An information matching management system and method for financial marketing platform data

Abstract

The present invention relates to the field of data matching technology, specifically to an information matching management system and method for financial marketing platform data, the system comprising a data acquisition module, a data analysis module, a dynamic adaptation module, a marketing strategy module, an anomaly detection module, and a strategy implementation module. In the present invention, by comprehensively analyzing market data sets, cluster analysis helps to more deeply understand the behaviors and needs of different customer groups. Such statistical analysis can provide a scientific basis for the formulation of marketing strategies, enabling financial institutions to identify and utilize market trends and patterns on a data-driven basis, predict market changes and respond quickly, and improve risk assessment results and anomaly detection capabilities, not only optimizing resource allocation, but also enhancing control over potential risks, improving the overall efficiency and effectiveness of marketing activities, enhancing the accuracy of data-driven decision-making, improving customer service experience and brand loyalty, and promoting sales growth.

Description

Technical Field

[0001] The present invention relates to the field of data matching technology, and in particular to an information matching management system and method for financial marketing platform data. Background Art

[0002] The field of data matching technology involves the use of algorithms and computer systems to identify, compare and correlate similarities or correlations between data elements. This technology is widely used in various data-intensive applications, such as data integration, data cleaning and big data analysis. Through data matching, the system can automatically identify and integrate information from different sources to optimize the accuracy and consistency of data. It not only improves the efficiency of data processing, but also supports complex data analysis. Data matching technology is used in customer data management, financial transaction monitoring, and online recommendation systems to improve service personalization and user satisfaction through accurate matching.

[0003] Among them, the information matching management system of financial marketing platform data refers to a system that uses data matching technology to optimize financial marketing activities. By analyzing and matching customer data from different sources, the system helps financial institutions accurately locate target customer groups and design more personalized marketing strategies. The system can also monitor and analyze marketing effects and adjust marketing strategies in real time to adapt to market changes. The main uses of this system include improving customer response rates, optimizing marketing resource allocation, and enhancing customer service experience, driving sales growth and brand loyalty.

[0004] Existing data matching technologies are insufficient when processing highly dynamic market data, especially in the financial sector. The rapid changes in market events and the existence of batch heterogeneous data sources make it difficult for existing systems to synchronize and analyze data in real time and accurately. This limitation results in the inability to adjust marketing strategies in a timely manner, missed market opportunities, or delayed responses to risks. Existing technologies also face challenges in the implementation of personalized services. Due to the lack of in-depth clustering analysis and dependency exploration of customer behavior, marketing activities cannot accurately reach the target customer group, affecting customer response rates and the effective use of marketing resources. For example, in financial marketing platforms, the failure to accurately identify and predict changes in customer needs results in service provision that does not meet customer expectations, affecting customer satisfaction and the company's market competitiveness. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an information matching management system and method for financial marketing platform data.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: an information matching management system for financial marketing platform data includes:

[0007] The data acquisition module collects market event information and customer behavior data from the financial marketing platform, assigns time tags, synchronizes financial time series data, removes invalid and erroneous data, and generates a market data set;

[0008] The data analysis module performs hierarchical cluster analysis on the financial marketing platform data based on the market data set, calculates statistical features for each category, including mean and variance, draws a dependency graph, and obtains dependency analysis results;

[0009] The dynamic adaptation module monitors the dependency analysis results, captures market data changes in real time, adjusts weights and connections in the dependency graph, and builds a real-time update model through multiple simulation tests;

[0010] The marketing strategy module designs marketing strategies based on the real-time update model, simulates performance under differentiated market scenarios, quantifies potential risks and benefits, and generates risk assessment results;

[0011] The anomaly detection module identifies abnormal patterns in the financial marketing platform data through the risk assessment results, compares regular transaction behaviors and abnormal indicators, uses threshold analysis to calibrate abnormal behaviors, and generates abnormal behavior classification results;

[0012] The strategy implementation module uses the abnormal behavior classification results to adjust the marketing strategy, evaluate the effects of controlling risks and optimizing benefits, optimize resource allocation, and generate marketing effect evaluation results.

[0013] As a further solution of the present invention, the market data set includes screened market event timestamps, user transaction frequencies, and customer purchasing patterns; the dependency analysis results include purchase trend charts of financial product categories, user preference statistics, and market reaction speeds; the real-time update model includes adjusted market sensitivity parameters, transaction volume response indicators, and time series prediction models; the risk assessment results include expected rates of return, investment risk levels, and capital liquidity analysis under differentiated market scenarios; the abnormal behavior classification results include abnormal transaction frequency identification, risk account marking, and abnormal purchasing pattern identification; and the marketing effectiveness evaluation results include return on investment calculations, customer satisfaction survey results, and market share change analysis.

[0014] As a further solution of the present invention, the data acquisition module includes:

[0015] The data labeling submodule collects market events and customer behavior data from the financial marketing platform, classifies and identifies each data item, assigns a timestamp based on the time when the event occurred, labels information including the data source and time, and generates time-stamped data;

[0016] The data calibration submodule verifies the correspondence between the data timestamp and the financial time series based on the time-stamped data, calibrates the data records to a unified time axis, checks the time consistency of the data points, optimizes the continuity of the data stream, and obtains synchronized time data;

[0017] The data processing submodule uses the synchronized time data to detect abnormal values ​​and redundant items in the data, uses data filtering technology to remove invalid data, verifies the integrity of the data, deletes and corrects erroneous data, and generates a market data set.

[0018] As a further solution of the present invention, the data analysis module includes:

[0019] The cluster analysis submodule performs hierarchical data clustering based on the market data set, classifies the data, and groups the customer data according to data characteristics and behavior patterns to obtain classified data;

[0020] The volatility analysis submodule calculates the basic statistical characteristics of each category, including the mean and variance, through the classified data, verifies the data distribution and volatility of the differentiated categories through numerical analysis, and obtains statistical characteristic results;

[0021] The dependency graph submodule uses the statistical characteristic results to draw a dependency graph between multiple categories, analyze the mutual influence and dependency between multiple categories, identify the connection and flow of data, and generate dependency analysis results.

[0022] As a further solution of the present invention, the dynamic adaptation module includes:

[0023] Based on the dependency analysis results, the market monitoring submodule monitors market data and changes in real time, captures fluctuations and trend changes in key data, continuously tracks changes in dependency relationships, and obtains dynamic monitoring data;

[0024] The data adjustment submodule uses the monitoring dynamic data to adjust the connections and weights in the dependency graph, and obtains an adjusted dependency graph according to the real-time changes of the market data;

[0025] The model building submodule adopts the adjusted dependency graph, verifies the adaptability of the dependency graph through simulation tests, iterates multiple times, optimizes the response speed, and obtains a real-time updated model.

[0026] As a further solution of the present invention, the marketing strategy module includes:

[0027] The strategy design submodule designs marketing plans based on the real-time update model according to differentiated customer groups, performs information matching, matches market demand and consumer behavior, and obtains customized marketing strategies;

[0028] The market simulation submodule adopts the customized marketing strategy, simulates the strategy performance under the differentiated market scenario, analyzes the marketing effect under the differentiated market change conditions, evaluates the adaptability and efficiency of the customized marketing strategy through simulation experiments, and obtains the simulation performance results;

[0029] The risk quantification submodule uses the simulation performance results and the Monte Carlo method to analyze potential market risks and expected returns, calculate risk values ​​and return indicators, and generate risk assessment results.

[0030] As a further solution of the present invention, the formula of the Monte Carlo method is as follows:

[0031] ;

[0032] Among them, R is the risk assessment value, N represents the total number of simulated scenarios, represents the expected rate of return of i in each simulation scenario, represents the average rate of return of the simulated scenario, w represents the risk weight coefficient, and n represents the sample size.

[0033] As a further solution of the present invention, the anomaly detection module includes:

[0034] The anomaly identification submodule analyzes the abnormal patterns in the financial marketing platform data based on the risk assessment results, uses machine learning technology to filter data points that do not conform to the normal pattern from the batch transaction data, analyzes potential abnormal behaviors, and obtains initial abnormal indicators;

[0035] The abnormal analysis submodule uses the initial abnormal indicators to compare standard transaction behaviors and abnormal data points, analyzes the differences between normal behaviors and abnormal behaviors, evaluates the impact and frequency of abnormal behaviors, and obtains abnormal behavior comparison results;

[0036] The threshold analysis submodule uses the abnormal behavior comparison results to set thresholds of differentiated levels to calibrate abnormal behaviors, record abnormal behaviors that exceed the normal business scope, identify and classify abnormal behaviors through threshold settings, and generate abnormal behavior classification results.

[0037] As a further solution of the present invention, the policy implementation module includes:

[0038] The consistency check submodule reviews and adjusts the marketing strategy based on the abnormal behavior classification results, formulates countermeasures for the detected abnormal behavior, avoids potential negative impacts, checks the consistency of the marketing strategy with market demand and risk status, and obtains the adjusted marketing strategy;

[0039] The risk management submodule implements the adjusted marketing strategy, conducts risk assessment and control, evaluates the impact of strategy adjustment on risk changes, analyzes the implementation effect of risk control measures, and obtains risk control records through risk management;

[0040] The resource optimization submodule utilizes the risk control records to optimize resource allocation and marketing investment, analyze the benefits and costs of marketing activities, optimize overall marketing efficiency and ROI through reasonable allocation of marketing resources, and generate marketing effect evaluation results.

[0041] A method for information matching management of financial marketing platform data, the method for information matching management of financial marketing platform data is executed based on the above-mentioned information matching management system for financial marketing platform data, and comprises the following steps:

[0042] S1: Collect market event information and customer behavior data from the financial marketing platform, assign time tags to each data item, synchronize financial time series data, filter out invalid and erroneous data, and generate a market data set;

[0043] S2: performing hierarchical cluster analysis on the market data set, calculating statistical characteristics for each data category, including mean and variance, and obtaining a dependency analysis graph;

[0044] S3: According to the dependency analysis graph, monitor the changes in market data, dynamically adjust the weights and connections in the graph, and repeatedly verify through simulation tests to generate a dynamic market adaptation model;

[0045] S4: Based on the dynamic market adaptation model, design marketing strategies, simulate strategy performance under differentiated market scenarios, conduct quantitative analysis of potential risks and benefits, and generate strategy risk assessment results;

[0046] S5: Using the abnormal patterns identified in the strategy risk assessment results, adjust the marketing strategy, compare regular and abnormal transaction behaviors, optimize resource allocation and control risks, and generate marketing strategy assessment results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by capturing market data changes in real time and dynamically adjusting dependency graphs, financial marketing activities can more accurately target customer groups and design personalized marketing strategies that meet the actual needs of the market. By comprehensively analyzing market data sets, cluster analysis helps to more deeply understand the behaviors and needs of different customer groups. Such statistical analysis can provide a scientific basis for the formulation of marketing strategies. The introduction of dependency analysis enables financial institutions to identify and utilize market trends and patterns on a data-driven basis, predict market changes and respond quickly. The improvement of risk assessment results and anomaly detection capabilities not only optimizes resource allocation, but also enhances control over potential risks, improves the overall efficiency and effectiveness of marketing activities, enhances the accuracy of data-driven decision-making, improves customer service experience and brand loyalty, and drives sales growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a system flow chart of the present invention;

[0050] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0051] Figure 3 It is a flow chart of the data acquisition module of the present invention;

[0052] Figure 4 It is a flow chart of the data analysis module of the present invention;

[0053] Figure 5 It is a flow chart of the dynamic adaptation module of the present invention;

[0054] Figure 6 It is a flow chart of the marketing strategy module of the present invention;

[0055] Figure 7 is a flow chart of the abnormality detection module of the present invention;

[0056] Figure 8 It is a flow chart of the strategy implementation module of the present invention;

[0057] Fig. 9 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0060] See also Figure 1 to Figure 2 The present invention provides a technical solution, an information matching management system for financial marketing platform data, comprising:

[0061] The data acquisition module collects market event information and customer behavior data from the financial marketing platform, assigns time tags, synchronizes financial time series data, removes invalid and erroneous data, and generates a market data set;

[0062] The data analysis module conducts hierarchical cluster analysis on the financial marketing platform data based on the market data set, calculates statistical features for each category, including mean and variance, compares multi-category features, draws dependency graphs, and obtains dependency analysis results;

[0063] The dynamic adaptation module monitors the results of dependency analysis, captures market data changes in real time, adjusts the weights and connections in the dependency graph, and builds a real-time update model through multiple simulation tests;

[0064] The marketing strategy module designs and tests marketing strategies based on real-time updated models, simulates performance under differentiated market scenarios, and quantifies the potential risks and benefits of each scenario to generate risk assessment results;

[0065] The anomaly detection module uses risk assessment results to identify abnormal patterns in the financial marketing platform data, compares regular transaction behaviors and abnormal indicators, uses threshold analysis to calibrate abnormal behaviors, and generates abnormal behavior classification results;

[0066] The strategy implementation module uses the abnormal behavior classification results to adjust the marketing strategy, evaluate the effectiveness of the new strategy in controlling risks and optimizing benefits, optimize the timing and resource allocation of the new strategy execution, and generate marketing effectiveness evaluation results.

[0067] The market data set includes the filtered market event timestamps, user transaction frequencies, and customer purchasing patterns. The dependency analysis results include purchase trend charts of financial product categories, user preference statistics, and market reaction speeds. The real-time update model includes adjusted market sensitivity parameters, transaction volume response indicators, and time series prediction models. The risk assessment results include expected returns, investment risk levels, and capital liquidity analysis under differentiated market scenarios. The abnormal behavior classification results include abnormal transaction frequency identification, risk account marking, and abnormal purchasing pattern identification. The marketing effectiveness evaluation results include return on investment calculations, customer satisfaction survey results, and market share change analysis.

[0068] See also Figure 2 , 3 , the data acquisition module includes:

[0069] The data labeling submodule collects market events and customer behavior data from the financial marketing platform, classifies and identifies each data item, assigns a timestamp based on the time when the event occurred, and labels the information including the data source and time. The execution process for generating time-stamped data is as follows;

[0070] The collection process of market event and customer behavior data is based on a financial marketing platform that monitors various types of transaction behaviors and user responses. These data are preliminarily processed through classification and recognition technology to identify important events and behaviors, such as purchases, inquiries or complaints, and timestamp them with the time when the event occurred to ensure that each data item has an accurate time label. The annotation information includes the data source and time. The process ensures the timeliness and traceability of the data, provides a reliable foundation for subsequent data processing and analysis, and generates time-stamped data.

[0071] The data calibration submodule verifies the correspondence between the data timestamp and the financial time series based on the time-stamped data, calibrates the data records to a unified time axis, checks the time consistency of the data points, optimizes the continuity of the data flow, and obtains the execution process of synchronized time data as follows;

[0072] Check the correspondence between the data timestamp and the financial time series, according to the formula , calculate the data time consistency. In the formula, represents the temporal consistency score, represents the timestamp of each data point, represents the timestamp of the corresponding financial time series, is a comparison function, when and Returns 1 if they are equal, otherwise returns 0. Detailed explanation of the formula and formula calculation derivation process: Consider a specific case, where there are three data points with timestamps of 10:00, 10:05 and 10:10, and the timestamps of the financial time series are 10:00, 10:05 and 10:15. According to the formula, , , , The results show that two of the three data points have timestamps that match the financial time series, indicating that the consistency of the data time is high.

[0073] The data processing submodule uses synchronized time data to detect outliers and redundant items in the data, applies data filtering technology to remove invalid data, verifies data integrity, and deletes and corrects erroneous data. The execution process of generating the market data set is as follows;

[0074] Using synchronized time data to detect outliers and redundant items is a complex task that involves data filtering technology, which uses specialized algorithms to identify and delete data points that deviate from normal patterns, such as outdated or erroneous information. Synchronized data allows the system to identify these anomalies more effectively because the consistency of time stamps helps distinguish the specific time period when the data was generated. Data integrity verification ensures that each data item is complete and intact. After the erroneous data is corrected or deleted, a market data set is generated.

[0075] See also Figure 2 , 4 , the data analysis modules include:

[0076] The cluster analysis submodule performs hierarchical clustering based on the market data set, classifies the data, and groups the customer data according to the data characteristics and behavior patterns. The execution process of the classified data is as follows;

[0077] Perform hierarchical clustering of data according to the formula , calculate the classification data. In the formula, represents the cluster center of the b-th class, represents the number of data points belonging to the bth class, Represents the eigenvalue of the jth data point in the bth class. Detailed explanation of the formula and the process of formula calculation: Assume that there are 3 data points in a certain class, and their eigenvalues ​​are 2, 4, and 6 respectively. According to the formula, calculate the cluster center of this class: This calculation shows that the central eigenvalue for this category is 4, which helps to determine the representative characteristics and behavior patterns of the category data.

[0078] The volatility analysis submodule calculates the basic statistical characteristics of each category, including the mean and variance, by classifying the data, and verifies the data distribution and volatility of the differentiated categories through numerical analysis. The execution process of obtaining the statistical characteristic results is as follows;

[0079] The calculation of basic statistical characteristics using classified data involves the calculation of mean and variance. These statistical measures are used to describe the central tendency of each category of data and the breadth of data distribution. By calculating the mean, we can understand the average behavior or characteristics of the data category, while the calculation of the variance reveals the fluctuation of the data around the mean. This numerical analysis is an important means to verify the differences between categories. It not only helps to understand the consistency and diversity within each category, but also provides a scientific basis for further data processing and analysis to obtain statistical characteristic results.

[0080] The dependency graph submodule uses statistical characteristic results to draw dependency graphs between multiple categories, analyze the mutual influence and dependency between multiple categories, identify the connection and flow of data, and generate the dependency analysis results. The execution process is as follows;

[0081] Use statistical characteristic results to draw dependency maps between multiple categories. In this process, multi-dimensional analysis of data helps depict the mutual influence and dependency between data categories. Through the map, you can intuitively see how different categories of data interact with each other and identify the main connections and flows of data. This information is very valuable for understanding market dynamics and customer behavior patterns, providing important data perspectives and insights for decision-making, better adjusting strategies and optimizing services, and generating dependency analysis results.

[0082] See also Figure 2 , 5 , the dynamic adaptation module includes:

[0083] Based on the dependency analysis results, the market monitoring submodule monitors market data and changes in real time, captures fluctuations and trend changes in key data, and continuously tracks changes in dependencies. The execution process of obtaining dynamic monitoring data is as follows;

[0084] Utilize the dependency analysis results to monitor market data in real time, which includes capturing key fluctuations and trend changes in the data. The monitored data covers a wide range of dynamic changes in the market, such as price adjustments, demand changes, and supply fluctuations. The dependency changes of the data are continuously tracked. By analyzing the time series of the data, the evolution of the dependency can be accurately depicted. This process not only helps to understand the current state of the market, but also predicts future market trends, provides data support for strategy adjustments, and obtains dynamic monitoring data.

[0085] The data adjustment submodule uses the monitored dynamic data to adjust the connections and weights in the dependency graph. According to the real-time changes in market data, the execution process of the adjusted dependency graph is as follows;

[0086] The connections and weights in the dependency graph are adjusted by monitoring dynamic data. The data adjustment strategy is based on the latest changes in market data. For example, when market demand surges, the corresponding supply chain data connections will be strengthened, reflecting the supply chain's rapid response to market changes. During the adjustment process, the change in weight reflects the changing intensity and direction of the relationship between different data points. This dynamic adjustment helps the dependency graph more accurately reflect the current market structure and relationship network, and obtains an adjusted dependency graph.

[0087] The model building submodule adopts the adjusted dependency graph, verifies the adaptability of the dependency graph through simulation tests, iterates multiple times, optimizes the response speed, and obtains the execution process of the real-time update model as follows;

[0088] Verify the adaptability of the dependency graph through simulation testing, according to the formula , the adaptability of the calculation model. In the formula, represents the fitness score of the d-th model, Represents model d and model The dependency weight between Represents the response speed ratio of the two models. Detailed explanation of the formula and formula calculation derivation process: Consider a specific case, where the dependency weights of three models are set to be 0.5, 0.3 and 0.2, and the response speed ratios are 1.2, 0.8 and 1.0. According to the formula This calculation shows that model d has a fitness score of 0.84, reflecting its adaptability and response efficiency in the current dependency graph.

[0089] See also Figure 2 , 6 , the marketing strategy module includes:

[0090] The strategy design submodule designs marketing plans based on the real-time update model according to differentiated customer groups, performs information matching, matches market demand and consumer behavior, and obtains the execution process of customized marketing strategies as follows;

[0091] Design marketing plans based on differentiated customer groups and follow the formula , calculate customized marketing strategy. In the formula, represents the score of the g-th strategy, represents the priority of the kth market demand, represents the value of satisfying the demand, and m is the total number of market demand types. Formula explanation and formula calculation derivation process: Consider a specific case, where there are three market demands with priorities of 0.4, 0.3, and 0.3, and the values ​​of satisfying these demands are 10, 20, and 15, respectively. According to the formula, This calculation shows that a marketing strategy designed to target a specific customer group scores 14.5, showing the overall effectiveness of this strategy in meeting market needs.

[0092] The market simulation submodule adopts customized marketing strategies, simulates the strategy performance under differentiated market scenarios, analyzes the marketing effects under differentiated market change conditions, and evaluates the adaptability and efficiency of customized marketing strategies through simulation experiments. The execution process for obtaining simulation performance results is as follows;

[0093] Customized marketing strategies are used to simulate strategic performance in differentiated market scenarios. The process includes prediction and analysis of different market dynamics and consumer behaviors. By simulating different market change conditions, such as economic fluctuations, changes in consumption trends, or adjustments in competitive strategies, we analyze how these factors affect the effectiveness and efficiency of marketing strategies. Simulation experiments not only evaluate the performance of marketing strategies under ideal conditions, but also examine their adaptability in unstable or rapidly changing market environments. They can provide data support and decision-making basis for strategy adjustments in actual applications, optimize strategy design and implementation effects, and obtain simulation performance results.

[0094] The risk quantification submodule uses the simulation performance results and the Monte Carlo method to analyze the potential market risks and expected returns, calculate the risk value and return indicators, and generate the risk assessment results. The execution process is as follows;

[0095] The formula of the Monte Carlo method is as follows:

[0096] ;

[0097] Among them, R is the risk assessment value, N represents the total number of simulated scenarios, represents the expected rate of return of i in each simulation scenario, represents the average rate of return of the simulated scenario, w represents the risk weight coefficient, and n represents the sample size.

[0098] Parameter settings and sources:

[0099] The total number of scenarios simulated, N, is set to 1000, representing the number of simulations used in risk assessment. This number is derived through data analysis and market volatility patterns to ensure sufficient sample size to reduce the impact of randomness.

[0100] Expected rate of return : Collect the daily returns of the past year from the market data, and perform inductive calculations to obtain the values ​​of each simulation scenario. Set the average daily return of a stock in the past year to 0.05%, and this value will be used as the value of a scenario. .

[0101] Average yield :Through all simulation scenarios Calculated. Settings From -0.1% to 0.2%, It is 0.05%.

[0102] Risk weight coefficient w: set to 0.5. This coefficient is determined based on industry standards and the company's risk management strategy, reflecting the company's sensitivity to different risk levels.

[0103] Example of setting specific numbers for calculation: is [0.04%, 0.05%, 0.05%, 0.06%, 0.03%, 0.04%, 0.07%, 0.05%, 0.05%, 0.04%], then =(0.04%+0.05%+0.05%+0.06%+0.03%+0.04%+0.07%+0.05%+0.05%+0.04%) / 10=0.048%, calculate the square of the deviation of each scenario from the average, and then find the average:

[0104] ;

[0105] Risk Assessment Indicators ;

[0106] The results show that under the given market scenario and risk sensitivity, the average risk assessment value is 0.0006%, which represents the average risk level under the current model and parameter settings, and is used to guide further investment decisions or risk management strategies.

[0107] See also Figure 2 , 7 , the anomaly detection module includes:

[0108] Based on the risk assessment results, the anomaly identification submodule analyzes the abnormal patterns in the financial marketing platform data, uses machine learning technology to filter data points that do not conform to the normal pattern from the batch transaction data, analyzes potential abnormal behaviors, and obtains the execution process of initializing abnormal indicators as follows;

[0109] The data of the financial marketing platform is analyzed using machine learning technology. In the process guided by risk assessment results, transaction data points that do not conform to the normal pattern are screened out. The machine learning model is trained to identify and analyze potential abnormal behaviors in these data points. These behaviors include frequent large transactions or transaction patterns that are significantly different from historical data. This method can effectively detect abnormal patterns that indicate fraud, money laundering or illegal activities, protect the platform and users from potential financial losses, and obtain initialized abnormal indicators.

[0110] The abnormal analysis submodule uses the initial abnormal indicators to compare the standard transaction behavior and abnormal data points. By analyzing the differences between normal behavior and abnormal behavior, the impact and frequency of abnormal behavior are evaluated. The execution process of obtaining the abnormal behavior comparison results is as follows;

[0111] Compare standard trading behavior and abnormal data points, according to the formula , calculate the impact of abnormal behavior. In the formula, E represents the impact index of abnormal behavior, represents the number of abnormal data points, represents the value of the vth abnormal data point, Represents the average value of standard trading behavior. Detailed explanation of the formula and formula calculation process: Consider a specific case, where there are five abnormal trading data points with values ​​of 100, 120, 80, 150 and 90 respectively, and the average value of standard trading behavior is 100. According to the formula, calculate the impact index of abnormal behavior:

[0112] ;

[0113] This calculation shows that the average deviation of deviant behavior is 20 units from standard behavior, providing a quantitative description of the impact of deviant behavior.

[0114] The threshold analysis submodule uses the abnormal behavior comparison results to set the thresholds of differentiated levels to calibrate abnormal behaviors, record abnormal behaviors that exceed the normal business scope, identify and classify abnormal behaviors through threshold settings, and generate abnormal behavior classification results. The execution process is as follows;

[0115] The abnormal behavior comparison results are used to set thresholds for differentiation levels. These thresholds are carefully designed according to the type and frequency of abnormal behaviors and are used to calibrate and classify abnormal behaviors. Through the set thresholds, abnormal behaviors that exceed the normal business scope can be identified, such as abnormally high transaction frequency or abnormally large transaction amounts. These calibrated abnormal behaviors are recorded and classified for further analysis and processing. The setting of thresholds not only helps to reduce the false alarm rate, but also improves the accuracy of anomaly detection and generates abnormal behavior classification results.

[0116] See also Figure 2 ,8 , the policy implementation module includes:

[0117] The consistency check submodule reviews and adjusts the marketing strategy based on the abnormal behavior classification results, formulates countermeasures for the detected abnormal behavior, avoids potential negative impacts, and checks the consistency of the marketing strategy with market demand and risk conditions. The execution process of the adjusted marketing strategy is as follows;

[0118] Check the consistency of marketing strategy with market demand and risk conditions, and follow the formula , calculate the consistency of strategy adjustment. In the formula, represents the policy consistency indicator, represents the number of strategy points considered, Representing market demand The expected policy response of the point, Represents the response of the actual risk situation at point z. Detailed explanation of the formula and formula calculation derivation process: The expected responses for the four market strategy points are set to 100, 150, 120 and 130 respectively, and the actual risk responses are 95, 160, 110 and 140 respectively. According to the formula, calculate the strategy consistency:

[0119]

[0120] This calculation indicates that the strategy's alignment with market demand and risk profile is scored at 81.25, indicating that further adjustments are needed to improve alignment.

[0121] The risk management submodule implements the adjusted marketing strategy, conducts risk assessment and control, evaluates the impact of strategy adjustments on risk changes, analyzes the implementation effect of risk control measures, and obtains risk control records. The execution process is as follows;

[0122] Risk assessment and control are carried out through the implementation of adjusted marketing strategies. The process includes evaluating the impact of strategy adjustments on risk changes, determining the actual implementation effect of the strategy and potential risk points through detailed risk analysis, and confirming the implementation effect of risk control measures through regular evaluation and monitoring. These control measures include market monitoring, customer behavior analysis and investment risk restrictions, which not only help companies better understand the effectiveness of risk management, but also provide dynamic records of strategy adjustments and risk management, providing an empirical basis for future strategy formulation and obtaining risk control records.

[0123] The resource optimization submodule uses risk control records to optimize resource allocation and marketing investment, analyze the benefits and costs of marketing activities, and optimize overall marketing efficiency and ROI by rationally allocating marketing resources. The execution process of generating marketing effect evaluation results is as follows;

[0124] Utilize risk control records to optimize resource allocation and marketing investment. Through comprehensive analysis of the benefits and costs of marketing activities, aim to improve overall marketing efficiency and return on investment (ROI) by rationally allocating marketing resources. The optimization process involves evaluating the cost-effectiveness of different marketing channels and strategies to ensure that every investment can produce the greatest effect. The results of optimization are demonstrated through marketing effectiveness evaluation, including the benefits of marketing activities, cost savings and market share growth, generating marketing effectiveness evaluation results.

[0125] See also Fig. 9 , a method for information matching management of financial marketing platform data, the method for information matching management of financial marketing platform data is executed based on the above-mentioned information matching management system for financial marketing platform data, and includes the following steps:

[0126] S1: Collect market event information and customer behavior data from the financial marketing platform, assign time tags to each data item, synchronize financial time series data, filter out invalid and erroneous data, and generate a market data set;

[0127] S2: Perform hierarchical cluster analysis on the market data set, calculate statistical characteristics for each data category, including mean and variance, and obtain dependency analysis graphs;

[0128] S3: Based on the dependency analysis graph, monitor market data changes, dynamically adjust the weights and connections in the graph, and repeatedly verify through simulation tests to generate a dynamic market adaptation model;

[0129] S4: Design marketing strategies based on dynamic market adaptation models, simulate strategy performance under differentiated market scenarios, conduct quantitative analysis of potential risks and benefits, and generate strategy risk assessment results;

[0130] S5: Use the abnormal patterns identified in the strategy risk assessment results to adjust the marketing strategy, compare regular and abnormal transaction behaviors, optimize resource allocation and control risks, and generate marketing strategy evaluation results.

[0131] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An information matching management system for financial marketing platform data, characterized in that: The system comprises: The data acquisition module collects market event information and customer behavior data from the financial marketing platform, assigns time tags, synchronizes financial time series data, removes invalid and erroneous data, and generates a market data set; The data analysis module performs hierarchical cluster analysis on the financial marketing platform data based on the market data set, calculates statistical features for each category, including mean and variance, draws a dependency graph, and obtains dependency analysis results; The dynamic adaptation module monitors the dependency analysis results, captures market data changes in real time, adjusts weights and connections in the dependency graph, and builds a real-time update model through multiple simulation tests; The marketing strategy module designs marketing strategies based on the real-time update model, simulates performance under differentiated market scenarios, quantifies potential risks and benefits, and generates risk assessment results; The anomaly detection module identifies abnormal patterns in the financial marketing platform data through the risk assessment results, compares regular transaction behaviors and abnormal indicators, uses threshold analysis to calibrate abnormal behaviors, and generates abnormal behavior classification results; The strategy implementation module uses the abnormal behavior classification results to adjust the marketing strategy, evaluate the effects of controlling risks and optimizing benefits, optimize resource allocation, and generate marketing effect evaluation results; The data analysis module includes: The cluster analysis submodule performs hierarchical data clustering based on the market data set, classifies the data, groups the customer data according to data characteristics and behavior patterns, and obtains classified data; The volatility analysis submodule calculates the basic statistical characteristics of each category, including the mean and variance, through the classified data, verifies the data distribution and volatility of the differentiated categories through numerical analysis, and obtains statistical characteristic results; The dependency graph submodule uses the statistical characteristic results to draw a dependency graph between multiple categories, analyze the mutual influence and dependency between multiple categories, identify the connection and flow of data, and generate dependency analysis results.

2. The information matching management system for financial marketing platform data according to claim 1, characterized in that: The market data set includes the screened market event timestamps, user transaction frequencies, and customer purchasing patterns; the dependency analysis results include purchase trend charts of financial product categories, user preference statistics, and market reaction speeds; the real-time update model includes adjusted market sensitivity parameters, transaction volume response indicators, and time series prediction models; the risk assessment results include expected rates of return, investment risk levels, and capital liquidity analysis under differentiated market scenarios; the abnormal behavior classification results include abnormal transaction frequency identification, risk account marking, and abnormal purchasing pattern identification; the marketing effectiveness evaluation results include return on investment calculations, customer satisfaction survey results, and market share change analysis.

3. The information matching management system for financial marketing platform data according to claim 1, characterized in that: The data acquisition module comprises: The data labeling submodule collects market events and customer behavior data from the financial marketing platform, classifies and identifies each data item, assigns a timestamp based on the time when the event occurred, labels information including the data source and time, and generates time-stamped data; The data calibration submodule verifies the correspondence between the data timestamp and the financial time series based on the time-stamped data, calibrates the data records to a unified time axis, checks the time consistency of the data points, optimizes the continuity of the data stream, and obtains synchronized time data; The data processing submodule uses the synchronized time data to detect abnormal values ​​and redundant items in the data, uses data filtering technology to remove invalid data, verifies the integrity of the data, deletes and corrects erroneous data, and generates a market data set.

4. The information matching management system for financial marketing platform data according to claim 1, characterized in that: The dynamic adaptation module comprises: Based on the dependency analysis results, the market monitoring submodule monitors market data and changes in real time, captures fluctuations and trend changes in key data, continuously tracks changes in dependency relationships, and obtains dynamic monitoring data; The data adjustment submodule uses the monitoring dynamic data to adjust the connections and weights in the dependency graph, and obtains an adjusted dependency graph according to the real-time changes of the market data; The model building submodule adopts the adjusted dependency graph, verifies the adaptability of the dependency graph through simulation tests, performs multiple iterations, optimizes the response speed, and obtains a real-time updated model.

5. The information matching management system for financial marketing platform data according to claim 1, characterized in that: The marketing strategy module includes: The strategy design submodule designs marketing plans based on the real-time update model according to differentiated customer groups, performs information matching, matches market demand and consumer behavior, and obtains customized marketing strategies; The market simulation submodule adopts the customized marketing strategy, simulates the strategy performance under the differentiated market scenario, analyzes the marketing effect under the differentiated market change conditions, evaluates the adaptability and efficiency of the customized marketing strategy through simulation experiments, and obtains the simulation performance results; The risk quantification submodule uses the simulation performance results and the Monte Carlo method to analyze potential market risks and expected returns, calculate risk values ​​and return indicators, and generate risk assessment results.

6. The information matching management system for financial marketing platform data according to claim 5, characterized in that: The formula of the Monte Carlo method is as follows: ； Among them, R is the risk assessment value, N represents the total number of simulated scenarios, represents the expected rate of return of i in each simulation scenario, represents the average rate of return of the simulated scenario, w represents the risk weight coefficient, and n represents the sample size.

7. The information matching management system for financial marketing platform data according to claim 1, characterized in that: The anomaly detection module comprises: The anomaly identification submodule analyzes the abnormal patterns in the financial marketing platform data based on the risk assessment results, uses machine learning technology to filter data points that do not conform to the normal pattern from the batch transaction data, analyzes potential abnormal behaviors, and obtains initial abnormal indicators; The abnormal analysis submodule uses the initial abnormal indicators to compare standard transaction behaviors and abnormal data points, analyzes the differences between normal behaviors and abnormal behaviors, evaluates the impact and frequency of abnormal behaviors, and obtains abnormal behavior comparison results; The threshold analysis submodule uses the abnormal behavior comparison results to set thresholds of differentiated levels to calibrate abnormal behaviors, record abnormal behaviors that exceed the normal business scope, identify and classify abnormal behaviors through threshold settings, and generate abnormal behavior classification results.

8. The information matching management system for financial marketing platform data according to claim 1, characterized in that: The policy implementation module includes: The consistency check submodule reviews and adjusts the marketing strategy based on the abnormal behavior classification results, formulates countermeasures for the detected abnormal behavior, avoids potential negative impacts, checks the consistency of the marketing strategy with market demand and risk status, and obtains the adjusted marketing strategy; The risk management submodule implements the adjusted marketing strategy, conducts risk assessment and control, evaluates the impact of strategy adjustment on risk changes, analyzes the implementation effect of risk control measures, and obtains risk control records through risk management; The resource optimization submodule utilizes the risk control records to optimize resource allocation and marketing investment, analyze the benefits and costs of marketing activities, optimize overall marketing efficiency and ROI through reasonable allocation of marketing resources, and generate marketing effect evaluation results.

9. A method for information matching management of financial marketing platform data, characterized in that: The information matching management system for financial marketing platform data according to any one of claims 1 to 8 comprises the following steps: Collect market event information and customer behavior data from the financial marketing platform, assign time tags to each piece of data, synchronize financial time series data, filter out invalid and erroneous data, and generate market data sets; Performing hierarchical cluster analysis on the market data set, calculating statistical characteristics for each data category, including mean and variance, and obtaining a dependency analysis graph; According to the dependency analysis graph, market data changes are monitored, weights and connections in the graph are dynamically adjusted, and repeated verification is performed through simulation tests to generate a dynamic market adaptation model; Based on the dynamic market adaptation model, design marketing strategies, simulate strategy performance under differentiated market scenarios, conduct quantitative analysis of potential risks and benefits, and generate strategy risk assessment results; The abnormal patterns identified in the strategy risk assessment results are used to adjust the marketing strategy, compare regular and abnormal transaction behaviors, optimize resource allocation and control risks, and generate marketing strategy assessment results.

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