Risk control management method and system combining discount coupons with rights and interests in financial field

Abstract

The invention discloses a risk control management method and system combining discount coupons with rights and interests in the financial field. The method comprises the steps of data preprocessing, feature engineering, model training, dynamic risk management of discount coupons and rights and interests, real-time risk monitoring, early warning, disposal and the like. According to the invention, an artificial intelligence technology is adopted, a coupon and right risk assessment model is established, and the effectiveness of risk management is improved by identifying potential risks in real time and taking corresponding measures. By monitoring and evaluating the use conditions of coupons and rights in real time, the management cost of the coupons and the rights can be effectively reduced; according to the method, issuing and recycling strategies of coupons and rights and interests are automatically adjusted according to user behaviors and risk levels, and personalized management and refined operation are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-based financial risk control, and particularly relates to a risk control management method and system for combining coupons and rights and interests in the financial field. Background Art

[0002] In the financial field, the management of coupons and rights and interests has always been the focus of financial institutions. However, there are many problems with traditional methods for managing coupons and rights and interests. For example, the abuse of coupons and fraud of rights and interests occur frequently, causing huge losses to financial institutions. In addition, traditional risk management methods often fail to effectively identify and prevent risks in the face of an increasingly complex and changeable financial environment. The management of coupons and rights and interests lacks effective real-time performance and cannot detect and handle problems in a timely manner.

[0003] In the financial field, the abuse and fraud of coupons and rights and interests have become a serious problem. These behaviors not only result in economic losses for financial institutions, but also damage the rights and interests of consumers and affect the stability and reliability of the financial market. To solve these problems, financial institutions need an effective risk control management system to monitor the use of coupons and rights and interests in real time, identify potential risks, and take corresponding measures. Summary of the Invention

[0004] The purpose of the present invention is to provide a risk control management method and system for combining coupons and rights and interests in the financial field to effectively identify, prevent, and solve the risk problems in the management of coupons and rights and interests. By introducing the concepts of coupons and rights and interests, the risk management level of financial institutions is improved, the rights and interests of consumers are protected, and the stability and reliability of the financial system are enhanced.

[0005] The technical solution of the present invention is as follows:

[0006] A risk control management method for combining coupons and rights and interests in the financial field, comprising the following steps:

[0007] (1) Data preprocessing: Collect a large amount of user behavior data, transaction data, and coupon usage history, clean and integrate this data for subsequent analysis;

[0008] (2) Feature engineering: Extract key features, including user behavior patterns, consumption frequency, coupon usage frequency, and account activity level;

[0009] (3) Model training: Use machine learning algorithms such as random forest, gradient boosting machine, or deep learning network to train the features to identify normal and abnormal patterns of coupon and right and interest usage. During the model training process, techniques such as cross-validation are used to optimize the model performance;

[0010] (4) Dynamic Risk Management of Coupons and Benefits: After the model training is completed, the usage of coupons and benefits is monitored in real time, and abnormal behaviors are identified in a timely manner. The issuance and recovery strategies of coupons and benefits are automatically adjusted according to user behaviors and risk levels.

[0011] (5) Real-time Risk Monitoring: Continuously update the risk score based on real-time data to improve the flexibility and adaptability of risk management.

[0012] (6) Early Warning and Disposal: When the risk score exceeds a certain threshold, notify the risk management team to take corresponding disposal measures.

[0013] Furthermore, the dynamic risk management of coupons and benefits specifically includes the following steps:

[0014] a. User Behavior Analysis: By analyzing the user's behavior data, understand the user's consumption habits, preferences, and response patterns, providing data support for personalized coupon issuance.

[0015] b. Risk Level Classification: According to the results of user behavior analysis and the risk assessment model, divide users into different risk levels.

[0016] c. Personalized Coupon Strategy: Develop a personalized coupon issuance strategy based on the user's risk level and preferences.

[0017] d. Dynamic Adjustment: Dynamically adjust the coupon issuance and recovery strategies according to the results of user behaviors and risk assessments.

[0018] e. User Feedback and Optimization: Continuously optimize the coupon strategy through user feedback on coupon usage and market data to improve user satisfaction and the overall effectiveness of coupons.

[0019] The present invention also provides a risk control management system for the combination of coupons and benefits in the financial field, used to implement the above-mentioned risk control management method for the combination of coupons and benefits in the financial field, including a data collection and processing module, a data storage and management module, a risk assessment module, an early warning and disposal module, and a user interface and service module:

[0020] The data collection and processing module is used to collect user behavior data, coupon and benefit data, and perform processing and storage.

[0021] The data storage and management module is used to store the preprocessed data using a data warehouse or a distributed storage system, and use a data management system to maintain the organization, storage, retrieval, and maintenance of the data.

[0022] The risk assessment module is used to conduct risk assessment on the collected data by leveraging big data analysis and artificial intelligence technologies; through a unique deep learning network structure, the present invention can achieve accurate identification of complex data patterns, thereby improving the accuracy of risk assessment.

[0023] The early warning and handling module is used to issue early warnings for high-risk situations based on the risk assessment results and take corresponding handling measures, and automatically adjust the issuance and recovery strategies of coupons and rights according to user behavior and risk levels;

[0024] Among them, the user interface and service module is used to provide a human-computer interaction interface for viewing or performing operations and services including risk assessment reports, managing coupons and rights, and configuring risk parameters.

[0025] The advantages of the present invention are as follows:

[0026] The present invention adopts artificial intelligence technologies to establish a risk assessment model for coupons and rights, and improves the effectiveness of risk management by identifying potential risks in real time and taking corresponding measures.

[0027] By monitoring and evaluating the usage of coupons and rights in real time, the management costs of coupons and rights can be effectively reduced;

[0028] (3) The present invention automatically adjusts the issuance and recovery strategies of coupons and rights according to user behavior and risk levels, realizing personalized management and refined operation. Specific embodiments

[0029] The following further describes the specific embodiments of the present invention. It should be noted here that the descriptions of these embodiments are used to help understand the present invention, but do not constitute a limitation to the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0030] Traditional risk assessment of coupons and rights usually relies on rule engines or simple statistical analysis, and these methods often cannot handle complex data patterns and dynamically changing risk environments.

[0031] The risk control management method combining coupons and rights in the financial field proposed by the present invention improves the accuracy and real-time performance of assessment through the following key steps:

[0032] - Data preprocessing: First, collect a large amount of user behavior data, transaction data, coupon usage history, etc., and this data may come from multiple different systems and channels. Through the data preprocessing stage, clean and integrate this data for subsequent analysis.

[0033] - Feature engineering: Extract key features such as user behavior patterns, consumption frequency, coupon usage frequency, account activity level, etc. These features are crucial for building a risk assessment model.

[0034] - Model training: Use machine learning algorithms such as random forest, gradient boosting machine, or deep learning network to train the features to identify normal and abnormal patterns of coupon and entitlement usage. During the model training process, techniques such as cross-validation can be used to optimize the model performance.

[0035] - Dynamic risk management of coupons and entitlements: Once the model is trained, it can monitor the usage of coupons and entitlements in real time and promptly identify abnormal behaviors such as coupon abuse and false entitlement applications. Automatically adjust the issuance and recovery strategies of coupons and entitlements according to user behavior and risk levels.

[0036] - Dynamic risk scoring: The model will continuously update the risk score based on real-time data, which can quickly respond to market changes and changes in user behavior, improving the flexibility and adaptability of risk management. The real-time monitoring system proposed by the present invention can instantly identify abnormal behaviors and dynamically adjust the coupon and entitlement issuance strategies, thereby realizing instant risk management.

[0037] - Warning and handling: When the risk score exceeds a certain threshold, the system will trigger a warning to notify the risk management team to take corresponding handling measures such as restricting user behavior and freezing the account.

[0038] The specific content of the dynamic risk management of the above-mentioned coupons and entitlements is as follows:

[0039] Traditional coupon and entitlement management is usually static. The issuance and recovery of coupons are carried out according to predetermined rules. This method often fails to meet the personalized needs of different user groups and cannot respond to risk changes in real time. The dynamic risk management of coupons and entitlements proposed by the present invention includes the following key steps:

[0040] - User behavior analysis: Real-time monitor users' consumption behavior, coupon usage behavior, entitlement collection behavior, etc. through a data analysis platform. By analyzing users' behavior data, understand users' consumption habits, preferences, and response patterns, providing data support for personalized coupon issuance.

[0041] - Risk level classification: According to the results of user behavior analysis and the risk assessment model, divide users into different risk levels such as high risk, medium risk, low risk, etc.

[0042] - Personalized coupon strategy: Based on the user's risk level and preferences, formulate a personalized coupon distribution strategy. For example, for high-risk users, the distribution of coupons can be reduced or lower-value coupons can be provided; for low-risk users, the distribution of coupons can be increased or higher-value coupons can be provided, and the distribution volume, validity period, conditions, etc. of the coupons can be dynamically adjusted.

[0043] - Dynamic adjustment: The coupon distribution and recovery strategy is not fixed, but dynamically adjusted according to the results of user behavior and risk assessment. For example, if it is found that a certain coupon is abused, the distribution rules can be immediately adjusted to reduce the scope of use or value of the coupon.

[0044] - User feedback and optimization: Through user feedback on the use of coupons and market data, continuously optimize the coupon strategy to improve user satisfaction and the overall effectiveness of coupons. By analyzing user behavior and preferences, the present invention can provide personalized coupon recommendations for users and continuously optimize the coupon strategy in combination with user feedback to enhance user satisfaction and the overall effectiveness of coupons.

[0045] Specifically, collect user feedback on coupons and rights and interests through channels such as questionnaires, user forums, and social media, analyze the user feedback, and identify the advantages and disadvantages of coupons and rights and interests. According to user feedback and analysis results, continuously optimize the distribution strategy of coupons and rights and interests to enhance user satisfaction and risk management effects.

[0046] The present invention also provides a risk control management system for the combination of coupons and rights and interests in the financial field, including a data collection and processing module, a data storage and management module, a risk assessment module, an early warning and disposal module, and a user interface and service module, and the specific implementation is as follows.

[0047] Establish a data collection and processing module, which is responsible for collecting and processing various data of financial institutions and performing processing and storage. It includes user behavior data, coupon and rights and interests data, etc. These data can be collected through various methods such as front-end applications, back-end systems, and API interfaces, and are uniformly processed and stored. Data processing includes steps such as data cleaning, data integration, and data storage to ensure the quality and consistency of the data.

[0048] Data storage and management module

[0049] - Structured data storage: Adopt a data warehouse or a distributed storage system, such as a relational database, a NoSQL database, etc., to store the preprocessed data.

[0050] - Data management: Utilize a data management system (such as a data warehouse management system) to maintain the organization, storage, retrieval, and maintenance of data to ensure the efficient utilization and security of the data.

[0051] Risk assessment module, which uses big data analysis and artificial intelligence technologies to conduct risk assessment on the collected data. By establishing a risk assessment model, it can analyze users' consumption behaviors and the usage of coupons and benefits, and identify potential risks. The risk assessment model can include various algorithms such as logistic regression, decision tree, neural network, etc. Select a suitable risk assessment model according to actual needs. The following are the technical key points:

[0052] - Data analysis and mining: Apply data mining techniques to extract useful information and patterns from a large amount of data to support risk assessment.

[0053] - Artificial intelligence algorithms: Adopt artificial intelligence algorithms such as machine learning and deep learning to build risk assessment models. These models can learn and adapt to changes in data, improving the accuracy of assessment.

[0054] - Risk scoring mechanism: Through comprehensive analysis of factors such as users' behavior characteristics, coupon usage patterns, and benefit redemption history, score users for risk, realizing quantitative assessment of risks.

[0055] Early warning and handling module:

[0056] This module gives early warnings for high-risk situations based on the risk assessment results and takes corresponding handling measures. Early warning measures can include sending warning notifications, restricting user permissions, freezing accounts, etc. Select appropriate early warning measures according to the risk level and specific circumstances. Handling measures can include investigation and verification, taking legal actions, adjusting coupon and benefit strategies, etc., to ensure effective control of risks.

[0057] This module automatically adjusts the issuance and recovery strategies of coupons and benefits according to users' behaviors and risk levels. By real-time monitoring of users' behaviors and the usage of coupons and benefits, it can timely detect abnormal behaviors and potential risks, and adjust the issuance strategies of coupons and benefits according to actual situations. For example, it can reduce coupon issuance for high-risk users and increase coupon issuance for normal users to achieve personalized management and refined operation.

[0058] User interface and service module:

[0059] - Interactive user interface: Used to provide a human-computer interaction interface, providing a financial institution staff with an intuitive and easy-to-use interface for operations such as viewing risk assessment reports, managing coupons and benefits, and configuring risk parameters.

[0060] - Personalized service: Based on users' risk assessment results and preferences, provide users with personalized coupon recommendations, exclusive benefit services, etc., to enhance user experience and satisfaction.

[0061] The specific process of risk assessment by the above risk assessment module is as follows:

[0062] (1) Data collection:

[0063] - Consumption records: Collect users' consumption records from the transaction system, including information such as transaction amount, transaction time, and commodity category.

[0064] - Coupon usage history: Record the situation of users' coupon usage, including coupon type, usage time, usage frequency, etc.

[0065] - Privilege redemption history: Track the historical records of users' redemption of various privileges, such as points, discounts, gifts, etc.

[0066] (2) Feature engineering:

[0067] - Data preprocessing: Preprocess the collected data, including data cleaning, missing value filling, outlier handling, etc.

[0068] - Feature extraction: Extract key features helpful for risk assessment from the preprocessed data, such as users' consumption amount, coupon type, privilege value, etc.

[0069] (3) Model training and optimization:

[0070] - Select an algorithm: Select a suitable machine learning algorithm, such as logistic regression, decision tree, random forest, neural network, etc., as the initial model for risk assessment.

[0071] - Data splitting: Split the dataset into a training set, a validation set, and a test set to evaluate the generalization ability of the model.

[0072] - Model training: Train the model using the training set and optimize the model performance by continuously adjusting the model parameters.

[0073] - Model evaluation: Evaluate the trained model using the validation set and calculate metrics such as the accuracy, recall rate, and F1 score of the model.

[0074] - Parameter tuning: Further adjust the model parameters according to the model evaluation results to achieve the best prediction effect.

[0075] (4) Risk scoring:

[0076] - Input conversion: Convert the actual behavior data of users into an input format acceptable to the model, such as a feature vector.

[0077] - Risk score calculation: Input the behavior feature vector of users into the trained risk assessment model and calculate the obtained risk score.

[0078] (5) Risk decision-making:

[0079] - Risk level classification: According to the risk score, users are classified into different risk levels, such as high risk, medium risk, low risk, etc.

[0080] - Decision-making: Based on users with different risk levels, corresponding risk management strategies are formulated, such as monitoring, restriction, additional review processes or providing more personalized services, etc.

[0081] The above has described the embodiments of the present invention in detail, but the present invention is not limited to the described embodiments. For those skilled in the art, without departing from the principle and spirit of the present invention, various changes, modifications, substitutions and variations made to these embodiments still fall within the protection scope of the present invention.

Claims

1. A risk control management method for combining coupons and rights and interests in the financial field, characterized in that, It includes the following steps: (1) Data preprocessing: Collect a large amount of user behavior data, transaction data, and coupon usage history, and clean and integrate this data for subsequent analysis; (2) Feature engineering: Extract key features, including user behavior patterns, consumption frequency, coupon usage frequency, and account activity level; (3) Model training: Use machine learning algorithms such as random forest, gradient boosting machine, or deep learning network to train the features to identify normal and abnormal patterns of coupon and entitlement usage. During the model training process, use techniques such as cross-validation to optimize the model performance; (4) Dynamic risk management of coupons and entitlements: After the model training is completed, monitor the usage of coupons and entitlements in real time, and promptly identify abnormal behaviors. Automatically adjust the issuance and recovery strategies of coupons and entitlements according to user behavior and risk levels; (5) Real-time risk monitoring: Continuously update the risk score based on real-time data to improve the flexibility and adaptability of risk management; (6) Early warning and handling: When the risk score exceeds a certain threshold, notify the risk management team to take corresponding handling measures.

2. The risk control management method for the combination of coupons and rights and interests in the financial field according to claim 1, characterized in that: The dynamic risk management of coupons and entitlements specifically includes the following steps: a. User behavior analysis: By analyzing the user's behavior data, understand the user's consumption habits, preferences, and response patterns to provide data support for personalized coupon issuance; b. Risk level classification: According to the results of user behavior analysis and risk assessment models, classify users into different risk levels; c. Personalized coupon strategy: Develop a personalized coupon issuance strategy based on the user's risk level and preferences; d. Dynamic adjustment: Dynamically adjust the issuance and recovery strategies of coupons according to the results of user behavior and risk assessment; e. User feedback and optimization: Continuously optimize the coupon strategy through user feedback on coupon usage and market data to improve user satisfaction and the overall effectiveness of coupons.

3. A risk control management system for the combination of coupons and rights in the financial field, which is used to implement the risk control management method for the combination of coupons and rights in the financial field described in any one of claims 1-2, characterized in that, It includes a data collection and processing module, a data storage and management module, a risk assessment module, an early warning and handling module, and a user interface and service module: The data collection and processing module is used to collect user behavior data, coupon and entitlement data, and process and store them; The data storage and management module is used to store the preprocessed data using a data warehouse or a distributed storage system, and use a data management system to maintain the organization, storage, retrieval, and maintenance of the data; The risk assessment module is used to use big data analysis and artificial intelligence technologies to conduct risk assessment on the collected data; The early warning and handling module is used to issue early warnings for high-risk situations according to the risk assessment results, and take corresponding handling measures, and automatically adjust the issuance and recovery strategies of coupons and entitlements according to user behavior and risk levels; Among them, the user interface and service module is used to provide a human-computer interaction interface for viewing or performing operations and services including risk assessment reports, managing coupons and entitlements, and configuring risk parameters.