Decision-making method, device, equipment and medium based on big data

Abstract

The present invention relates to the fields of big data technology and financial technology, and discloses a decision-making method based on big data. By collecting multidimensional data related to the target business, extracting key features such as customer behavior patterns, potential problems, and resource stability, a customer analysis model is constructed, and a time series data set is generated in combination with behavior trend data; a behavior data set is generated through scenario analysis and quantitative analysis is performed to derive customer behavior patterns and resource utilization trends, and ultimately generate strategies adapted to various business scenarios. The present invention collects multidimensional data, extracts customer features, constructs an analysis model, and analyzes customer behavior trends to generate a time series data set. Scenario analysis and quantitative processing are performed in combination with different business scenarios, and ultimately business strategies are generated. This realizes real-time analysis of multidimensional data, scenario-based decision-making, and dynamic adjustment, thereby improving the flexibility and accuracy of the system in complex business scenarios and the efficiency and accuracy of business decisions.

Description

Technical Field

[0001] The present invention relates to the fields of big data technology and financial technology, and in particular to a decision-making method, device, equipment and storage medium based on big data. Background Art

[0002] In the insurance industry, premium assessment is a crucial step in the underwriting process. Reasonable premium pricing not only helps insurance companies reduce losses and optimize business operations, but also provides customers with quotes that better suit their needs, thereby attracting more customers and improving market competitiveness. In current insurance business practices, there are two common premium pricing methods: manual quoting and automated quoting.

[0003] In the traditional manual quotation model, underwriters determine premiums based on their own experience and collected customer information. Although this approach relies on underwriters' ability to judge and analyze customer information, it has the following significant shortcomings:

[0004] Reliance on personal experience: Manual quotations rely on the subjective judgment and experience of underwriters, making it difficult to guarantee the accuracy and consistency of pricing results.

[0005] Inefficiency: Underwriters need to review customer information one by one and process it manually, making it difficult to respond quickly to a large number of customer needs.

[0006] Limited data analysis: Manual methods have limited data processing capabilities and cannot fully utilize large amounts of historical data and market dynamics to optimize premium pricing.

[0007] With the rapid evolution of financial markets and the increasing diversity and complexity of customer needs, traditional premium pricing methods are no longer able to meet the demands of modern insurance. Existing technologies lack sufficient flexibility and accuracy to capture and analyze multi-dimensional customer behavior data and market changes in real time. Consequently, existing premium assessment methods are no longer effectively adapting to the current complex financial environment and customer needs. A pricing method that can dynamically adjust and flexibly respond to changing risks is urgently needed. Summary of the Invention

[0008] The main purpose of the present invention is to provide a decision-making method, device, equipment and storage medium based on big data, aiming to solve the technical problem that the existing technology lacks the ability to analyze multidimensional data and customer behavior in real time, resulting in insufficient flexibility and accuracy of the system.

[0009] To achieve the above objectives, the present invention provides a decision-making method based on big data, comprising:

[0010] Collect multi-dimensional data related to the target business, including customer personal information, historical behavior data, resource status and / or credit history;

[0011] extracting customer characteristics that influence business decisions from the multidimensional data, the customer characteristics including customer behavior patterns, potential problem indicators, and / or resource stability;

[0012] Building a customer analysis model based on the customer characteristics;

[0013] Obtaining key features of target customers, and analyzing behavioral trend data of the target customers based on the key features using the customer analysis model;

[0014] Segmenting the target customer's behavioral trend data according to time periods to generate a time series data set, wherein the time series data set includes behavioral trend data within multiple time periods;

[0015] Combine multiple business scenarios to perform scenario analysis on the time series dataset and generate a corresponding behavior dataset for each business scenario;

[0016] Conduct quantitative analysis on the behavioral data sets corresponding to each business scenario, and generate behavioral analysis results for target customers in each business scenario within each time period;

[0017] Based on the behavioral analysis results of target customers in each business scenario in each time period, combined with the target customers' historical behavioral data, target business strategies are generated for target customers.

[0018] Furthermore, to achieve the above-mentioned purpose, the present invention provides a decision-making device based on big data, comprising:

[0019] A data collection module, configured to collect multi-dimensional data related to the target business, including customer personal information, historical behavior data, resource status, and / or credit history;

[0020] A feature extraction module is used to extract customer features that affect business decisions from the multidimensional data, wherein the customer features include customer behavior patterns, potential problem indicators and / or resource stability;

[0021] A customer analysis model building module, used to build a customer analysis model based on the customer characteristics;

[0022] A customer behavior analysis module is used to obtain key features of target customers and analyze the behavior trend data of the target customers based on the key features using the customer analysis model;

[0023] A time series processing module is used to segment the target customer's behavior trend data according to time periods to generate a time series data set, which contains behavior trend data in multiple time periods;

[0024] A scenario analysis module is used to combine multiple business scenarios to perform scenario analysis on the time series dataset and generate a corresponding behavior dataset for each business scenario;

[0025] The quantitative analysis module is used to perform quantitative analysis on the corresponding behavioral data set in each business scenario and generate the behavioral analysis results of the target customers in each business scenario within each time period;

[0026] The business strategy generation module is used to generate target business strategies for target customers based on the behavior analysis results of target customers in each business scenario in each time period and combined with the historical behavior data of target customers.

[0027] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer device, which includes a memory, a processor, and a big data-based decision-making program stored in the memory and runnable on the processor. When the big data-based decision-making program is executed by the processor, the steps of the big data-based decision-making method described above are implemented.

[0028] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a decision-making generation program based on big data is stored. When the decision-making generation program based on big data is executed by a processor, the steps of the decision-making generation method based on big data as described above are implemented.

[0029] Beneficial effects: The present invention relates to the fields of big data technology and financial technology, and discloses a decision-making method based on big data, which collects multidimensional data related to the target business, extracts key features such as customer behavior patterns, potential problems, and resource stability, builds a customer analysis model, and generates a time series data set in combination with behavior trend data; generates a behavior data set through scenario analysis and performs quantitative analysis to derive customer behavior patterns and resource utilization trends, and finally generates a strategy adapted to each business scenario. The present invention collects multidimensional data, extracts customer features, constructs an analysis model, and analyzes customer behavior trends to generate a time series data set, performs scenario analysis and quantitative processing in combination with different business scenarios, and finally generates a business strategy, thereby achieving real-time analysis of multidimensional data, scenario-based decision-making, and dynamic adjustment, improving the flexibility and accuracy of the system in complex business scenarios, effectively reducing human intervention, and improving the efficiency and accuracy of business decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0031] Figure 1 Schematic diagram of an application environment of a decision-making method based on big data in one embodiment of the present invention;

[0032] Figure 2 This is a flow chart of an embodiment of a decision-making method based on big data according to the present invention;

[0033] Figure 3 This is a functional module diagram of a preferred embodiment of a decision-making device based on big data according to the present invention;

[0034] Figure 4 A schematic diagram of the structure of a computer device according to an embodiment of the present invention;

[0035] Figure 5 FIG. 2 is another structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] The decision-making method based on big data provided by the embodiment of the present invention can be applied in Figure 1 In an application environment, the user end communicates with the server end through a network. The server end can collect multidimensional data related to the target business through the user end, extract key features such as customer behavior patterns, potential problems and resource stability, build a customer analysis model, and generate a time series data set in combination with behavior trend data; generate a behavior data set through scenario analysis and perform quantitative analysis to derive customer behavior patterns and resource utilization trends, and finally generate a strategy that adapts to each business scenario. The present invention collects multidimensional data, extracts customer features, builds an analysis model, analyzes customer behavior trends, generates a time series data set, performs scenario analysis and quantitative processing in combination with different business scenarios, and finally generates a business strategy, thereby realizing real-time analysis, scenario-based decision-making and dynamic adjustment of multidimensional data, improving the flexibility and accuracy of the system in complex business scenarios, effectively reducing human intervention, and improving the efficiency and accuracy of business decision-making. Among them, the user end can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The server end can be implemented with an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0038] See also Figure 2 , Figure 2This is a flow chart of an embodiment of a decision-making method based on big data provided by the present invention. It should be noted that although a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0039] like Figure 2 As shown, the decision-making method based on big data proposed in the present invention includes the following steps:

[0040] S10, collecting multidimensional data related to the target business, wherein the multidimensional data includes customer personal information, historical behavior data, resource status and / or credit history;

[0041] In this embodiment, customer personal information refers to basic customer identification information, including but not limited to name, ID number, contact information, and address. In the financial sector, customer personal information is one of the fundamental data points for risk assessment and can be used to analyze customer stability and creditworthiness. By connecting to the customer data storage system and calling APIs or database query methods, this personal information can be batch retrieved and updated from the customer database. During the data collection process, the system also requires identity verification to ensure the data source is legitimate and accurate.

[0042] Historical behavior data includes records of customers' past operations and behaviors, such as transaction records, login history, and frequency of use of certain services. This data can be used to analyze customer behavior patterns and predict future trends. Historical behavior data is typically stored in behavior logs. The system uses log analysis tools or big data analysis platforms to connect to customer behavior log data sources and regularly collect and cleanse this data. During the data cleaning process, the system should eliminate invalid data to ensure data validity.

[0043] Resource status refers to the resources currently available to a client, including funds, assets, and liquidity. Within financial data, resource status reflects the client's financial health and directly influences the risk assessment of business decisions. By connecting to third-party financial institutions or corporate financial management systems, the system can access data such as client cash flows and balance sheets. This data can be regularly updated and collected through APIs or financial data service platforms.

[0044] A credit record is a customer's credit history, including their credit score, overdue payment history, and repayment history. This data serves as a crucial basis for financial institutions in risk management and credit assessment. Credit records can be obtained through integration with third-party credit platforms (such as credit reporting agencies). Through regular credit data synchronization, the system can call a credit query interface to automatically update a customer's credit status. Data security and privacy must be ensured, and access to credit records must comply with relevant laws and regulations.

[0045] Example: When a financial institution approves a customer's loan, it uses this system to collect personal information (such as age and occupation), historical behavioral data (such as past loan repayments), resource status (such as current income and assets), and credit history (such as credit score and number of overdue payments). This system then comprehensively assesses the customer's repayment ability and risk from multiple dimensions, and provides intelligent decision-making recommendations based on the system's analysis results. This multi-dimensional data analysis-based technology can significantly reduce the financial institution's credit risk and improve the efficiency of credit approval.

[0046] By collecting multidimensional data, the system can comprehensively analyze customers from multiple perspectives, including personal information, historical behavior data, resource status, and credit history, enabling accurate predictions of customer behavior patterns and potential risks. This not only increases the automation of data collection and reduces manual work, but also improves the real-time and completeness of data, providing more reliable data support for subsequent business decisions, thereby enhancing the efficiency and accuracy of business decisions.

[0047] S20, extracting customer characteristics that affect business decisions from the multidimensional data, the customer characteristics including customer behavior patterns, potential problem indicators, and / or resource stability;

[0048] In this embodiment, behavioral patterns refer to the customer's habitual behaviors and frequencies in past operations. These patterns may include the customer's consumption behavior, transaction frequency, interaction methods, etc., which can help the system predict the customer's future behavioral trends, thereby affecting business decisions. The extraction of behavioral patterns helps the system identify the customer's regular operating behaviors and preferences. By analyzing historical behavioral data, the system uses machine learning models or data mining technology to model and classify customers' daily interaction records (such as transaction frequency, consumption amount, and visit time). The system can perform label classification based on different types of behavioral patterns to generate the "behavioral pattern" part of the customer portrait.

[0049] Potential problem indicators refer to characteristics that may indicate a customer presents potential risk. These include, but are not limited to, a customer's decreased ability to fulfill their obligations, abnormal behavior, and a history of default. Potential problem indicators can help the system promptly identify risky customers and make corresponding adjustments in business decisions, such as adjusting risk control strategies or restricting specific business operations. The system identifies possible risk characteristics by analyzing a customer's credit history, resource status, and historical behavioral data. For example, if a customer has recently frequently changed their contact information or their transaction activity has suddenly decreased, the system will label these behaviors as "potential risk." By comparing these abnormal behaviors with past normal behavior patterns, the system can automatically identify these abnormal behaviors and generate a list of "potential problem indicators."

[0050] Resource stability refers to the stability of a client's available resources (such as funds, assets, and liquidity). Resource stability directly impacts a client's ability to fulfill obligations and is therefore a key factor in business decision-making. Clients with high resource stability generally represent lower risk, while clients with unstable resources may carry a higher credit risk. By analyzing a client's resource status (such as bank account balances, assets and liabilities, and investment status), the system can regularly assess client resource fluctuations. The system can generate a "resource stability" score or classification label based on the client's cash flow trends, changes in total assets, and other factors. The scoring or labeling results can be used in further business decision-making.

[0051] Example: A customer has maintained a stable transaction frequency and cash flow over the past six months, and the system labels their behavior as "stable." However, in the past month, the customer's credit card delinquencies have suddenly increased, and the balance in their cash account has fluctuated significantly. By extracting these "potential problem indicators" and "resource stability" characteristics, the system recommends that the bank adjust the customer's credit limit and issues a risk warning. This multi-dimensional data analysis approach enables banks to make more timely and accurate business decisions, mitigating credit risk.

[0052] By extracting customer behavior patterns, potential problem indicators, and resource stability from multidimensional data, the system comprehensively analyzes key customer characteristics and accurately assesses customer risk and behavior. This increases the automation of data analysis, reduces manual intervention, and enhances the accuracy and flexibility of business decisions, ensuring that companies can promptly identify potential risks and adjust business strategies, thereby reducing potential losses.

[0053] S30, building a customer analysis model based on the customer characteristics;

[0054] In this embodiment, before building a customer analysis model, the extracted customer feature data must first be cleaned and preprocessed. Data cleaning mainly includes removing duplicate data, processing missing values ​​and outliers, etc., and data preprocessing includes operations such as data formatting, standardization, and normalization. This process ensures that the data input to the model is accurate, neat, and suitable for subsequent modeling. The system can use common data preprocessing tools or libraries (such as Pandas, Scikit-learn, etc.) to standardize the original customer feature data. For example, the customer's income, transaction amount, etc. are normalized so that data with different characteristics can be processed on the same scale, thereby improving the training effect of the model.

[0055] Customer analysis models can be constructed using a variety of machine learning and artificial intelligence models. The specific model type should be selected based on the complexity of customer characteristics and business needs. Common basic model types include decision trees, support vector machines (SVMs), neural networks, etc. Different models are suitable for different scenarios. For example, decision trees are suitable for simple decision-making problems, while neural networks are suitable for complex nonlinear problems. The system automatically selects suitable basic models based on the structure and number of different customer characteristics. For example, when the customer's historical behavior pattern data is more complex, a neural network model based on deep learning can be selected; for simple classification problems, a decision tree or random forest model can be used. Model selection can be optimized through technical means such as cross-validation and model evaluation.

[0056] To ensure model accuracy and generalization, customer profile data must be divided into training, validation, and test sets. The training set is used for model training, the validation set is used for parameter tuning and evaluation, and the test set is used for final performance evaluation. The system divides customer profile data into a specific proportion (e.g., 70% training set, 20% validation set, and 10% test set). This division can be randomized to ensure a diverse and representative dataset. The divided data is then used for model training and evaluation at different stages.

[0057] Model training involves inputting customer feature data into a selected base model. Through the model training process, the relationship between customer features and the target is learned. Model optimization involves adjusting model parameters (such as the learning rate and regularization parameter) to improve model accuracy and generalization. The system inputs the training set into the base model and trains the model using a machine learning algorithm. During training, the system iteratively updates the model parameters to minimize the loss function (such as mean squared error or cross entropy). After training is complete, the system uses a validation set to evaluate model performance and adjusts hyperparameters based on the validation results to obtain the optimal model.

[0058] After optimization, the model undergoes a final performance evaluation to ensure it performs well in real-world applications. Evaluation metrics include accuracy, precision, recall, and F1-score. After model evaluation is complete, the trained model can be deployed to the business system for real-time customer analysis. The system uses a test set to evaluate the model's performance, calculating various evaluation metrics and comparing them with the results from the validation set to ensure model generalization. Once the evaluation is complete, the model is deployed to the production environment for real-time analysis of customer data and decision-making.

[0059] Example: A bank wants to perform credit assessments on new loan applicants. The system collects features such as credit histories, historical repayment data, and income trends from multiple customers to build a customer analysis model. By training on this data, the model can identify customers with good repayment ability and those with potential risks. For new loan applicants, the system inputs their key features into the trained model. The model then uses historical data to determine the customer's future repayment ability, predict the likelihood of loan default, and generate a business strategy tailored to the bank's current risk profile.

[0060] By building a customer analysis model based on customer characteristics, the system accurately captures customer behavior patterns, potential issues, and resource stability. Leveraging machine learning or artificial intelligence technologies, it intelligently analyzes and predicts customer outcomes. This model can be dynamically adjusted and optimized, enhancing the system's flexibility and accuracy, helping enterprises make precise business decisions in complex business scenarios.

[0061] S40, obtaining key features of target customers, and analyzing behavioral trend data of the target customers based on the key features using the customer analysis model;

[0062] In this embodiment, the key features of the target customers refer to important customer features that can influence business decisions. These features usually include customer behavior patterns, potential problem indicators, resource stability, etc., and are representative data extracted from the multidimensional data of the target customers. The purpose of extracting these key features is to better capture the individual behavioral characteristics of customers, so as to make more accurate analysis and predictions. The system automatically extracts relevant data of target customers through the customer analysis model that has been constructed, and filters out features that have a significant impact on business decisions. This can be achieved through feature selection algorithms (such as recursive feature elimination or feature importance based on tree models). Key features can include historical transaction frequency, risk points in credit records, resource changes, etc.

[0063] Customer behavior trend data is based on the key characteristics of target customers and predicts their possible behavioral changes in the future. Behavioral trend analysis aims to predict customers' future behavior patterns based on their historical behavior and key characteristics, helping the business make predictive decisions. These behavioral trends may include changes in consumption patterns, fluctuations in resource utilization, and increased potential risks. By inputting the key characteristics of target customers into a trained customer analysis model, the system can make predictions based on these characteristics. The customer analysis model may be trained using a supervised learning algorithm (such as a support vector machine, neural network, or random forest). It will analyze the customer's behavioral trends in the future based on the target customer's historical data and key characteristics, and output trend analysis results. For example, the model can predict the frequency of a customer's consumption behavior or changes in resource utilization in the future.

[0064] Behavioral trend data is a prediction of target customers' behavior over a predetermined time period. This data demonstrates how customer behavior changes over time, helping companies adjust their business strategies based on future customer trends. Based on the output of the customer analysis model, the system generates behavioral trend data, covering multiple time periods. This data is recorded in a time series database and used for subsequent scenario analysis and business strategy development.

[0065] Example: An insurance company's system automatically captures key characteristics of auto insurance customers, such as their driving history, accident frequency, and vehicle maintenance status. Based on these characteristics, a customer analysis model predicts the customer's likely future driving behavior trends. For example, if a customer's history shows a high driving frequency and numerous minor accidents over the past year, the system predicts a high probability of an accident within the next year. Based on this behavioral trend, the insurance company can adjust premiums or recommend safe driving training courses to the customer, thereby reducing the risk of future claims.

[0066] By capturing key characteristics of target customers and analyzing their behavior trends using customer analytics models, the system can accurately predict future customer behavior patterns and resource changes. This predictive analysis enables companies to adjust their business strategies based on future customer trends, thereby better responding to market changes and customer needs, improving business flexibility and accuracy, and mitigating potential risks.

[0067] S50, segmenting the target customer's behavior trend data according to time periods to generate a time series data set, wherein the time series data set includes behavior trend data within multiple time periods;

[0068] In this embodiment, the segmentation of time periods refers to dividing the target customer's behavioral trend data according to set time periods, such as by day, week, month or year. The purpose of segmentation is to divide the customer's behavioral trend data into multiple time segments, so that a more detailed analysis of the changes in the customer's behavior at different time points can be performed. The setting of the time period can be flexibly configured according to business needs or the characteristics of the data. The system pre-sets the time period and divides the customer's behavioral data into different time periods according to the specified period based on the business needs of the target customer. The system can automatically extract the data in each time period and archive the data to ensure the independence and continuity of the data. During the data segmentation process, the system should take into account the integrity of the data to ensure that the data in each time period is not missing or omitted.

[0069] A time series dataset refers to a behavioral trend dataset arranged in chronological order. A time series dataset contains behavioral trend data over multiple time periods and can demonstrate changes in target customers' behavior over different time periods. The purpose of generating a time series dataset is to be able to analyze the changing trends of customer behavior over different time periods, thereby providing support for subsequent scenario analysis and decision-making. The system arranges the behavioral data for each time period in chronological order to generate a complete time series dataset. This dataset covers changes in customer behavior over multiple time periods, ensuring the consistency of data on the timeline. The system can mark the location of each data segment with a timestamp or period identifier and store the time series data in the corresponding database for subsequent analysis and processing.

[0070] Time series datasets may contain missing or anomalous data points. The system needs to process this data to ensure the integrity and accuracy of the time series data. Missing data is usually supplemented through interpolation or regression methods, while anomalous data can be corrected through filtering or smoothing. When generating a time series dataset, the system first checks the integrity of the data. For missing data points, the system can use interpolation methods (such as linear interpolation and spline interpolation) to supplement the data. For anomalous data points, the system can smooth the data based on context or historical data to ensure data continuity.

[0071] Example: An insurance company analyzes the driving behavior of its auto insurance customers. By collecting the customer's historical driving records, the system segments the data by month, generating a time series dataset. This dataset includes the customer's monthly driving mileage, number of traffic violations, and vehicle maintenance status. The system analyzes the time series data and discovers a significant increase in the customer's number of violations over the past few months, coupled with a decrease in vehicle maintenance frequency. Based on this trend analysis, the insurance company predicts an increased risk of future accidents for the customer and decides to adjust the customer's premium or recommend driver safety training.

[0072] By segmenting target customers' behavioral trend data by time period and generating time series datasets, the system can dynamically track changing trends in customer behavior. Time series datasets provide customer behavior over multiple time periods, enabling businesses to predict future behavior patterns based on historical customer trends and develop more precise business strategies. This improves the sophistication of data analysis and the scientific nature of decision-making, ensuring flexible business response and optimized management.

[0073] S60, performing scenario analysis on the time series dataset in combination with multiple business scenarios to generate a behavior dataset corresponding to each business scenario;

[0074] In this embodiment, a business scenario is an operation or behavior scenario under specific conditions or environments defined according to the target business needs. These scenarios can include routine operations, emergency handling, resource fluctuations, etc. By defining multiple business scenarios, the system can analyze the behavioral data of target customers for different scenarios, thereby more accurately predicting the customer's performance in different environments. The system first predefines multiple business scenarios based on business needs and actual application environments. Each business scenario is assigned a unique identifier to distinguish data analysis in different scenarios. For example, in the insurance field, the scenarios can be "customers use insurance products normally", "customers encounter sudden accident claims", "customer behavior when the market environment fluctuates drastically", etc.

[0075] Scenario analysis involves analyzing target customers' behavioral patterns across different business scenarios. Scenario analysis helps the system identify changes in customer behavior under different circumstances, enabling more targeted assessments of customer behavior. This analysis can reveal customer behavioral characteristics under specific conditions, helping companies make personalized business decisions. Based on behavioral trend data from time series datasets, the system analyzes each business scenario separately. For example, in the "accident claims" scenario, the system can analyze customer behavioral patterns such as accident frequency, claims needs, and processing time. The analysis results for each scenario are output separately and used to generate the behavioral dataset for that scenario.

[0076] Behavioral datasets are complete datasets generated through scenario analysis, reflecting customer behavior characteristics in specific business scenarios. Each behavioral dataset reflects the customer's behavioral patterns and characteristics in a specific scenario and can support subsequent business decisions. Based on the results of scenario analysis, the system generates behavioral datasets for multiple scenarios. Each dataset corresponds to a business scenario and covers information such as customer behavior patterns, resource utilization, and risk assessment in that scenario. For example, in the "market volatility" scenario, the system-generated behavioral dataset may include data on changes in customer purchasing behavior and risk tolerance.

[0077] Example: An insurance company analyzes the behavior of its auto insurance customers in a routine scenario. Using a time series dataset, the system analyzes the customer's daily driving habits, maintenance records, and insurance product usage. The system discovers that a customer's driving habits over the past three months exhibit high-risk characteristics, such as frequent speeding and an increase in accidents. Based on this behavioral dataset from these routine scenarios, the insurance company can recommend driving safety training to the customer and adjust the premium appropriately.

[0078] By combining multiple business scenarios with scenario-based analysis of time series datasets, the system can comprehensively assess customer behavior in different scenarios. The behavioral datasets generated for each scenario provide precise support for business decision-making in different contexts. This scenario-based analysis enables the system to not only address routine business needs but also provide more flexible business strategies in the face of emergencies and market fluctuations, improving the accuracy and responsiveness of customer service.

[0079] S70, performing quantitative analysis on the behavior data set corresponding to each business scenario to generate behavior analysis results of target customers in each business scenario within each time period;

[0080] In this embodiment, quantitative analysis refers to converting qualitative behavioral data into quantifiable values ​​so that they can be statistically analyzed. The system generates behavioral data sets for target customers in each business scenario. These data sets usually contain multi-dimensional behavioral characteristics (such as resource utilization, risk indicators, behavioral patterns, etc.). Through quantitative analysis, the system can perform statistics, scoring, modeling, and other processing on these behavioral data in order to more accurately evaluate the performance of customers in specific scenarios. The system can use statistical methods (such as mean, variance, distribution analysis, etc.) or machine learning algorithms (such as cluster analysis, regression analysis, etc.) to perform quantitative analysis on behavioral data sets. For example, in the auto insurance business scenario, the system scores the customer's driving behavior (such as the number of speeding, accident frequency), converts qualitative behavior into quantitative indicators, and generates behavioral scores or trend change data. This process ensures that the behavioral data of each customer is comparable and evaluable in different scenarios.

[0081] Behavioral analysis results are generated through a comprehensive evaluation of quantified behavioral data, reflecting customer behavior within specific time periods and business scenarios. These results can include behavioral patterns, risk levels, and resource utilization efficiency. These insights provide crucial insights for subsequent business decisions. Based on the quantitative analysis results for each business scenario, the system generates behavioral analysis results for each time period. For example, in the auto insurance business scenario, the system analyzes a customer's driving behavior data from the past three months to determine the customer's accident risk trends within a specific time period. Based on these quantitative results, the system categorizes or rates customers to provide insurance companies with targeted pricing and service recommendations.

[0082] The results of behavioral analysis conducted over multiple time periods require a comprehensive analysis across time. This means the system must not only quantify behavior within a single time period but also conduct cross-period analysis across multiple time periods to identify changing trends or long-term performance in customer behavior. The system integrates the behavioral analysis results generated within each time period vertically to analyze changing trends in customer behavior. For example, the system can perform a time series analysis of a customer's driving behavior over the past year to identify changes in driving habits or trends in increased risk. These cross-period behavioral analysis results can serve as a basis for long-term decision-making.

[0083] Example: An insurance company wants to quantify the driving behavior of its auto insurance customers. The system generates a driving behavior score by quantifying the customer's speeding, hard braking, and accident frequency. The system analyzes the customer's driving data over the past three months and finds that the customer's speeding behavior is more frequent on weekends, indicating an increased risk of accidents. Based on the quantitative analysis, the insurance company provides the customer with driving behavior improvement recommendations and adjusts the customer's premium upon renewal.

[0084] By performing quantitative analysis on behavioral datasets within each business scenario, the system transforms qualitative customer behavior data into actionable numerical metrics and generates behavioral analysis results for each time period. These quantitative analysis results provide businesses with more accurate customer behavior assessments, helping them develop more effective business strategies across multiple business scenarios. Based on these analysis results, businesses can adjust customer insurance products, risk assessments, and pricing strategies, thereby improving business flexibility and customer satisfaction.

[0085] S80, based on the behavior analysis results of the target customer in each business scenario in each time period and combined with the historical behavior data of the target customer, generate a target business strategy for the target customer.

[0086] In this embodiment, the system comprehensively evaluates target customers' performance in specific situations based on behavioral analysis results across multiple business scenarios over different time periods. These behavioral analysis results, including customer behavior patterns, resource utilization, and risk assessments, can provide data support for subsequent business strategies. The system combines behavioral analysis results for customers in different business scenarios within each time period to conduct a global assessment. For example, in the auto insurance scenario, the system analyzes a customer's driving behavior data over each month and, based on their accident frequency and driving habits, derives their risk level. This comprehensive behavioral analysis can provide a comprehensive picture of the customer, providing a reference for the subsequent business strategy generation.

[0087] The system not only uses the behavioral analysis results for the current time period but also conducts comparative analysis based on historical behavioral data to assess changing trends in customer behavior. Historical behavioral data can be a customer's long-term behavioral record, including past behavior in similar scenarios. Through this comparison, the system can identify long-term changes in customer behavior and thus more accurately generate business strategies. The system compares the behavioral analysis results for each time period with the customer's historical data. For example, in the health insurance field, the system can compare a customer's recent health status with their long-term health records to determine whether their health is improving or deteriorating. Through this comparative analysis, the system can better predict a customer's future behavior and provide a more accurate basis for formulating business strategies.

[0088] Based on a comparison of comprehensive behavioral analysis results and historical behavioral data, the system generates personalized business strategies tailored to target customers. Business strategies can include recommendations for optimizing customer resource allocation, providing personalized insurance products, or adjusting a customer's risk level or premium. Targeted business strategies are designed to help companies flexibly adjust their business offerings based on changes in customer behavior. After a comprehensive analysis of target customers' behavioral data, the system generates specific business strategies based on the evaluation results. For example, in a life insurance scenario, the system can recommend flexible payment plans based on a customer's financial situation or adjust insurance coverage based on their risk profile. These generated strategies directly influence subsequent business decisions, helping companies optimize services and manage customer relationships.

[0089] Example: A car insurance company analyzes a customer's driving behavior and accident records. Combined with historical driving habits, it discovers that the customer's accident frequency has increased over the past three months. The system generates business strategies such as increasing premiums, recommending driver safety training courses, or providing additional safety equipment. These strategies help the insurance company reduce future claims risk while providing customers with targeted safety improvement recommendations.

[0090] By combining behavioral analysis results from each business scenario within each time period, the system can comprehensively assess customer behavior patterns and risk changes. By combining historical behavioral data for comparative analysis, the system can not only identify the customer's current status but also predict their future behavioral trends, thereby generating personalized business strategies for each customer. This strategy, generated based on multi-dimensional data analysis, helps companies flexibly adjust their business strategies in different scenarios, improving the accuracy and responsiveness of customer service while effectively reducing operational risks.

[0091] The present invention relates to the fields of big data technology and financial technology, and discloses a decision-making method based on big data. By collecting multidimensional data related to the target business, extracting key features such as customer behavior patterns, potential problems, and resource stability, a customer analysis model is constructed, and a time series data set is generated in combination with behavior trend data; a behavior data set is generated through scenario analysis and quantitative analysis is performed to derive customer behavior patterns and resource utilization trends, and ultimately generate strategies adapted to various business scenarios. The present invention collects multidimensional data, extracts customer features, constructs an analysis model, and analyzes customer behavior trends to generate a time series data set. Scenario analysis and quantitative processing are performed in combination with different business scenarios, and ultimately business strategies are generated. This realizes real-time analysis of multidimensional data, scenario-based decision-making, and dynamic adjustment, thereby improving the flexibility and accuracy of the system in complex business scenarios and the efficiency and accuracy of business decisions.

[0092] In one embodiment, in S10 above, collecting multi-dimensional data related to the target business includes:

[0093] S101, identifying a multi-dimensional data source related to a target business, wherein the multi-dimensional data source includes a customer database, a credit inquiry platform, and / or a public resource data platform;

[0094] S102, configuring corresponding data collection interfaces for different data sources, wherein the data collection interfaces are used to obtain customer personal information, historical behavior data, resource status, and / or credit records from the data sources;

[0095] S103 , performing unified formatting processing on the collected data, converting the data from different data sources into a standardized data structure, wherein the unified formatting processing includes unifying the time format and converting the numerical unit.

[0096] In this embodiment, a multidimensional data source refers to a data source related to the target business and obtained from multiple channels, including customer databases, credit inquiry platforms, and public resource data platforms. Different data sources provide customer information of different dimensions, which helps to form a global understanding of the customer. For example, a customer database can provide basic information about the customer, a credit inquiry platform can provide credit records, and a public resource data platform can provide the customer's financial or resource status. The system first identifies the data source related to the target business through configuration and determines which external or internal platforms need to obtain data from. The system connects customer databases, third-party credit platforms (such as credit rating companies), and public resource data platforms (such as government websites, market research platforms), etc., to ensure that multidimensional information of target customers can be comprehensively collected.

[0097] The data collection interface is an interface module used to obtain customer information from various data sources. Each data source may have different formats, protocols, and access methods, so a corresponding interface needs to be configured for each data source to ensure that data can be smoothly and accurately collected from different platforms into the system. For example, the customer database can use API calls, while the public resource data platform may collect data by regularly importing data tables. The system designs and develops a corresponding data collection interface for each data source. For customer databases, the system may use a database connection driver to directly access data; for credit inquiry platforms, the system may call through API interfaces; for public resource data platforms, the system may use data crawlers or regular update functions to collect data. Each interface module is designed according to the characteristics of the data source to ensure the real-time and integrity of data collection.

[0098] Different data sources usually provide data in different formats, which may include different time formats, numerical units, etc. In order to ensure that the system can process and analyze multidimensional data from different data sources, unified formatting is required to standardize all data into a unified structure. The formatting process includes unifying the time format, converting numerical units, etc. to ensure the consistency and comparability of the data. After data collection, the system preprocesses all data. First, the system checks the time format of the data and converts it into a unified standard format (such as ISO 8601 format). Next, the system standardizes the numerical units to ensure that all data is expressed in consistent units. For data in different units, the system can convert them according to predefined conversion rules, such as converting US dollars into local currency units. The formatted data can be seamlessly connected in subsequent analysis.

[0099] This embodiment collects multidimensional data related to the target business from multiple data sources and uniformly formats the data, enabling the system to provide comprehensive, standardized data support. The integration and standardization of multidimensional data enables the system to efficiently process data across multiple data sources, improving data availability and consistency. This ensures more accurate data analysis and decision-making in different business scenarios, helping enterprises develop personalized product and service strategies.

[0100] In one embodiment, the above S20 includes:

[0101] S201, analyzing the customer's historical behavior data to determine the customer's behavior pattern, wherein the behavior pattern includes the customer's usage habits, interaction frequency, and access behavior;

[0102] S202, analyzing the customer's credit record to determine potential problem indicators of the customer, including the possibility of insufficient performance ability and abnormal behavior of the customer;

[0103] S203, analyzing the client's resource status and determining the client's resource stability, wherein the resource status includes the client's resource sources, expenditures, and available resources;

[0104] S204, integrating the customer's behavior pattern, potential problem indicators and resource stability to generate a comprehensive customer feature for business decision-making.

[0105] In this embodiment, historical behavior data refers to behavioral information such as customer interactions and usage records within a specific time period. By analyzing this data, the system can identify customer behavior patterns, such as usage habits, interaction frequency, and access behavior. These behavioral patterns help companies understand customer preferences and behavioral patterns, thereby providing a basis for business decisions. The system extracts specific indicators from the customer's historical behavior data, such as daily access frequency, interaction behavior (such as purchases, inquiries), and usage habits (such as product access time and usage frequency). The system can use data analysis tools and models (such as cluster analysis, association rule mining, etc.) to identify customer behavior patterns. For example, in the insurance industry, analyze customer claims records, policy management behaviors, etc. to determine the customer's insurance usage habits and frequency.

[0106] Credit records reflect a customer's performance history and credit risk. By analyzing a customer's credit record, the system identifies potential risks and issues, and determines the possibility of insufficient performance and abnormal behavior. Potential problem indicators can help companies provide early warnings of potential default risks or abnormal behavior. The system obtains a customer's credit history (such as credit score, outstanding records, etc.) from a third-party credit inquiry platform or internal credit data, and identifies the customer's credit risk by analyzing changes in the customer's credit score and loan performance. For example, in the insurance industry, the system can assess a customer's ability to fulfill contracts in the future and identify potential default risks by analyzing the customer's loan performance history and debt situation.

[0107] Resource status refers to a client's economic, financial, and material stability. By analyzing a client's resource sources, expenditures, and available resources, the system can assess their resource stability. This characteristic directly impacts the client's decision-making ability and risk tolerance in future business. The system assesses resource stability by analyzing a client's resource data, such as income sources, expenditure records, and available resources. For example, the system can analyze a client's monthly income, expenditure stability, and asset status to calculate their resource health and risk tolerance. For example, in the insurance sector, analyzing a client's income and expenditure status can assess their ability to consistently pay insurance premiums.

[0108] The goal of integrating customer behavior patterns, potential problem indicators, and resource stability is to create a comprehensive customer profile, helping businesses make more accurate decisions. By comprehensively considering these diverse characteristics, businesses can better assess a customer's business value, risk profile, and potential for future business collaboration. The system combines these three categories of characteristics: behavior patterns, potential problem indicators, and resource stability, to form a comprehensive customer profile. These comprehensive characteristics can be quantified using a scoring model to facilitate subsequent business decisions. For example, the system integrates a customer's insurance product usage behavior, credit risk, and income stability to generate a comprehensive score that can be used to tailor product recommendations or adjust premium structures.

[0109] This embodiment extracts customer behavior patterns, potential problem indicators, and resource stability from multidimensional data, enabling the system to provide more comprehensive support for business decision-making. The system can identify customer behavior patterns, potential risks, and resource health, helping companies develop more precise business strategies. This can effectively enhance companies' risk management capabilities, improve customer satisfaction, and increase business growth potential.

[0110] In one embodiment, the above S30 includes:

[0111] S301, determining a basic model based on the complexity of the customer characteristics and target business requirements, the basic model including a decision tree, a support vector machine, or a neural network;

[0112] S302, dividing the customer feature data into a training set and a validation set;

[0113] S303, inputting the training set of customer feature data into the basic model, performing preliminary model training, and optimizing the parameters of the basic model through iteration;

[0114] S304: Use the validation set to analyze the trained model, determine the performance index results of the model on the validation set, and adjust the hyperparameters of the model according to the performance index results to generate a trained customer analysis model.

[0115] In this embodiment, the basic model is a statistical or machine learning model selected based on the diversity and complexity of customer characteristics and the business needs of the enterprise. The choice of the basic model directly determines the effect of subsequent model training and the accuracy of business applications. Commonly used basic models include decision trees, support vector machines (SVMs) and neural networks. The system first selects a suitable basic model based on the scale of customer feature data, data dimensions and business objectives. For example, decision trees are suitable for scenarios where the data structure is relatively simple and the decision path can be expressed in a tree structure; support vector machines are suitable for classification tasks of small-scale high-dimensional data; neural networks are suitable for scenarios that process large-scale, complex data, such as predicting customer behavior trends or long-term customer churn rates.

[0116] When building a machine learning model, the dataset typically needs to be divided into a training set and a validation set. The training set is used to train the model, while the validation set is used to evaluate the model's performance, ensuring that the model does not overfit the training data and performs well on unseen data. The partitioning of the dataset typically follows a certain ratio, such as 80% of the data as the training set and 20% as the validation set. The system randomly divides the collected customer feature data, using the majority of the data as the training set and the remainder as the validation set. The system ensures that the training set covers as many customer features as possible, while the validation set is used for a preliminary assessment of model performance. In the auto insurance scenario, the system may divide the customer's driving data, credit score, and other information by time period to ensure that the time and data distribution between the training and validation sets are representative.

[0117] Model training is the process of learning and adjusting model parameters based on the model's structure using data from the training set. The system uses customer feature data to initially train the base model, adjusting internal model parameters (such as the branching structure of a decision tree, the boundaries of a support vector machine, or the weights of a neural network) to improve the model's ability to recognize characteristic patterns. The system inputs the training set data into the selected base model. For example, in a decision tree model, the system constructs a decision tree by splitting customer features; in a neural network, the system adjusts the network's weights through multiple rounds of iteration. Through this process, the system continuously optimizes the model's parameters, enabling it to better classify or predict customer features.

[0118] The purpose of using a validation set is to evaluate the performance of a trained model on unseen data. The validation set helps assess the model's generalization ability and provides feedback for model optimization. By evaluating the validation set, the system can detect whether the model is overfitting. The system inputs the validation set into the trained model and calculates model performance metrics such as accuracy, recall, and F1 score. In an auto insurance scenario, the system might measure model performance by verifying the accuracy of its predictions about a customer's driving behavior. Based on the analysis results on the validation set, the system assesses whether the model's performance meets business requirements.

[0119] Hyperparameter tuning aims to optimize the overall performance of the model. Hyperparameters include the model structure, learning rate, and regularization parameters. Based on the performance results on the validation set, the system adjusts the model's hyperparameters, ultimately generating an optimized model. This process is typically completed through multiple iterations and validations. The system adjusts the model's hyperparameters based on the validation set analysis results. For example, for a neural network model, the system might adjust the number of hidden layers or the learning rate; for a support vector machine, the system might adjust the kernel function or the regularization coefficient. Through multiple iterations, the system optimizes the hyperparameters to generate the optimal customer analysis model. After validation, the final customer analysis model can be used for tasks such as predicting customer behavior and risk assessment.

[0120] This embodiment builds a customer analysis model based on customer characteristics, enabling the system to better predict customer behavior and assess business risks. Through multi-dimensional feature model training and performance evaluation on a validation set, the system ensures the model has good generalization capabilities and can generate personalized business strategies based on the characteristics of different customers.

[0121] In one embodiment, the above S40 includes:

[0122] S401, collecting target customers’ behavior data and resource status through real-time data monitoring module;

[0123] S402, generating an interaction relationship diagram between the target customer and external related entities based on the target customer's behavior data and resource status;

[0124] S403, analyzing the interaction behavior between the target customer and the external related entities based on the interaction relationship diagram, and determining the key features that affect the target customer's behavior;

[0125] S404: Input the key features into a customer analysis model to analyze the target customer's behavior trend data, where the behavior trend data reflects the target customer's behavior analysis results over multiple time periods.

[0126] In this embodiment, the real-time data monitoring module is responsible for collecting the target customer's behavioral data in a specific business and their resource status. These data may include the customer's usage behavior (such as access frequency, interactive operations, etc.) and resource status (such as account balance, credit status, asset information, etc.). Through real-time monitoring, the system can promptly capture changes in customer behavior and analyze their impact on the business. The system deploys a real-time data monitoring module to regularly collect customers' real-time behavioral data and resource status. For example, an insurance company can monitor a customer's policy operation records, the frequency of initiation of claims requests, account balances, etc. At the same time, by connecting with the data of a third-party resource platform, the system can also obtain the customer's credit record or other resource-related information. These data serve as the basis for subsequent behavioral analysis to ensure that the analysis is timely and accurate.

[0127] An interaction diagram is a visual representation of the interactions between target customers and external entities (such as partners, service providers, and suppliers). Through comprehensive analysis of behavioral data and resource status, the system can reveal the key interactions between customers in the business ecosystem. These relationships help identify factors influencing customer behavior. Based on customer behavioral data, such as insurance product purchase records, policy management behaviors, and interactions with agents, the system generates an interaction diagram between the customer and other related entities. For example, an interaction diagram for an auto insurance customer can display the interactions between the customer and the insurance agent, vehicle repair shop, and partner financial institution. The system visually displays the customer's business network in the form of a diagram and provides background information for subsequent behavioral analysis.

[0128] The system analyzes the interactions between customers and external entities in the interaction graph to identify key factors influencing customer behavior. These key characteristics may include customer dependencies, resource utilization, and response behavior. After identifying these characteristics, the system can further analyze customer behavior trends in specific businesses. By analyzing the nodes and edges in the interaction graph, the system identifies the frequency and patterns of interactions between customers and external entities, as well as the flow of resources. For example, in the insurance sector, the system can analyze customer interactions with claims agencies to identify customer claims behavior patterns and the degree of cooperation and dependence with external agencies. These analysis results are then used in subsequent behavior prediction models.

[0129] The system inputs the key features obtained from the analysis into a previously constructed customer analysis model to predict the behavioral trends of target customers. Behavioral trend data refers to the patterns of changes in customer behavior over a specific time period. This data can be used to assess customers' future decisions, needs, or risks. Based on identified key customer characteristics, such as the intensity of the customer's interaction with external entities and the frequency of resource usage, the system inputs these characteristics into the customer analysis model. The customer analysis model processes these characteristics to generate behavioral trend prediction data for target customers. For example, an auto insurance company can use data such as a customer's vehicle usage history and maintenance frequency to predict a customer's likely future insurance needs or risk level. These predictions can help companies provide customers with more targeted services and products.

[0130] This embodiment uses real-time monitoring of target customers' behavioral data and resource status to identify key interactions between customers and external entities, thereby extracting key features that influence customer behavior. By feeding these features into a customer analysis model, the system can predict customer behavior trends, helping companies more accurately provide customized services or products for future business decisions, improving customer satisfaction while reducing business risks.

[0131] In one embodiment, the above S60 includes:

[0132] S601, defining multiple business scenarios according to target business requirements, wherein the business scenarios include regular usage scenarios, emergency event handling scenarios, or resource fluctuation scenarios, and generating a unique identifier for each business scenario;

[0133] S602, analyzing the time series data in each time period based on different business scenarios to generate behavior analysis results of target customers in each time period in each business scenario;

[0134] S603, generating a corresponding behavior data set based on the behavior analysis results of each business scenario in each time period, wherein the behavior data set includes the behavior pattern, resource utilization, and change trend of the target customer in each time period;

[0135] S604: Integrate the behavioral data sets of each business scenario in each time period to form a comprehensive analysis result.

[0136] In this embodiment, a business scenario refers to a behavioral pattern in a specific business situation or situation. The system defines a variety of possible scenarios based on different business needs. Common scenarios include routine usage scenarios (customer's daily behavior), emergency handling scenarios (dealing with unforeseen situations), and resource fluctuation scenarios (situations with resource shortages or excess resources). These scenarios help analyze customer behavior in different environments. The system defines multiple specific scenarios based on the needs of the insurance business. For example, in the health insurance business, a routine scenario may be a customer's normal use of medical services, while an emergency scenario may involve sudden illness or emergency medical needs. Each scenario will be assigned a unique identifier to ensure that the system can clearly identify the behavioral pattern corresponding to each scenario during subsequent processing and analysis.

[0137] Time series data is a record of customer behavior over a specific time period. The system analyzes this data based on different business scenarios to identify customer behavior patterns in specific scenarios. The behavior in each business scenario may be different, so the system needs to perform independent analysis in each time period based on the time series data. The system segments the time series data by time period and analyzes the data for each scenario. For example, in the auto insurance scenario, the system will analyze the customer's regular driving behavior (regular scenario) and the behavior in handling sudden accidents (sudden scenario). The system will model the behavioral data for each scenario and generate customer behavior analysis results for that time period.

[0138] The behavioral analysis results for each business scenario within each time period include customer behavior patterns, resource utilization, and changing trends at that specific time and scenario. These analysis results are organized into behavioral datasets for more comprehensive business decision-making and forecasting. Based on the analyzed behavioral patterns, the system generates datasets containing customer behavior data, resource utilization data, and changing trends. For example, in a resource fluctuation scenario, the system analyzes customer responses to resource shortages and the changing trends in their resource usage. Behavioral analysis results are generated separately for each time period and archived separately by scenario for further analysis.

[0139] The system will integrate the behavioral data sets in each business scenario within each time period to form a comprehensive analysis result. The comprehensive analysis can provide the customer's overall behavioral performance in different scenarios, as well as the changes in behavioral patterns over time and context. This analysis helps companies fully understand the customer's behavioral trends and risk situations. The system aggregates the data sets of multiple business scenarios in each time period, combines the customer's overall behavioral patterns and resource utilization trends, and generates a comprehensive analysis result. For example, a health insurance company can generate a comprehensive health risk assessment report covering the entire time period based on the customer's health management behavior, response to sudden illnesses, etc. This report combines the health status in regular scenarios and sudden scenarios to provide insurance companies with a more comprehensive customer portrait.

[0140] This embodiment uses scenario analysis on time series data from different business scenarios to refine customer behavior patterns and their changing trends in various scenarios. By integrating behavioral data sets from various business scenarios, companies can obtain a more comprehensive customer behavior portrait.

[0141] In one embodiment, the above S80 includes:

[0142] S801, comparing the behavior analysis results of the target customer in each business scenario in each time period with the historical behavior data of the target customer to analyze the changing trends of the customer's behavior pattern and the changes in resource utilization;

[0143] S802: Generate a business strategy for each business scenario based on the changing trends of customer behavior patterns and resource utilization, combined with the needs of each business scenario. The business strategy includes resource optimization suggestions, behavior pattern adjustment suggestions, and potential risk warnings.

[0144] S803: Send the business policy for each business scenario to the business processing end.

[0145] In this embodiment, the system first obtains the behavior analysis results of the target customers in each time period and each business scenario, and compares these results with the customer's historical behavior data. Through comparison, the system can identify changes in customer behavior patterns and analyze whether these changes are abnormal or pose business risks. The system obtains customer behavior data for each time period and each business scenario from the analysis module (for example, in health insurance, the customer's health management behavior or the handling of sudden medical events). The system then compares this data with the customer's historical behavior data (for example, past health examination records or long-term medical expenditure trends) to identify changing trends in customer behavior patterns and changes in resource usage. These analysis results will serve as the basis for generating business strategies.

[0146] By analyzing the changing trends of customer behavior patterns, the system can determine whether the customer has developed new behavior patterns or deviations, and by analyzing changes in the customer's resource utilization, identify whether the customer is effectively utilizing their resources (such as money, time, medical resources, etc.). The system identifies changes in customer behavior patterns by comparing current behavior data with historical behavior data. For example, in auto insurance, the system may find that the customer's driving behavior has become more frequent or aggressive within a specific time period. In terms of resource utilization, the system can also monitor changes in the customer's resources (such as insurance coverage) to analyze whether the customer has effectively utilized their resources in risk events.

[0147] Based on the analyzed trends in behavioral patterns and resource utilization, and combined with the specific needs of each business scenario, the system generates corresponding business strategies. Business strategies can include resource optimization suggestions, behavioral adjustment suggestions, and potential risk warnings for customers. The system generates customized business strategies for different business scenarios. For example, in the field of health insurance, if a customer's health behavior patterns indicate that their health condition has deteriorated, the system can generate health management optimization suggestions, such as increasing the frequency of physical examinations or changing health management plans. For customers who under-utilize or over-use resources, the system can generate resource optimization strategies to help customers use insurance resources more effectively.

[0148] System-generated business strategies typically include several key components: resource optimization suggestions to help customers utilize resources more efficiently in future business, behavioral pattern adjustment suggestions to address customers' negative behaviors in specific scenarios, and potential risk warnings to provide early warnings of risks customers may face in the future. System-generated business strategies can be optimized based on the customer's actual situation. For example, for car insurance customers who frequently have accidents, the system will generate driving behavior adjustment suggestions and issue warnings about their future accident risks. In the health insurance sector, the system may generate resource optimization suggestions, such as advising customers to use medical resources more rationally to avoid the risk of overspending on medical expenses.

[0149] The system ultimately sends the business policies generated for each business scenario to the business processing end for further processing by the business department or related systems. For example, the system can pass the business policies to the relevant insurance manager or customer service platform for execution or communication with the customer. The system sends the policies generated for each business scenario to the relevant business processing system via standardized interfaces or APIs. For example, in the insurance system, the policies can be passed to the account manager's CRM system, who can then communicate with the customer based on these policies and adjust the customer's premium or service content.

[0150] This embodiment analyzes customer behavior trends and resource utilization to generate personalized strategies tailored to specific customer needs. These strategies, including resource optimization, behavioral adjustments, and potential risk warnings, can help companies more effectively manage customer relationships, mitigate risks, and provide personalized service recommendations. Once business strategies are delivered to the business processing end, companies can quickly implement and provide feedback, improving service quality and customer satisfaction.

[0151] In one embodiment, a decision-making device based on big data is provided, and the decision-making device based on big data corresponds to the decision-making method based on big data in the above embodiment. Figure 3 , Figure 3 This is a functional module diagram of a preferred embodiment of a big data-based decision-making device according to the present invention. It includes a data collection module 10, a feature extraction module 20, a customer analysis model construction module 30, a customer behavior analysis module 40, a time series processing module 50, a scenario analysis module 60, a quantitative analysis module 70, and a business strategy generation module 80. Each functional module is described in detail below:

[0152] A data collection module 10 is used to collect multi-dimensional data related to the target business, wherein the multi-dimensional data includes customer personal information, historical behavior data, resource status and / or credit history;

[0153] A feature extraction module 20 is used to extract customer features that affect business decisions from the multidimensional data, wherein the customer features include customer behavior patterns, potential problem indicators, and / or resource stability;

[0154] A customer analysis model building module 30 is used to build a customer analysis model based on the customer characteristics;

[0155] A customer behavior analysis module 40 is configured to obtain key features of target customers and analyze the target customers' behavior trend data based on the key features using the customer analysis model;

[0156] A time series processing module 50 is used to segment the target customer's behavior trend data according to time periods to generate a time series data set, wherein the time series data set includes behavior trend data within multiple time periods;

[0157] A scenario analysis module 60 is used to perform scenario analysis on the time series dataset in combination with multiple business scenarios to generate a corresponding behavior dataset for each business scenario;

[0158] Quantitative analysis module 70, for performing quantitative analysis on the behavioral data set corresponding to each business scenario, and generating behavioral analysis results of target customers in each business scenario within each time period;

[0159] The business strategy generation module 80 is used to generate a target business strategy for the target customer based on the behavior analysis results of the target customer in each business scenario in each time period and in combination with the historical behavior data of the target customer.

[0160] In one embodiment, the data collection module 10 is specifically configured to:

[0161] Identifying multidimensional data sources related to the target business, wherein the multidimensional data sources include a customer database, a credit inquiry platform, and / or a public resource data platform;

[0162] Configuring corresponding data collection interfaces for different data sources, wherein the data collection interfaces are used to obtain customer personal information, historical behavior data, resource status and / or credit records from the data sources;

[0163] The collected data is formatted uniformly to convert data from different data sources into a standardized data structure. The uniform formatting includes the unification of time formats and the conversion of numerical units.

[0164] In one embodiment, the feature extraction module 20 is specifically configured to:

[0165] Analyze the customer's historical behavior data to determine the customer's behavior pattern, including the customer's usage habits, interaction frequency, and access behavior;

[0166] Analyze the customer's credit history and identify potential problem indicators of the customer, including the possibility of insufficient performance ability and abnormal behavior of the customer;

[0167] Analyze the client's resource status and determine the client's resource stability, including the client's resource sources, expenditures, and available resources;

[0168] Integrate customer behavior patterns, potential problem indicators, and resource stability to generate comprehensive customer profiles for business decision-making.

[0169] In one embodiment, the customer analysis model building module 30 is specifically configured to:

[0170] Determine a basic model based on the complexity of the customer characteristics and target business needs, wherein the basic model includes a decision tree, a support vector machine, or a neural network;

[0171] Dividing the customer feature data into a training set and a validation set;

[0172] Inputting the training set of customer feature data into the basic model to perform preliminary model training, and optimizing the parameters of the basic model through iteration;

[0173] Use the validation set to analyze the trained model, determine the performance indicator results of the model on the validation set, and adjust the model's hyperparameters based on the performance indicator results to generate a trained customer analysis model.

[0174] In one embodiment, the customer behavior analysis module 40 is specifically configured to:

[0175] Collect target customers' behavior data and resource status through real-time data monitoring module;

[0176] Based on the target customer's behavior data and resource status, generate an interaction relationship diagram between the target customer and external related entities;

[0177] Based on the interaction relationship diagram, analyzing the interaction behavior between the target customer and the external related entities, and determining the key features that affect the target customer's behavior;

[0178] The key features are input into a customer analysis model to analyze the behavior trend data of the target customers, where the behavior trend data reflects the behavior analysis results of the target customers in multiple time periods.

[0179] In one embodiment, the scene analysis module 60 is specifically configured to:

[0180] Based on target business needs, define multiple business scenarios, including regular usage scenarios, emergency event handling scenarios, or resource fluctuation scenarios, and generate a unique identifier for each business scenario;

[0181] Based on different business scenarios, analyze the time series data within each time period to generate the target customer's behavior analysis results in each time period in each business scenario;

[0182] Generate a corresponding behavioral data set based on the behavioral analysis results of each business scenario in each time period. The behavioral data set contains the target customer's behavior pattern, resource utilization, and change trend in each time period.

[0183] Integrate the behavioral data sets of each business scenario in each time period to form comprehensive analysis results.

[0184] In one embodiment, the business policy generation module 80 is specifically configured to:

[0185] Compare the behavior analysis results of target customers in each business scenario in each time period with the target customers' historical behavior data to analyze the changing trends of customers' behavior patterns and changes in resource utilization;

[0186] Generate business strategies for each business scenario based on the changing trends of customer behavior patterns and resource utilization, combined with the needs of each business scenario. These strategies include resource optimization suggestions, behavior pattern adjustment suggestions, and potential risk warnings.

[0187] Send the business strategy for each business scenario to the business processing end.

[0188] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, memory, network interface and database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external user terminal via a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the server side of a decision-making method based on big data.

[0189] In one embodiment, a computer device is provided. The computer device may be a user terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the user side of a decision-making method based on big data.

[0190] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed:

[0191] Collect multi-dimensional data related to the target business, including customer personal information, historical behavior data, resource status and / or credit history;

[0192] extracting customer characteristics that influence business decisions from the multidimensional data, the customer characteristics including customer behavior patterns, potential problem indicators, and / or resource stability;

[0193] Building a customer analysis model based on the customer characteristics;

[0194] Obtaining key features of target customers, and analyzing behavioral trend data of the target customers based on the key features using the customer analysis model;

[0195] Segmenting the target customer's behavioral trend data according to time periods to generate a time series data set, wherein the time series data set includes behavioral trend data within multiple time periods;

[0196] Combine multiple business scenarios to perform scenario analysis on the time series dataset and generate a corresponding behavior dataset for each business scenario;

[0197] Conduct quantitative analysis on the behavioral data sets corresponding to each business scenario, and generate behavioral analysis results for target customers in each business scenario within each time period;

[0198] Based on the behavioral analysis results of target customers in each business scenario in each time period, combined with the target customers' historical behavioral data, target business strategies are generated for target customers.

[0199] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0200] Collect multi-dimensional data related to the target business, including customer personal information, historical behavior data, resource status and / or credit history;

[0201] extracting customer characteristics that influence business decisions from the multidimensional data, the customer characteristics including customer behavior patterns, potential problem indicators, and / or resource stability;

[0202] Building a customer analysis model based on the customer characteristics;

[0203] Obtaining key features of target customers, and analyzing behavioral trend data of the target customers based on the key features using the customer analysis model;

[0204] Segmenting the target customer's behavioral trend data according to time periods to generate a time series data set, wherein the time series data set includes behavioral trend data within multiple time periods;

[0205] Combine multiple business scenarios to perform scenario analysis on the time series dataset and generate a corresponding behavior dataset for each business scenario;

[0206] Conduct quantitative analysis on the behavioral data sets corresponding to each business scenario, and generate behavioral analysis results for target customers in each business scenario within each time period;

[0207] Based on the behavioral analysis results of target customers in each business scenario in each time period, combined with the target customers' historical behavioral data, target business strategies are generated for target customers.

[0208] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the user side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0209] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0210] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0211] It should be noted that if any software tools or components other than those of the Company appear in the embodiments of this application, they are merely for illustration and do not represent actual use. The above embodiments are intended only to illustrate the technical solutions of the present invention, not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein with equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A decision-making method based on big data, characterized in that: The following steps are involved: Collect multi-dimensional data related to the target business, including customer personal information, historical behavior data, resource status and / or credit history; extracting customer characteristics that influence business decisions from the multidimensional data, the customer characteristics including customer behavior patterns, potential problem indicators, and / or resource stability; Building a customer analysis model based on the customer characteristics; Determining key features that influence the target customer's behavior based on the target customer's behavior data and resource status, and analyzing the target customer's behavior trend data based on the key features using the customer analysis model, wherein the behavior trend data reflects the target customer's behavior analysis results over multiple time periods; Segmenting the target customer's behavioral trend data according to time periods to generate a time series data set, wherein the time series data set includes behavioral trend data within multiple time periods; Based on the target business needs, multiple business scenarios are defined, including regular usage scenarios, emergency event handling scenarios, or resource fluctuation scenarios. Based on different business scenarios, time series data within each time period is analyzed to generate corresponding behavior datasets for each business scenario. The behavior datasets contain the target customers' behavior patterns, resource utilization, and change trends within each time period. Conduct quantitative analysis on the behavioral data sets corresponding to each business scenario, and generate behavioral analysis results for target customers in each business scenario within each time period; Compare the behavior analysis results of target customers in each business scenario in each time period with the historical behavior data of target customers, analyze the changing trends of customers' behavior patterns and resource utilization, and generate target business strategies for target customers based on the changing trends of customers' behavior patterns and resource utilization.

2. The decision-making method based on big data according to claim 1, characterized in that: Collect multi-dimensional data related to the target business, including: Identifying multidimensional data sources related to the target business, wherein the multidimensional data sources include a customer database, a credit inquiry platform, and / or a public resource data platform; Configuring corresponding data collection interfaces for different data sources, wherein the data collection interfaces are used to obtain customer personal information, historical behavior data, resource status and / or credit records from the data sources; The collected data is formatted uniformly to convert data from different data sources into a standardized data structure. The uniform formatting includes the unification of time formats and the conversion of numerical units.

3. The decision-making method based on big data according to claim 1, characterized in that: Extracting customer characteristics that influence business decisions from the multidimensional data, wherein the customer characteristics include customer behavior patterns, potential problem indicators, and / or resource stability, including: Analyze the customer's historical behavior data to determine the customer's behavior pattern, including the customer's usage habits, interaction frequency, and access behavior; Analyze the customer's credit history and identify potential problem indicators of the customer, including the possibility of insufficient performance ability and abnormal behavior of the customer; Analyze the client's resource status and determine the client's resource stability, including the client's resource sources, expenditures, and available resources; Integrate customer behavior patterns, potential problem indicators, and resource stability to generate comprehensive customer profiles for business decision-making.

4. The decision-making method based on big data according to claim 1, characterized in that: Based on the customer characteristics, a customer analysis model is constructed, including: Determine a basic model based on the complexity of the customer characteristics and target business needs, wherein the basic model includes a decision tree, a support vector machine, or a neural network; Dividing the customer feature data into a training set and a validation set; Inputting the training set of customer feature data into the basic model to perform preliminary model training, and optimizing the parameters of the basic model through iteration; Use the validation set to analyze the trained model, determine the performance indicator results of the model on the validation set, and adjust the model's hyperparameters based on the performance indicator results to generate a trained customer analysis model.

5. The decision-making method based on big data according to claim 1, characterized in that: Based on the target customer's behavior data and resource status, key features that influence the target customer's behavior are determined, and the target customer's behavior trend data is analyzed based on the key features using the customer analysis model. The behavior trend data reflects the target customer's behavior analysis results over multiple time periods, including: Collect target customers' behavior data and resource status through real-time data monitoring module; Based on the target customer's behavior data and resource status, generate an interaction relationship diagram between the target customer and external related entities; Based on the interaction relationship diagram, analyzing the interaction behavior between the target customer and the external related entities, and determining the key features that affect the target customer's behavior; The key features are input into a customer analysis model to analyze the behavior trend data of the target customers, where the behavior trend data reflects the behavior analysis results of the target customers in multiple time periods.

6. The decision-making method based on big data according to claim 1, characterized in that: Based on the target business needs, multiple business scenarios are defined. These business scenarios include regular usage scenarios, emergency event handling scenarios, or resource fluctuation scenarios. Based on different business scenarios, the time series data within each time period is analyzed to generate a corresponding behavior dataset for each business scenario. The behavior dataset contains the target customer's behavior patterns, resource utilization, and change trends within each time period, including: Based on target business needs, define multiple business scenarios, including regular usage scenarios, emergency event handling scenarios, or resource fluctuation scenarios, and generate a unique identifier for each business scenario; Based on different business scenarios, analyze the time series data within each time period to generate the target customer's behavior analysis results in each time period in each business scenario; Generate a corresponding behavioral data set based on the behavioral analysis results of each business scenario in each time period. The behavioral data set contains the target customer's behavior pattern, resource utilization, and change trend in each time period. Integrate the behavioral data sets of each business scenario in each time period to form comprehensive analysis results.

7. The decision-making method based on big data according to claim 1, characterized in that: Compare the target customer's behavior analysis results for each business scenario in each time period with the target customer's historical behavior data, analyze the changing trends of the customer's behavior patterns and resource utilization, and generate target business strategies for the target customer based on the changing trends of the customer's behavior patterns and resource utilization, including: Compare the behavior analysis results of target customers in each business scenario in each time period with the target customers' historical behavior data to analyze the changing trends of customers' behavior patterns and changes in resource utilization; Generate business strategies for each business scenario based on the changing trends of customer behavior patterns and resource utilization, combined with the needs of each business scenario. These strategies include resource optimization suggestions, behavior pattern adjustment suggestions, and potential risk warnings. Send the business strategy for each business scenario to the business processing end.

8. A decision-making device based on big data, characterized in that: The big data-based decision-making device includes: A data collection module, configured to collect multi-dimensional data related to the target business, including customer personal information, historical behavior data, resource status, and / or credit history; A feature extraction module is used to extract customer features that affect business decisions from the multidimensional data, wherein the customer features include customer behavior patterns, potential problem indicators and / or resource stability; A customer analysis model building module, used to build a customer analysis model based on the customer characteristics; A customer behavior analysis module is used to determine key features that influence the behavior of target customers based on the behavior data and resource status of the target customers, and to analyze the behavior trend data of the target customers based on the key features using the customer analysis model, wherein the behavior trend data reflects the behavior analysis results of the target customers over multiple time periods; A time series processing module is used to segment the target customer's behavior trend data according to time periods to generate a time series data set, which contains behavior trend data in multiple time periods; The scenario analysis module is used to define multiple business scenarios based on target business needs. The business scenarios include regular usage scenarios, emergency event handling scenarios, or resource fluctuation scenarios. Based on different business scenarios, the module analyzes the time series data within each time period to generate a corresponding behavior dataset for each business scenario. The behavior dataset contains the target customer's behavior patterns, resource utilization, and change trends in each time period. The quantitative analysis module is used to perform quantitative analysis on the corresponding behavioral data set in each business scenario and generate the behavioral analysis results of the target customers in each business scenario within each time period; The business strategy generation module is used to compare the behavior analysis results of target customers in each business scenario in each time period with the historical behavior data of target customers, analyze the changing trends of customers' behavior patterns and resource utilization, and generate target business strategies for target customers based on the changing trends of customers' behavior patterns and resource utilization.

9. A computer device, characterized in that: The computer device includes a memory, a processor, and a big data-based decision generation program stored in the memory and capable of running on the processor. When the big data-based decision generation program is executed by the processor, the steps of the big data-based decision generation method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The storage medium stores a decision-making program based on big data, and when the decision-making program based on big data is executed by the processor, the steps of the decision-making method based on big data as described in any one of claims 1 to 7 are implemented.

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