time period, and business type.

[0086] The degree of abnormality of core indicators is determined based on the degree of deviation, and the risk level is output in combination with the preset risk ladder model;

[0087] Store risk labels are generated based on risk level and the anomaly of core indicators.

[0088] S104. Construct a risk transmission network based on store risk labels, and generate targeted governance solutions based on the risk transmission network;

[0089] S105. Conduct closed-loop verification of the targeted governance plan with real-time operational data, and output a risk governance report that includes an assessment of the treatment effect and suggestions for plan optimization.

[0090] In step S101, store operation data is a basic data set reflecting the store's operational status. This includes work behavior logs recording employee actions, transaction logs storing transaction details, and system operation tracing tracking of system interactions. This data is automatically collected by the business system and stored in a distributed database, providing raw data support for risk identification. Furthermore, the preset period can be configured as an hourly or daily collection frequency according to business needs, balancing real-time performance with system load.

[0091] In step S102, the heterogeneous operational data is transformed into analyzable structured data through preprocessing. Data cleaning removes invalid records and repairs missing values, field standardization unifies the format across different data sources, and operation link reconstruction reconstructs the complete business process through time-series analysis. Preferably, data cleaning uses a combination of rule engines and machine learning to identify outliers, field standardization is achieved through a metadata mapping table, and operation link reconstruction associates discrete events based on event timestamps and operation IDs. This step effectively improves data quality and provides reliable input for subsequent analysis.

[0092] In step S103, the dynamic quantile benchmark algorithm refers to a risk quantification method that considers spatiotemporal business differences. The spatiotemporal dynamic benchmark value is dynamically calculated and generated through a three-dimensional weight matrix of region, time period, and business type, reflecting the normal operation level under different scenarios. Preferably, the deviation calculation adopts the quantile comparison method to quantify the degree of difference between the current indicator and the historical benchmark; the anomaly degree of the core indicator is obtained by weighted aggregation of the deviation degrees of each dimension, and the risk ladder model maps the anomaly degree to discrete risk levels. The dynamic quantile benchmark algorithm avoids the misjudgment problem caused by traditional fixed thresholds by adaptive benchmark adjustment.

[0093] In steps S104 and S105, the store risk label is an identifier generated by combining the risk level and abnormal characteristics. It encodes core risk indicators through feature vectors and is associated with confidence assessments. The risk transmission network models risk propagation relationships based on a graph structure, where nodes represent risk sources or transmission hubs, and edges represent the strength of transmission paths. Targeted governance solutions are generated based on network topology characteristics, including node-level interception strategies and path-level early warning rules. Closed-loop verification evaluates governance effectiveness through real-time data feedback, and the risk governance report integrates execution indicators and optimization suggestions to form a continuous improvement mechanism.

[0094] This embodiment leverages the synergy of a dynamic quantile benchmark algorithm and a risk transmission network. By establishing a spatiotemporal dynamic benchmark value as a reference system for risk quantification and employing a three-dimensional weight matrix to dynamically adjust evaluation standards, risk identification can adapt to the characteristics of different regions, time periods, and business types, achieving accurate identification and targeted governance of operational risks. During implementation, heterogeneous store operational data is first transformed into structured risk information. Then, business anomalies are captured through deviation calculation, and finally, the anomaly degree is converted into an actionable risk level using a risk ladder model. The transmission network built based on risk tags visualizes the risk propagation path, while the closed-loop verification mechanism ensures continuous optimization of governance strategies.

[0095] This embodiment significantly improves the sensitivity and accuracy of risk identification. Dynamic calibration across the spatiotemporal dimensions avoids misjudgments caused by traditional static thresholds. Furthermore, the introduction of a risk transmission network enables governance measures to precisely pinpoint key risk nodes and transmission paths. The resulting risk governance report not only includes an assessment of the effectiveness of interventions but also provides optimization suggestions based on actual operational data, achieving a complete closed loop from risk monitoring to governance optimization. This method, combining dynamic quantification, networked analysis, and feedback verification, ensures comprehensive risk coverage while significantly improving the targeting and timeliness of governance measures.

[0096] Please see Figure 2 In some embodiments, store operation data is preprocessed to obtain structured risk information. Preprocessing includes data cleaning, field standardization, and operational chain reconstruction, including:

[0097] S201. Perform abnormal operation identification and compliance verification on the work behavior log, remove records of illegal operations and mark suspicious behavior fragments to obtain the preprocessed work behavior log.

[0098] S202. Perform integrity verification and amount threshold filtering on the transaction log records, repair missing transaction fields and remove abnormal transactions that exceed the preset amount threshold to obtain the preprocessed transaction log records.

[0099] S203. Perform time-series reconstruction and operation chain parsing on the system operation trajectory to restore the complete operation path and identify discontinuous operation segments, thereby obtaining the preprocessed system operation trajectory.

[0100] S204. Align the preprocessed job behavior logs, transaction flow records, and system operation trajectories according to a unified time base, and perform standardized transformation to obtain structured risk information.

[0101] In step S201, the anomaly identification and compliance verification refers to the process of dual screening of job behavior logs through a preset rule engine and a machine learning model. Specifically, records of violations are directly removed by matching them against a compliance rule base, while suspicious behavior fragments are marked and retained using an anomaly detection algorithm for subsequent analysis. Preferably, compliance verification can combine a job authority matrix and operation frequency thresholds for multi-dimensional judgment, ensuring that both explicit violations and potential risk patterns are identified. This step effectively filters noisy data while retaining valuable risk clues.

[0102] In step S202, the integrity check addresses the issue of missing key fields in transaction records by intelligently supplementing necessary information through association with transaction context and business rules. The amount threshold filtering employs a dynamic range setting, automatically adjusting the criteria for judging abnormal amounts based on the historical transaction distribution characteristics of the store. Preferably, missing field repair combines similar transaction pattern matching and business flow inference, while the amount threshold is dynamically updated using a moving quantile algorithm. This process ensures data integrity while avoiding false filtering caused by fixed thresholds.

[0103] In step S203, time-series reconstruction reassembles discrete events using operation timestamps and session IDs, while operation chain parsing identifies breakpoints and jumps in critical business processes. Non-continuous operation segments are detected using path matching algorithms, and their risk level is assessed in conjunction with the business scenario. Preferably, time-series reconstruction considers the impact of system response latency and concurrent operations, employing an event-time processing mechanism to ensure time-series accuracy. This step restores the true business flow, providing a reliable foundation for subsequent risk transmission analysis.

[0104] In step S204, a unified time base is achieved through a distributed clock synchronization protocol, while the standardization transformation is performed based on predefined metadata specifications. The preprocessed multi-source data is aligned using event time windows and converted into a unified data model and encoding format. Preferably, time alignment considers clock deviations across systems and employs event timestamp correction technology; the standardization process preserves the semantic information of the original data, ensuring that subsequent analysis is not distorted. The structured risk information ultimately output by this step possesses characteristics of temporal consistency, standardized format, and semantic clarity.

[0105] This embodiment transforms raw store operation data into high-quality structured risk information through a layered and progressive data processing flow. By employing a dynamic adjustment mechanism instead of fixed rules, it ensures both processing efficiency and adaptability to business changes. The processing strategy, combining hard filtering with flexible labeling, preserves risk characteristics to the maximum extent while maintaining data quality. The preprocessing process in this embodiment provides an accurate, complete, and time-series-correlated data foundation for subsequent risk quantification assessment.

[0106] Please see Figure 3 In some embodiments, the deviation of each business indicator from the spatiotemporal dynamic benchmark value is calculated. The spatiotemporal dynamic benchmark value is dynamically generated through a three-dimensional weight matrix of region, time period, and business type, including:

[0107] S301. The area where the store is located is gridded and regional weight coefficients are generated. The regional weight coefficients are dynamically adjusted based on the distribution density of historical risk events.

[0108] S302. Classify business peaks during operating periods and generate time period weighting coefficients. The time period weighting coefficients are dynamically configured based on transaction volume fluctuation characteristics.

[0109] S303. Mark the risk level of business types and generate business type weight coefficients. The business type weight coefficients are preset with a benchmark value based on regulatory requirements.

[0110] S304. Construct a three-dimensional weight matrix based on regional weight coefficients, time period weight coefficients, and business type weight coefficients;

[0111] S305. Extract current business metrics from structured risk information and match them with the corresponding three-dimensional weight matrix;

[0112] S306. Calculate the deviation of the current business indicator value from the historical benchmark value of the same region, same time period and same business type.

[0113] In step S301, grid coding refers to the process of dividing the area where the store is located into standard geographical units. Regional weight coefficients reflect the risk sensitivity of each unit and are dynamically adjusted through heatmap analysis of historical risk events. Preferably, the grid division considers administrative divisions and commercial clustering characteristics, and the weight coefficients are updated periodically using a sliding window algorithm to ensure timely reflection of regional risk trends. This step ensures that regional differences are reasonably reflected in the benchmark value calculation.

[0114] In step S302, the peak business classification identifies typical operating period characteristics through cluster analysis, and the period weight coefficients are dynamically configured based on transaction volume volatility and abnormal event occurrence rates. Preferably, the peak classification employs adaptive time slicing technology to avoid insufficient sensitivity caused by fixed period divisions; the period weights achieve a reasonable transition between old and new data through an exponential decay model, maintaining stability while capturing the latest trends.

[0115] In step S303, the risk level labeling is based on the inherent attributes of the business and regulatory requirements. After setting a benchmark value for the business type weight coefficient, it is fine-tuned based on actual risk performance. Preferably, high-risk business types, such as large cash transactions, are preset with a higher benchmark, which is then dynamically adjusted based on real-time monitoring data, thus meeting both the rigid requirements of regulation and retaining business flexibility.

[0116] In step S304, the three-dimensional weight matrix integrates spatial, temporal, and business dimension features through tensor operations, and the coefficients of each dimension are normalized to ensure comparability. Preferably, the matrix construction considers the interaction effects between dimensions; for example, high-risk businesses may receive higher weights in specific regions or time periods. This matrix serves as the computational framework for a dynamic benchmark, achieving the organic fusion of multi-dimensional features.

[0117] In step S305, business indicator matching is achieved through metadata identification, ensuring that the current indicator and historical data are strictly aligned in three-dimensional features. The matching process uses fuzzy search technology to handle boundary cases, such as intelligent association of adjacent regions or similar business types. This step provides an accurate comparison benchmark for deviation calculation.

[0118] In step S306, the deviation calculation uses a standardized relative difference measure, which includes both absolute differences and distribution characteristics. Preferably, quantile comparison is used for asymmetric distribution indicators, and a seasonal adjustment factor is introduced for periodic indicators. This multi-mode calculation method adapts to the characteristics of various business indicators and improves the accuracy of anomaly identification.

[0119] This embodiment addresses the issue of significant environmental interference in traditional risk monitoring by employing a three-dimensional dynamic benchmark system. The dynamic adjustment mechanism of the weights for each dimension ensures that the benchmark value always reflects the latest operating environment, while intelligent matching and diversified deviation calculation methods guarantee objective and accurate assessment results. The method for constructing the three-dimensional weight matrix achieves synergy between spatial, temporal, and business characteristics, providing a scientific reference system for risk quantification. This embodiment significantly improves the environmental adaptability and assessment accuracy of risk monitoring through multi-dimensional dynamic benchmark technology.

[0120] Please see Figure 4 In some embodiments, the abnormality of core indicators is determined based on the deviation, and a risk level is output in conjunction with a preset risk ladder model, including:

[0121] S401. Normalize the deviation and convert it into an anomaly score in the range of 0-100.

[0122] S402. Based on the business type, the anomaly score is divided into three dimensions: transaction security, operation compliance, and system stability.

[0123] S403, Set transaction security risk thresholds, operational compliance risk thresholds, and system stability risk thresholds;

[0124] S404. Match the anomaly scores of each dimension with the transaction security risk threshold, operation compliance risk threshold, and system stability risk threshold to generate the dimension risk level.

[0125] S405. Based on the preset risk ladder model, the dimensional risk levels are weighted and aggregated to output the final risk level, which includes four levels: normal, attention, warning, and severe.

[0126] In step S401, normalization refers to the process of converting deviations of different dimensions into a unified dimension. The anomaly score can be linearly mapped to the 0-100 range using the range method. Preferably, for non-normally distributed deviations, percentile ranking transformation is used to ensure that the score accurately reflects the position of the anomaly degree in the overall distribution. This step achieves standardized comparability of multi-source indicators, laying the foundation for subsequent risk classification.

[0127] In step S402, the business type is categorized based on three dimensions: transaction security, operational compliance, and system stability. The scores for each dimension are automatically classified using metadata tags. Specifically, the transaction security category primarily monitors abnormal fund flows, the operational compliance category focuses on identifying procedural violations, and the system stability category specifically detects technical fault indicators. This classification and evaluation method can specifically identify anomalies of different natures.

[0128] In step S403, the risk threshold setting adopts dynamic baseline technology. The transaction security risk threshold primarily considers the flow of funds, the operational compliance risk threshold refers to internal control standards, and the system stability risk threshold is based on system performance indicators. Preferably, each threshold is updated periodically through a sliding time window, maintaining the stability of the evaluation standards while adapting to business changes.

[0129] In step S404, the dimensional risk level matching employs an interval mapping method, transforming the relative positions of each dimension's anomaly score value with the transaction security risk threshold, operational compliance risk threshold, and system stability risk threshold into discrete levels. Preferably, buffer zones are set to avoid frequent fluctuations in boundary values, while a lag comparison strategy is used to enhance level stability. This step transforms continuous anomaly score values ​​into discrete risk level signals.

[0130] In step S405, the risk ladder model aggregates multi-dimensional levels through a weighted rule engine, with the weights of different dimensions dynamically configured according to business priorities. The final risk level is generated using a strategy combining majority voting and weighted averaging, and the four-level classification meets the progressive response requirements of risk management.

[0131] This embodiment eliminates assessment bias caused by differences in indicators through a standardized scoring system, ensures the timeliness of anomaly identification through dynamic threshold settings, and achieves intelligent mapping from technical anomalies to business risks through a preset risk ladder model. In particular, the three-dimensional independent assessment mechanism based on business type not only preserves the differences in various business indicators but also provides an overall risk assessment through weighted aggregation, providing a scientific basis for subsequent precise governance decisions.

[0132] This embodiment normalizes the original deviation into a standardized anomaly score, eliminating differences in indicator dimensions. Then, it performs independent assessments based on business type, setting differentiated risk thresholds for different risk characteristics. Finally, it achieves scientific aggregation of multi-dimensional risk levels through a pre-set risk ladder model. This embodiment ensures the comparability of multi-source indicators through standardization, maintains the relevance of risk characteristics through business-dimensional classification assessment, balances standard stability and adaptability with dynamic threshold settings, and provides a clear decision-making basis for the four-level risk classification. Ultimately, it achieves accurate transformation from technical indicators to management signals, ensuring that anomaly detection results reflect specific business characteristics while maintaining overall comparability, providing a hierarchical response foundation for risk governance.

[0133] Please see Figure 5 In some embodiments, the dimensional risk levels are weighted and aggregated based on a preset risk ladder model to output the final risk level, including:

[0134] S501. Establish dimensional risk mapping relationships and convert the anomaly scores of transaction security, operation compliance and system stability into standard risk equivalents respectively.

[0135] S502. Construct a risk transmission matrix and quantify the mutual influence coefficients between the standard risk equivalents of each dimension;

[0136] S503. Employs a dynamic weighted algorithm to automatically adjust the weight ratio of risk equivalent in each dimension based on the characteristics of the current business scenario, including:

[0137] Real-time data collection of transaction volume volatility coefficient, operational behavior dispersion, and system load rate as scenario characteristic parameters;

[0138] The scene feature parameters are input into the weight response function to calculate the dynamic weights of each dimension, and the sum of the weights is constrained to be 1 and within a preset range to obtain the weighted standard risk equivalent.

[0139] The weighted standard risk equivalents are nonlinearly superimposed to obtain the aggregated result.

[0140] S504. Set risk level determination rules and determine the final risk level based on the risk range in which the aggregation result is located;

[0141] Furthermore, when the standard risk equivalent in any dimension is detected to exceed the critical threshold, a cross-dimensional risk reassessment mechanism is triggered.

[0142] In step S501, the standard risk equivalent refers to converting the anomaly scores of different business dimensions into a comparable unified risk measurement unit. This is achieved by linearly transforming the anomaly scores in the 0-100 range onto a standard risk scale. Preferably, transaction security is calculated using the probability of financial loss, operational compliance using the severity of violations, and system stability based on the scope of the fault's impact, ensuring the comparability of risk equivalents across different dimensions in terms of business meaning.

[0143] In step S502, the risk transmission matrix is ​​used to describe the mutual influence of risks across various business dimensions. Its diagonal elements represent the risk intensity of each dimension itself, while the off-diagonal elements quantify the risk transmission effect between dimensions. Preferably, the transmission coefficient between transaction security and system stability is determined through statistical analysis of historical failure-induced financial loss cases, and the transmission coefficient between operational compliance and transaction security is obtained based on the analysis of financial risk events caused by violations. Furthermore, the risk transmission matrix can be dynamically updated through covariance analysis and Granger causality tests.

[0144] In step S503, the dynamic weighting algorithm adaptively adjusts the weights based on real-time business scenario characteristics: the transaction volume volatility coefficient reflects market activity and is obtained by calculating the standard deviation of transaction volume through a sliding time window; the operational behavior dispersion characterizes the heterogeneity of user behavior and is calculated based on the entropy value of the operational sequence; the system load rate is obtained in real time through resource monitoring data. Preferably, the weight response function adopts a constrained softmax function to ensure that the weight distribution conforms to the business logic and is interpretable; the nonlinear superposition calculation introduces a sigmoid activation function to handle the superposition effect of extreme risks.

[0145] In step S504, the risk level determination rule uses a piecewise linear function to divide the risk interval, and the interval boundary values ​​are determined through historical event backtracking analysis. The cross-dimensional risk reassessment mechanism is triggered when the risk equivalent of any dimension exceeds its business tolerance limit. At this time, the regular aggregation process is paused, and the risk contagion path analysis between dimensions is performed first to recalibrate the transmission matrix and weight parameters.

[0146] This embodiment addresses the comparability issue of multi-dimensional risks by establishing a standardized risk equivalent system, capturing the correlation of business risks using a transmission matrix, and ensuring the timeliness of assessment through dynamic weighting based on scenario characteristics. The cross-dimensional risk reassessment mechanism maintains the efficiency of conventional assessments while initiating in-depth analysis when critical risks emerge, achieving a balance between risk identification accuracy and response speed. This embodiment transforms scattered business anomaly signals into a systematic basis for risk decision-making through a three-level processing approach: risk quantification, transmission modeling, and dynamic weighting.

[0147] In some embodiments, store risk labels are generated based on risk level and the anomaly of core indicators, including:

[0148] Based on the risk level, a risk level identifier is obtained, and the anomaly degree of the core indicators is decomposed in multiple dimensions to extract transaction security anomaly factors, operational compliance deviation factors and system stability fluctuation factors, and generate an indicator anomaly feature vector.

[0149] Construct a risk label mapping rule base, combine and match risk level identifiers with indicator anomaly feature vectors, and output initial risk labels through preset label generation logic;

[0150] The initial risk labels are validated in context, and the confidence level of the labels is adjusted by combining the store's historical risk records and industry risk profiles. The final store risk labels are then generated and linked to the store risk profile library.

[0151] In this embodiment, the risk level identifier is a standardized representation of continuous risk levels (normal, attention, warning, severe) converted into discrete symbols, which can be quickly identified using letter encoding or color encoding. The indicator anomaly feature vector is a coordinate point in a three-dimensional feature space, where the transaction security anomaly factor is extracted through abnormal fund flow patterns, the operational compliance deviation factor is calculated based on the degree of violation of operational procedures, and the system stability fluctuation factor reflects the degree to which system performance deviates from the baseline. The risk label mapping rule base uses a decision tree structure to store matching rules. Each rule contains a combination of risk level identifier and feature vector threshold conditions, and pattern matching is performed through a rule engine.

[0152] Preferably, the contextual validation process incorporates a Bayesian probability model, using historical risk records of stores as prior probabilities and industry risk profiles as likelihood functions to calculate posterior confidence scores for the labels. The store risk profile database is constructed using a graph database, where nodes represent store entities and edges record the spatiotemporal propagation relationships of risk labels. The pre-defined label generation logic comprises two stages: feature weighting and logical operations. First, business weights are assigned to anomaly factors across each dimension, and then initial labels are generated through logical gate combinations.

[0153] This embodiment achieves accurate risk labeling for stores by constructing a multi-dimensional risk feature system: it combines discrete risk level identifiers with continuous indicator anomaly feature vectors, generates standardized labels through a risk label mapping rule base, and then calibrates confidence levels by combining historical data and industry characteristics. In this embodiment, multi-dimensional decomposition preserves the business attributes of anomaly features, rule mapping ensures the standardization of label generation, and contextual validation enhances the adaptability and accuracy of initial risk labels. The final store risk labels reflect both the current risk status and have historical comparability, providing a complete solution for store risk management from anomaly detection to label-based management, and realizing the visualization, standardization, and traceability of risk identification results.

[0154] In some embodiments, the targeted governance scheme includes a real-time interception strategy for key risk nodes, an early warning escalation mechanism for potential transmission paths, and dynamic adjustment rules based on feedback from historical treatment effects.

[0155] A risk transmission network is constructed based on store risk tags, and targeted governance solutions are generated based on this network, including:

[0156] Topological analysis is performed on store risk labels to extract risk source nodes, transmission path nodes, and risk convergence points, and a three-dimensional risk transmission network map is constructed.

[0157] Based on the risk transmission network graph, key risk nodes are identified, and real-time interception strategies including forced blocking, secondary verification, and manual review are generated according to the key risk nodes and real-time business scenario characteristics.

[0158] Potential transmission paths can be identified through path sensitivity analysis, and an early warning escalation mechanism linked to risk levels can be established.

[0159] A feedback loop of historical handling effects is introduced to dynamically evaluate the interception success rate and path warning accuracy of each node. Based on the evaluation results, the risk weight coefficient and handling response threshold are adjusted to generate dynamic adjustment rules.

[0160] In this embodiment, the three-dimensional risk transmission network map is a three-dimensional network model constructed using graph theory. Risk source nodes represent the initial location of the risk, transmission path nodes represent intermediate links in the risk propagation, and risk convergence points reflect the final target affected by the risk. This map extracts relationships from store risk labels using a topology analysis algorithm and employs a force-directed layout algorithm for visualization.

[0161] Key risk nodes are those with the highest degree centrality in the three-dimensional risk transmission network graph, which can be identified using the PageRank algorithm. They control the main channels of risk transmission. The real-time interception strategy involves forced blocking, which directly terminates suspicious transaction processes via system commands. Secondary verification uses a multi-factor authentication mechanism to verify the operator's identity, while manual verification triggers intervention from risk control specialists.

[0162] The path sensitivity analysis uses a random walk algorithm to simulate the risk transmission process, calculates the vulnerability index for each path, and dynamically adjusts the warning escalation mechanism based on this index to adjust the warning level and response time. A historical response effect feedback loop establishes a knowledge base of response records, analyzes the correlation between interception success rate and warning accuracy using a logistic regression model, and dynamically adjusts the rules based on the analysis results to optimize risk weight allocation and response threshold settings.

[0163] This embodiment achieves precise positioning and dynamic optimization of risk governance by constructing a risk transmission network graph. It transforms store risk labels into a three-dimensional network structure, identifies key risk nodes and potential transmission paths through topology analysis, and establishes a three-tiered defense system of real-time interception, early warning escalation, and dynamic adjustment. In this embodiment, the three-dimensional risk transmission network graph visually displays the risk transmission topology, the real-time interception strategy enables precise control of key nodes, the early warning escalation mechanism strengthens the defense capabilities of transmission paths, and the dynamic adjustment rules continuously optimize governance strategies based on historical feedback, forming a closed-loop management system from risk identification to optimized handling. This significantly improves the targeting and timeliness of risk governance, achieving a shift from passive response to proactive prevention.

[0164] In some embodiments, the targeted governance scheme is validated in a closed loop with real-time operational data, including:

[0165] Using risk transmission network maps and targeted governance solutions as inputs, a closed-loop verification model is established, which includes a strategy execution module, an effect evaluation module, and a solution optimization module.

[0166] The strategy execution module injects real-time interception strategies into the risk control system and outputs strategy hit records and false interception analysis reports.

[0167] The effect evaluation module collects early warning handling logs and outputs an early warning response timeliness matrix and a false alarm / missed alarm statistics table.

[0168] The scheme optimization module receives policy hit records, false interception analysis reports, early warning response time matrix, and false alarm / missed alarm statistics table, and outputs dynamically adjusted risk transmission network parameters and governance strategy weight coefficients.

[0169] In this embodiment, the strategy execution module embeds the real-time interception strategy into the risk control system through the API interface, the effect evaluation module obtains the early warning and handling data through the log collection system, and the scheme optimization module uses machine learning algorithms to analyze and verify the results.

[0170] The strategy hit record tracks the number of times the interception strategy is triggered and its hit rate. The false interception analysis report summarizes cases where normal business operations are mistakenly identified as risk events. Both are generated through analysis of the risk control system logs. The early warning response timeliness matrix records the time distribution from early warning issuance to completion of handling. The false alarm and missed alarm statistics table summarizes the number of erroneous and missed early warnings. Both are obtained through analysis of the early warning system logs.

[0171] The dynamically adjusted risk transmission network parameters include node association weights and path transmission probabilities. Governance strategy weight coefficients reflect the effectiveness score of each strategy, and both are optimized using a gradient descent algorithm. The risk transmission network graph serves as the validation foundation, and its node connections are stored and updated through a graph database. The strategy parameters in the targeted governance scheme are dynamically adjusted through a configuration management center to ensure that the validation process is synchronized with the actual risk control environment.

[0172] This embodiment achieves continuous optimization of the governance solution by establishing a closed-loop verification mechanism. The strategy execution module ensures seamless integration between the verification process and actual business operations, the effect evaluation module provides multi-dimensional quantitative evaluation indicators, and the solution optimization module automatically adjusts strategy parameters based on data feedback. By combining the risk transmission network map with real-time operational data for verification, the effectiveness of the governance solution is tested, and the accuracy of the network model is optimized, forming a complete closed loop from theoretical solution to practical verification and parameter tuning.

[0173] In some embodiments, the output includes a risk governance report containing an assessment of the effectiveness of the intervention and recommendations for optimizing the approach, including:

[0174] Based on the strategy hit record, a risk interception effectiveness analysis is generated, and the interception success rate, false judgment rate and average response time of key risk nodes are output as the first governance information.

[0175] An early warning response timeliness matrix is ​​used to construct an early warning and handling evaluation model, which outputs the early warning accuracy, handling timeliness deviation and escalation mechanism trigger frequency of each transmission path, and is recorded as the second governance information.

[0176] Based on the false alarm and missed alarm statistics table, strategy sensitivity analysis is performed, and false alarm rate curves and missed alarm risk heat maps under different risk levels are output, which are denoted as third governance information.

[0177] Based on the dynamically adjusted risk transmission network parameters and governance strategy weight coefficients, the system outputs strategy optimization suggestions that include node importance ranking and path sensitivity change trends, which are denoted as the fourth governance information.

[0178] The first, second, third, and fourth governance information are integrated into a risk governance report.

[0179] In this embodiment, risk interception effectiveness analysis is a quantitative evaluation of the execution effect of real-time interception strategy. The interception success rate is calculated by the ratio of the number of valid interceptions in the hit record to the total number of triggers. The false judgment rate is the proportion of normal business being erroneously intercepted. The average response time is the average time from strategy triggering to completion of handling.

[0180] The early warning and response assessment model is constructed using time series analysis. The early warning accuracy is measured by the ratio of the number of correct early warnings to the total number of early warnings. The deviation in response time reflects the degree of difference between the actual response time and the standard time. The escalation mechanism trigger frequency counts the number of times the early warning level is automatically upgraded.

[0181] The strategy sensitivity analysis is achieved through a machine learning model. The false alarm rate curve shows the probability distribution of false alarms under different risk thresholds, and the missed alarm risk heat map identifies high-risk areas through spatial clustering algorithms.

[0182] Preferably, the node importance ranking is calculated based on the PageRank algorithm, reflecting the criticality of each node in the network; the path sensitivity change trend is shown through time series comparative analysis, demonstrating the dynamic evolution of the vulnerability of the transmission path.

[0183] The risk governance report integrates four types of governance information through data visualization technology. The first type of governance information evaluates the effectiveness of interception strategies, the second type verifies the rationality of early warning mechanisms, the third type analyzes the adaptability of strategy parameters, and the fourth type guides the optimization direction of network structure and strategy weights.

[0184] This embodiment forms a complete feedback loop for risk governance by providing multi-dimensional governance effectiveness evaluation and optimization suggestions. Risk interception effectiveness analysis verifies the accuracy of interception strategies, the early warning and handling evaluation model optimizes the early warning response process, strategy sensitivity analysis calibrates risk judgment criteria, and strategy optimization suggestions guide system parameter adjustments. By combining quantitative indicators with visual analysis, the current governance effectiveness is comprehensively evaluated, providing data support for subsequent optimization and achieving a virtuous cycle of risk governance from execution to evaluation to improvement.

[0185] In a second aspect, this embodiment also provides a system for operational risk management, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for operational risk management according to any one of the present invention.

[0186] In this embodiment, the system for operational risk governance includes a risk transmission network construction module, a targeted governance scheme generation module, and a closed-loop verification module. The risk transmission network construction module uses a graph computing engine to perform topology analysis, the targeted governance scheme generation module uses a rule engine and a machine learning model, and the closed-loop verification module includes a policy executor, an evaluation analyzer, and an optimization controller.

[0187] The system is deployed using a distributed architecture, with modules interacting via message queues to ensure stable operation under high concurrency scenarios. The risk transmission network construction module transforms store risk labels into a risk transmission network graph, the targeted governance solution generation module formulates interception strategies and early warning mechanisms, and the closed-loop verification module executes strategies and provides feedback on optimization suggestions.

[0188] This embodiment achieves intelligent operation management by constructing a complete risk governance closed loop. The risk transmission network construction module transforms scattered risk data into a visualized network structure, the targeted governance solution generation module formulates precise governance strategies based on network characteristics, and the closed-loop verification module monitors the strategy execution effect in real time and dynamically adjusts parameters. All modules of the system work collaboratively to form a complete link from risk identification to strategy execution and effect optimization, significantly improving the automation level and response speed of risk governance, realizing the transformation from manual intervention to system-driven decision-making, effectively reducing operational risks and improving management efficiency.

[0189] By adopting the above technical solutions, this invention differs from existing technologies and possesses the following beneficial effects: By constructing a governance system that combines a dynamic quantile benchmark algorithm with a risk transmission network, it achieves accurate identification and targeted governance of operational risks. First, the quantitative assessment method based on spatiotemporal dynamic benchmark values ​​effectively solves the misjudgment problem caused by traditional static thresholds. The assessment standards are dynamically adjusted through a three-dimensional weight matrix of region, time period, and business type, enabling risk identification to adapt to different scenario characteristics. Second, the construction of the risk transmission network map transforms discrete store risk labels into a visualized topological structure, accurately locating key risk nodes and transmission paths, providing a scientific basis for formulating targeted governance solutions. The closed-loop verification mechanism continuously optimizes governance strategies through real-time operational data feedback, forming a complete closed loop from risk monitoring to disposal assessment and solution adjustment. The final generated risk governance report integrates multi-dimensional assessment indicators and optimization suggestions, comprehensively reflecting the current governance effect and providing data support for subsequent improvements. The above technical solutions combine dynamic quantification, networked analysis, and feedback verification, significantly improving the pertinence and timeliness of governance measures while ensuring comprehensive risk coverage, achieving a shift from passive response to proactive prevention and control.

[0190] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. A method for operational risk governance, characterized in that, include: Store operation data is collected according to a preset cycle. The store operation data includes work behavior logs, transaction records, and system operation trajectories. The store operation data is preprocessed to obtain structured risk information. The preprocessing includes data cleaning, field standardization, and operation link reconstruction. The structured risk information is quantitatively assessed based on a dynamic quantile benchmark algorithm, and the quantitative assessment includes: Calculate the deviation of each business indicator from the spatiotemporal dynamic benchmark value, which is dynamically generated through a three-dimensional weight matrix of region, time period, and business type. The degree of abnormality of the core indicators is determined based on the deviation, and the risk level is output in combination with the preset risk ladder model; Store risk labels are generated based on the aforementioned risk level and the anomaly of core indicators. A risk transmission network is constructed based on the store risk labels, and a targeted governance plan is generated based on the risk transmission network. The targeted governance plan is validated in a closed loop with real-time operational data, and a risk governance report is output, which includes an assessment of the treatment effect and suggestions for plan optimization. Preprocessing of store operation data yields structured risk information. Preprocessing includes data cleaning, field standardization, and operational workflow reconstruction, including: The operation behavior log is subjected to abnormal operation identification and compliance verification, and records of illegal operation are removed and suspicious behavior fragments are marked to obtain the preprocessed operation behavior log; The transaction log records are subjected to integrity verification and amount threshold filtering. Missing transaction fields are repaired and abnormal transactions exceeding the preset amount threshold are removed to obtain the preprocessed transaction log records. The system operation trajectory is reconstructed in time and the operation chain is parsed to restore the complete operation path and identify discontinuous operation segments, thus obtaining the preprocessed system operation trajectory. The preprocessed job behavior logs, transaction records, and system operation trajectories are aligned according to a unified time base and standardized transformation is performed to obtain structured risk information. Calculate the deviation of each business indicator from the spatiotemporal dynamic benchmark value, which is dynamically generated through a three-dimensional weight matrix of region, time period, and business type, including: The area where the store is located is gridded to generate regional weight coefficients, which are dynamically adjusted based on the distribution density of historical risk events. The system classifies business peak periods by operating hours and generates time period weighting coefficients, which are dynamically configured based on transaction volume fluctuation characteristics. Risk levels are assigned to business types, and weight coefficients for each business type are generated. These weight coefficients are based on preset benchmark values ​​according to regulatory requirements. A three-dimensional weight matrix is ​​constructed based on regional weight coefficients, time period weight coefficients, and business type weight coefficients. Extract current business metrics from structured risk information and match them with corresponding three-dimensional weight matrices; Calculate the deviation of the current business indicator value from the historical benchmark value of the same region, time period, and business type; The targeted governance plan will be validated in a closed loop with real-time operational data, including: Using risk transmission network maps and targeted governance solutions as inputs, a closed-loop verification model is established, which includes a strategy execution module, an effect evaluation module, and a solution optimization module. The strategy execution module injects real-time interception strategies into the risk control system and outputs strategy hit records and false interception analysis reports. The effect evaluation module collects early warning handling logs and outputs an early warning response timeliness matrix and a false alarm / missed alarm statistics table. The scheme optimization module receives policy hit records, false interception analysis reports, early warning response time matrix, and false alarm / missed alarm statistics table, and outputs dynamically adjusted risk transmission network parameters and governance strategy weight coefficients.

2. The method for operational risk management according to claim 1, characterized in that, The core indicator anomaly degree is determined based on the deviation, and the risk level is output in conjunction with a preset risk ladder model, including: The deviation is normalized and converted into an anomaly score value in the range of 0-100; Based on the business type, the anomaly score is divided into three dimensions: transaction security, operational compliance, and system stability. Set transaction security risk thresholds, operational compliance risk thresholds, and system stability risk thresholds; The anomaly scores of each dimension are matched with the transaction security risk threshold, operational compliance risk threshold, and system stability risk threshold to generate a dimension risk level. The risk levels of each dimension are weighted and aggregated based on a preset risk ladder model to output the final risk level, which includes four levels: normal, attention, warning, and severe.

3. The method for operational risk management according to claim 2, characterized in that, The risk levels of each dimension are weighted and aggregated based on a pre-defined risk ladder model to output the final risk level, including: Establish a dimensional risk mapping relationship and convert the anomaly scores of transaction security, operational compliance and system stability into standard risk equivalents respectively; Construct a risk transmission matrix and quantify the mutual influence coefficients among the standard risk equivalents of each dimension; A dynamic weighted algorithm is used to automatically adjust the weight ratio of risk equivalent in each dimension based on the characteristics of the current business scenario, including: Real-time data collection of transaction volume volatility coefficient, operational behavior dispersion, and system load rate as scenario characteristic parameters; The scene feature parameters are input into the weight response function to calculate the dynamic weights of each dimension, and the total weights are constrained to be 1 and within a preset range to obtain the weighted standard risk equivalent. The weighted standard risk equivalents are nonlinearly superimposed to obtain the aggregated result. Set risk level determination rules and determine the final risk level based on the risk range in which the aggregation result is located; Furthermore, when the standard risk equivalent in any dimension is detected to exceed the critical threshold, a cross-dimensional risk reassessment mechanism is triggered.

4. The method for operational risk management according to claim 1, characterized in that, Store risk labels are generated based on the aforementioned risk level and the anomaly of core indicators, including: Based on the risk level, a risk level identifier is obtained, and the anomaly degree of the core indicator is decomposed in multiple dimensions to extract transaction security anomaly factor, operation compliance deviation factor and system stability fluctuation factor, and generate indicator anomaly feature vector. Construct a risk label mapping rule base, combine and match the risk level identifier with the indicator abnormal feature vector, and output the initial risk label through preset label generation logic; The initial risk label is validated in context, and the label confidence is adjusted by combining the store's historical risk records and industry risk profiles to generate the final store risk label and associate it with the store risk profile library.

5. The method for operational risk management according to claim 1, characterized in that, The targeted governance scheme includes real-time interception strategies for key risk nodes, early warning escalation mechanisms for potential transmission paths, and dynamic adjustment rules based on feedback from historical treatment effects. A risk transmission network is constructed based on the store risk tags, and a targeted governance plan is generated based on the risk transmission network, including: The risk labels of the stores are analyzed topologically to extract risk source nodes, transmission path nodes and risk convergence points, and a three-dimensional risk transmission network graph is constructed. Based on the risk transmission network graph, key risk nodes are identified, and real-time interception strategies including forced blocking, secondary verification, and manual review are generated according to the key risk nodes and real-time business scenario characteristics. Potential transmission paths can be identified through path sensitivity analysis, and an early warning escalation mechanism linked to risk levels can be established. A feedback loop of historical handling effects is introduced to dynamically evaluate the interception success rate and path warning accuracy of each node. Based on the evaluation results, the risk weight coefficient and handling response threshold are adjusted to generate dynamic adjustment rules.

6. The method for operational risk management according to claim 1, characterized in that, The output includes a risk governance report that assesses the effectiveness of the intervention and provides recommendations for optimizing the approach, including: Based on the hit records of the aforementioned strategy, a risk interception effectiveness analysis is generated, and the interception success rate, false judgment rate, and average response time of key risk nodes are output as indicators, which are recorded as the first governance information. Based on the aforementioned early warning response timeliness matrix, an early warning handling evaluation model is constructed, and the early warning accuracy, handling timeliness deviation, and escalation mechanism trigger frequency of each transmission path are output, which are denoted as the second governance information. Based on the false alarm and missed alarm statistics table, a strategy sensitivity analysis is performed, and the false alarm rate curves and missed alarm risk heatmaps under different risk levels are output, which are denoted as the third governance information. Based on the dynamically adjusted risk transmission network parameters and governance strategy weight coefficients, a strategy optimization suggestion containing node importance ranking and path sensitivity change trend is output, which is denoted as the fourth governance information. The first governance information, the second governance information, the third governance information, and the fourth governance information are integrated into the risk governance report.

7. A system for operational risk governance, characterized in that, The method applicable to any one of claims 1 to 6.

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