Intelligent anti-fraud system based on large model application

Abstract

The invention relates to the field of anti-fraud systems, in particular to an intelligent anti-fraud system based on large model application, which supports access of multiple types of large models by constructing a flexible large model integration platform so as to meet fraud behavior feature extraction requirements of different dimensions. Comprising a data acquisition and preprocessing module, a feature engineering and modeling module, a model training and evaluation module, a real-time monitoring and feedback module and an anti-fraud system deployment and optimization module. According to the method, an advanced large model algorithm and a machine learning technology are deeply fused, and a highly-intelligent and self-adaptive anti-fraud system is constructed. The system can automatically learn and extract fraud modes from massive heterogeneous data, and realizes accurate identification and instant response to complex fraud behaviors.

Description

Technical Field

[0001] The present invention relates to the field of anti-fraud systems, and in particular to an intelligent anti-fraud system based on large model applications. Background Art

[0002] The anti-fraud system is an important security protection measure, and its main purpose is to identify and prevent various types of fraud. It monitors users' transactions and behaviors in real time through data analysis, pattern recognition, machine learning and other technologies to identify abnormal behaviors or potential risks.

[0003] With the acceleration of digital transformation, fraudulent behaviors are becoming increasingly complex and changeable. Traditional rule-based anti-fraud systems have been unable to cope with new fraud methods. Large model technology, especially pre-trained ultra-large-scale language and image models, has demonstrated its outstanding ability to process complex data patterns and understand context, bringing new breakthroughs to the field of intelligent anti-fraud. Summary of the invention

[0004] In view of the problems existing in the background technology, an intelligent anti-fraud system based on large model application is proposed. The system deeply integrates advanced large model algorithms and machine learning technology to build a highly intelligent and adaptive anti-fraud system. The system can automatically learn and refine fraud patterns from massive heterogeneous data, and realize accurate identification and immediate response to complex fraud behaviors.

[0005] The present invention proposes an intelligent anti-fraud system based on big model application. By building a flexible big model integration platform, it supports the access of various types of big models to cope with the needs of fraud behavior feature extraction in different dimensions; it includes data acquisition and preprocessing modules, feature engineering and modeling modules, model training and evaluation modules, real-time monitoring and feedback modules, and anti-fraud system deployment and optimization modules.

[0006] The data collection and preprocessing module uses real-time data collection technology to obtain user behavior and transaction data, and supports real-time fraud detection and identification; the feature engineering and modeling module uses machine learning algorithms and statistical analysis tools to perform feature engineering and extract key features of user behavior and transaction data; the model training and evaluation module identifies and evaluates fraud by building machine learning models; through continuous training and online learning mechanisms, the model training and evaluation module enables the large model to update its understanding of fraud in real time, identify emerging fraud patterns, and significantly improve the timeliness and adaptability of the anti-fraud system; the real-time monitoring and feedback module uses real-time monitoring units to continuously monitor user behavior and transaction data, promptly detect potential fraud, and combine predictive models and real-time data to generate fraud scores and trigger alarms to take corresponding anti-fraud measures; the anti-fraud system deployment and optimization module deploys the constructed anti-fraud system to the cloud computing platform.

[0007] Preferably, the data acquisition and preprocessing module utilizes data cleaning and preprocessing techniques to remove noise and abnormal data.

[0008] Preferably, the data acquisition and preprocessing module utilizes a variety of data processing tools and techniques to process and analyze data in real time.

[0009] Preferably, the feature engineering and modeling module utilizes a distributed storage and computing framework to achieve feature extraction and modeling of large-scale data.

[0010] Preferably, the feature engineering and modeling module converts the feature engineering results into a format acceptable to the model for subsequent machine learning model training and prediction.

[0011] Preferably, the model training and evaluation module evaluates and optimizes the model through cross-validation and indicator evaluation.

[0012] Preferably, the model training and evaluation module predicts and identifies the process of fraudulent behavior through model interpretive analysis and visualization technology.

[0013] Preferably, a feedback mechanism is introduced into the real-time monitoring and feedback module to dynamically update the model and data to adapt to the changes and evolution of fraudulent behavior.

[0014] Preferably, the anti-fraud system deployment and optimization module is used to regularly optimize the system architecture and algorithms.

[0015] Preferably, the anti-fraud system deployment and optimization module is used to introduce automated deployment and monitoring tools.

[0016] Compared with the prior art, the present invention has the following beneficial technical effects: through the deep integration of large model applications, efficient identification and prevention of fraudulent behavior is achieved. Utilizing the powerful characterization ability of the large model, the system can discover subtle and complex fraud patterns from a large amount of data, significantly improving the accuracy of fraud identification. Through real-time data processing and online learning mechanisms, the system can quickly adapt to changes in fraudulent behavior, reduce fraud losses, and improve response speed. Due to the compatibility of large models with various types of data, this system is suitable for anti-fraud application scenarios in multiple industries such as finance, e-commerce, and insurance, and has a high degree of flexibility and generalization capabilities. Although the initial construction investment is large, in the long run, the automated and intelligent anti-fraud process reduces the cost of manual review, improves the overall risk control efficiency, and saves a lot of resources for the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a workflow diagram of the intelligent anti-fraud system based on large model application in the present invention. DETAILED DESCRIPTION

[0018] like Figure 1As shown, the intelligent anti-fraud system based on large model application proposed in the present invention supports the access of various types of large models (such as natural language processing and computer vision models) by building a flexible large model integration platform to cope with the requirements of fraud behavior feature extraction in different dimensions. The above system includes data acquisition and preprocessing module, feature engineering and modeling module, model training and evaluation module, real-time monitoring and feedback module and anti-fraud system deployment and optimization module.

[0019] The data collection and preprocessing module uses real-time data collection technology to obtain user behavior and transaction data, and supports real-time fraud detection and identification.

[0020] The data collection and preprocessing module develops advanced data preprocessing algorithms to efficiently integrate heterogeneous data from various sources such as social media, transaction records, user behavior logs, etc., providing rich and structured input information for large models.

[0021] The feature engineering and modeling module uses machine learning algorithms and statistical analysis tools to perform feature engineering and extract key features of user behavior and transaction data.

[0022] The feature engineering and modeling module uses the deep learning capabilities of the large model to automatically learn and extract fraud-related feature vectors. These features include not only traditional transaction patterns, but also deeper features such as users' language habits and behavior sequences.

[0023] The model training and evaluation module identifies and evaluates fraudulent behavior by constructing machine learning models, such as random forests and logistic regression. Through continuous training and online learning mechanisms, the large model can update its understanding of fraudulent behavior in real time, identify emerging fraud patterns, and significantly improve the timeliness and adaptability of the anti-fraud system.

[0024] The real-time monitoring and feedback module uses the real-time monitoring unit to continuously monitor user behavior and transaction data, promptly detect potential fraud, and combine the predictive model with real-time data to generate fraud scores and trigger alerts to take corresponding anti-fraud measures.

[0025] The real-time monitoring and feedback module combines the prediction model and optimization algorithm of machine learning to dynamically adjust the anti-fraud strategy based on the fraud probability output by the large model, thereby realizing intelligent interception of suspicious transactions or further manual review recommendations.

[0026] The anti-fraud system deployment and optimization module deploys the constructed anti-fraud system to the cloud computing platform to improve the availability and scalability of the system and support large-scale data and high-concurrency requests.

[0027] It should be further explained that the data acquisition and preprocessing module uses data cleaning and preprocessing techniques to remove noise and abnormal data, improve data quality, and prepare for subsequent processing.

[0028] It should be further explained that the data collection and preprocessing module uses a variety of data processing tools and technologies, such as stream processing frameworks such as Flink and Kafka, to process and analyze data in real time.

[0029] It should be further explained that the feature engineering and modeling module uses distributed storage and computing frameworks, such as Hadoop and Spark, to achieve feature extraction and modeling of large-scale data.

[0030] It should be further explained that the feature engineering and modeling module converts the feature engineering results into a format acceptable to the model for subsequent machine learning model training and prediction.

[0031] It should be further explained that the model training and evaluation module evaluates and optimizes the model through cross-validation and indicator evaluation to ensure the accuracy and generalization ability of the model.

[0032] It should be further explained that the model training and evaluation module predicts and identifies fraudulent behavior through model explanatory analysis and visualization technology.

[0033] It should be further explained that the real-time monitoring and feedback module introduces a feedback mechanism to dynamically update the model and data to adapt to the changes and evolution of fraudulent behavior.

[0034] It should be further explained that the anti-fraud system deployment and optimization module is used to regularly optimize the system architecture and algorithms to adapt to the ever-changing fraud behavior patterns and ensure the efficiency and accuracy of the system.

[0035] It should be further explained that the anti-fraud system deployment and optimization module is used to introduce automated deployment and monitoring tools, simplify system maintenance and management, and improve system stability and performance.

[0036] The present invention uses big data technology to analyze massive amounts of data and identify potential fraud patterns and rules. Advanced machine learning algorithms, such as deep learning and neural networks, are used to train and model data to identify fraudulent behavior. An intelligent decision-making system is established to automatically make real-time decisions on fraudulent behavior and improve anti-fraud efficiency. Real-time monitoring of transaction and behavior data, timely detection of suspicious activities through early warning mechanisms, and corresponding measures are taken. By analyzing user behavior patterns and characteristics, abnormal behavior is identified to prevent fraud risks. Comprehensive consideration of multi-dimensional data such as user information, transaction data, and device information is performed to conduct risk assessment and analysis to improve the accuracy of anti-fraud. Continuously optimize system algorithms and models, and update model parameters based on real-time data to adapt to changing fraud methods and patterns.

[0037] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. An intelligent anti-fraud system based on large model application, characterized by: By building a flexible large model integration platform, we support the access of various types of large models to meet the needs of extracting fraud behavior features in different dimensions; It includes data collection and preprocessing module, feature engineering and modeling module, model training and evaluation module, real-time monitoring and feedback module, and anti-fraud system deployment and optimization module; The data collection and preprocessing module uses real-time data collection technology to obtain user behavior and transaction data, and supports real-time fraud detection and identification; The feature engineering and modeling module uses machine learning algorithms and statistical analysis tools to perform feature engineering and extract key features of user behavior and transaction data; The model training and evaluation module identifies and evaluates fraud by building machine learning models; Model training and evaluation module: Through continuous training and online learning mechanisms, the large model can update its understanding of fraudulent behavior in real time, identify emerging fraud patterns, and significantly improve the timeliness and adaptability of the anti-fraud system. The real-time monitoring and feedback module uses the real-time monitoring unit to continuously monitor user behavior and transaction data, promptly detect potential fraudulent behavior, and combine the prediction model with real-time data to generate fraud scores and trigger alerts to take corresponding anti-fraud measures; The anti-fraud system deployment and optimization module deploys the constructed anti-fraud system to the cloud computing platform.

2. The intelligent anti-fraud system based on large model application according to claim 1 is characterized in that: The data collection and preprocessing module uses data cleaning and preprocessing techniques to remove noise and abnormal data.

3. The intelligent anti-fraud system based on large model application according to claim 1 is characterized in that: The data acquisition and preprocessing module uses a variety of data processing tools and techniques to process and analyze data in real time.

4. The intelligent anti-fraud system based on large model application according to claim 1 is characterized in that: The feature engineering and modeling module uses distributed storage and computing framework to achieve feature extraction and modeling of large-scale data.

5. The intelligent anti-fraud system based on large model application according to claim 1 is characterized in that: The feature engineering and modeling module converts the feature engineering results into a format acceptable to the model for subsequent machine learning model training and prediction.

6. The intelligent anti-fraud system based on large model application according to claim 1 is characterized in that: The model training and evaluation module evaluates and optimizes the model through cross-validation and indicator evaluation.

7. The intelligent anti-fraud system based on large model application according to claim 1 is characterized in that: The model training and evaluation module predicts and identifies fraudulent behavior through model interpretive analysis and visualization technology.

8. The intelligent anti-fraud system based on large model application according to claim 1 is characterized in that: The real-time monitoring and feedback module introduces a feedback mechanism to dynamically update models and data to adapt to the changes and evolution of fraudulent behavior.

9. The intelligent anti-fraud system based on large model application according to claim 1 is characterized in that: The anti-fraud system deployment and optimization module is used to regularly optimize the system architecture and algorithms.

10. The intelligent anti-fraud system based on large model application according to claim 1 is characterized in that: The anti-fraud system deployment and optimization module is used to introduce automated deployment and monitoring tools.

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