Anti-money laundering mode recognition system based on AI

Abstract

The invention relates to the technical field of artificial intelligence, in particular to an anti-money laundering mode recognition system based on AI, which comprises a data acquisition processing module, a data enhancement module, a training optimization module, an early warning evaluation module, a data learning module and a data generation module. A traditional anti-money laundering system usually depends on a static rule set, and the rules are often lagged in facing continuously evolving money laundering means, however, the system provided by the invention has the characteristic of dynamic adaptability by continuously learning new data and updating and optimizing an identification model. The method can dynamically adapt to a new money laundering mode and a complex transaction behavior, and the flexibility enables the system to maintain an efficient recognition capability when facing an unknown threat.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an anti-money laundering pattern recognition system based on AI. Background Art

[0002] With the rapid development of the global financial market, Anti-Money Laundering (AML) has become a major challenge that regulators and financial institutions in various countries must face. With the popularization of digital transactions and the increasing complexity of global financial markets, emerging technologies are increasingly being used in the financial field, such as blockchain technology, cryptocurrency, e-wallets, mobile payments, etc. While these technologies improve the efficiency of financial transactions, they are also used by criminals to cover up illegal capital flows. Existing anti-money laundering technologies mainly rely on rules and experience set by experts, which are usually based on known money laundering behavior patterns. However, as money laundering methods become increasingly complex, criminals have begun to use new technical means and transaction structures to circumvent traditional rule detection systems.

[0003] Therefore, it is very necessary to propose an AI-based anti-money laundering pattern recognition system, combined with the latest machine learning, deep learning, natural language processing (NLP) and other technologies, to build an efficient, accurate, and dynamically adaptive anti-money laundering detection and early warning system. Summary of the invention

[0004] The purpose of the present invention is to provide an anti-money laundering pattern recognition system based on AI. It is very necessary to build an efficient, accurate, and dynamically adaptive anti-money laundering detection and early warning system by combining the latest machine learning, deep learning, natural language processing (NLP) and other technologies.

[0005] To achieve the above-mentioned purpose, the present invention adopts an AI-based anti-money laundering pattern recognition system, including a data acquisition and processing module, a data enhancement module, a training optimization module, an early warning evaluation module, a data learning module and a data generation module, wherein the data acquisition and processing module is connected to the data enhancement module, the training optimization module is connected to the data enhancement module, the early warning evaluation module is connected to the data acquisition and processing module, the data learning module is connected to the data enhancement module, and the data generation module is connected to the early warning evaluation module;

[0006] The data acquisition and processing module is used for data acquisition, cleaning and standardization processing;

[0007] The data enhancement module is used to extract meaningful features from the original data and improve the robustness and generalization ability of the model;

[0008] The training optimization module is used for anti-money laundering models constructed by various machine learning and deep learning algorithms;

[0009] The early warning assessment module is used for real-time monitoring of financial transactions and money laundering risk assessment;

[0010] The data learning module is used for self-learning and model updating mechanism;

[0011] The data generation module is used to identify suspicious transactions and automatically generate detailed anti-money laundering reports to help financial institutions and regulatory agencies make decisions.

[0012] Among them, the data enhancement module includes a feature extraction unit, a feature prediction unit, an associated grid processing unit, a natural language processing unit and a data enhancement unit, the feature prediction unit is connected to the feature extraction unit, the associated grid processing unit is connected to the feature prediction unit, the natural language processing unit is connected to the feature extraction unit, and the data enhancement unit is connected to the feature extraction unit.

[0013] The feature extraction unit is used to identify hidden fund flows and potential abnormal behaviors between accounts;

[0014] The feature prediction unit is used to analyze the time series characteristics of transaction behavior;

[0015] The association grid processing unit is used to identify complex transaction structures, account relationships, potential money laundering groups and other network characteristics;

[0016] The natural language processing unit is used to capture implicit information in the text to identify potential money laundering activities;

[0017] The data enhancement unit is used to enhance the robustness of the model and improve the recognition ability of the model.

[0018] Among them, the training optimization module includes a machine learning model, a deep learning model, a fusion unit, a graphical unit and a test unit, the machine learning model is connected to the deep learning model, the fusion unit is connected to the deep learning model, the graphical unit is connected to the fusion unit, and the test unit is connected to the deep learning model.

[0019] Wherein, the machine learning model is used to provide a machine learning algorithm and perform modeling;

[0020] The deep learning model is used to process complex nonlinear relationships and massive data;

[0021] The fusion unit is used to improve the overall recognition performance;

[0022] The graphical unit is used to help analysts understand the decision logic of the model;

[0023] The test unit is used to tune the hyperparameters of the model and to cross-validate and evaluate the performance of the model.

[0024] Among them, the early warning assessment module includes a data flow processing unit, a risk analysis unit, a threshold early warning unit and a data backtracking analysis unit. The risk analysis unit is connected to the data flow processing unit, the threshold early warning unit is connected to the risk analysis unit, and the data backtracking analysis unit is connected to the threshold early warning unit.

[0025] Wherein, the data stream processing unit is used to remove redundant information, fill in missing values ​​and process inconsistencies in data formats;

[0026] The risk analysis unit is used to evaluate the risk of the transaction and output the risk value of the transaction;

[0027] The threshold warning unit is used to autonomously adjust the scoring criteria according to the risk changes in different time periods and different market conditions;

[0028] The data backtracking analysis unit is used to identify potential money laundering networks, discover hidden connections behind seemingly normal transactions, and backtrack and analyze transaction records.

[0029] Among them, the data learning module includes a self-learning unit, an incremental update unit and a model evaluation unit, the incremental update unit is connected to the self-learning unit, and the model evaluation unit is connected to the incremental update unit.

[0030] The self-learning unit is used to automatically collect feedback from the manual review team, update the training set and continue learning;

[0031] The incremental update unit is used to quickly adapt to emerging transaction patterns and money laundering methods;

[0032] The model evaluation unit is used to regularly evaluate the performance of the model and decide whether to update the deployed model version based on the evaluation results.

[0033] Wherein, the data generation module includes a report production unit and a decision processing unit, and the decision processing unit is connected to the report production unit;

[0034] The report production unit is used to automatically generate a detailed anti-money laundering report;

[0035] The decision processing unit is used to provide specific decision suggestions and generate compliance documents that meet regulatory requirements.

[0036] The present invention discloses an anti-money laundering pattern recognition system based on AI, wherein the data collection and processing module is responsible for collecting and integrating transaction data, customer information, historical transaction records, account behavior logs, and external database data from multiple data sources, wherein the data sources include but are not limited to: transaction flow and account information in the core system of the bank; transaction records of the third-party payment platform; unstructured data sources, such as customer interaction records, transaction notes, etc. The collected data undergoes preliminary cleaning and standardization processing to remove noise data, complete missing data, and perform data format conversion to ensure data integrity and consistency. The processed data is stored in a high-performance data warehouse or distributed file system for subsequent analysis and processing. The data enhancement module extracts meaningful features from the original data by adopting the most advanced algorithms and technologies, and improves the robustness and generalization ability of the model by means of data enhancement. The training optimization module uses a variety of machine learning and The deep learning algorithm constructs an anti-money laundering model. The early warning assessment module ensures real-time monitoring of all transaction activities through advanced real-time data stream processing technology and dynamic risk assessment mechanism, and provides accurate risk assessment results to help financial institutions make decisions and respond in the first time. The data learning module ensures that the recognition model always remains efficient through self-learning and model update mechanism. After the data generation module identifies suspicious transactions, it will automatically generate a detailed anti-money laundering report to help financial institutions and regulators make decisions. By introducing machine learning and deep learning technologies in this way, it has the characteristics of dynamic adaptability. Traditional anti-money laundering systems usually rely on static rule sets, which often lag behind when facing evolving money laundering methods. The system of the present invention can dynamically adapt to new money laundering patterns and complex transaction behaviors by continuously learning new data, updating and optimizing recognition models. This flexibility enables the system to maintain efficient recognition capabilities when facing unknown threats. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a schematic diagram of the principle of the AI-based anti-money laundering pattern recognition system of the present invention.

[0039] Figure 2 It is a schematic diagram of the principle of the data enhancement module of the present invention.

[0040] Figure 3It is a schematic diagram of the principle of the training optimization module of the present invention.

[0041] Figure 4 It is a schematic diagram of the principle of the early warning evaluation module of the present invention.

[0042] Figure 5 It is a schematic diagram of the principle of the data learning module of the present invention.

[0043] Figure 6 It is a schematic diagram of the principle of the data generation module of the present invention.

[0044] 100-data acquisition and processing module, 200-data enhancement module, 201-feature extraction unit, 202-feature prediction unit, 203-associated grid processing unit, 204-natural language processing unit, 205-data enhancement unit, 300-training optimization module, 301-machine learning model, 302-deep learning model, 303-fusion unit, 304-graphic unit, 305-test unit, 400-early warning assessment module, 401-data stream processing unit, 402-risk analysis unit, 403-threshold warning unit, 404-data backtracking analysis unit, 500-data learning module, 501-self-learning unit, 502-incremental update unit, 503-model evaluation unit, 600-data generation module, 601-report production unit, 602-decision processing unit. DETAILED DESCRIPTION

[0045] See also Figures 1 to 6 , Figure 1 It is a schematic diagram of the principle of the anti-money laundering pattern recognition system based on AI of the present invention. Figure 2 is a schematic diagram of the principle of the data enhancement module of the present invention, Figure 3 is a schematic diagram of the principle of the training optimization module of the present invention, Figure 4 is a schematic diagram of the principle of the early warning assessment module of the present invention, Figure 5 is a schematic diagram of the principle of the data learning module of the present invention, Figure 6 It is a schematic diagram of the principle of the data generation module of the present invention.

[0046] The present invention provides an anti-money laundering pattern recognition system based on AI, comprising a data acquisition and processing module 100, a data enhancement module 200, a training optimization module 300, an early warning evaluation module 400, a data learning module 500 and a data generation module 600, wherein the data enhancement module 200 comprises a feature extraction unit 201, a feature prediction unit 202, an associated grid processing unit 203, a natural language processing unit 204 and a data enhancement unit 205, the training optimization module 300 comprises a machine learning model 301, a deep learning model 302, a fusion unit 303, a graphic unit 304 and a test unit 305, the early warning evaluation module 400 comprises a data stream processing unit 401, a risk analysis unit 402, a threshold early warning unit 403 and a data backtracking analysis unit 404, the data learning module 500 comprises a self-learning unit 501, an incremental update unit 502 and a model evaluation unit 503, and the data generation module 600 comprises a report production unit 601 and a decision processing unit 602.

[0047] The data acquisition and processing module 100 is connected to the data enhancement module 200, the training optimization module 300 is connected to the data enhancement module 200, the early warning evaluation module 400 is connected to the data acquisition and processing module 100, the data learning module 500 is connected to the data enhancement module 200, and the data generation module 600 is connected to the early warning evaluation module 400;

[0048] The data acquisition and processing module 100 is used for data acquisition, cleaning and standardization processing;

[0049] The data enhancement module 200 is used to extract meaningful features from the original data and improve the robustness and generalization ability of the model;

[0050] The training optimization module 300 is used for anti-money laundering models constructed by various machine learning and deep learning algorithms;

[0051] The early warning assessment module 400 is used for real-time monitoring of financial transactions and money laundering risk assessment;

[0052] The data learning module 500 is used for self-learning and model updating mechanism;

[0053] The data generation module 600 is used to identify suspicious transactions and automatically generate detailed anti-money laundering reports to help financial institutions and regulatory agencies make decisions.

[0054] In this embodiment, the data collection and processing module 100 is responsible for collecting and integrating transaction data, customer information, historical transaction records, account behavior logs, and external database data from multiple data sources. The data sources include but are not limited to: transaction flows and account information in the bank's core system; transaction records of third-party payment platforms; unstructured data sources, such as customer interaction records, transaction notes, etc. The collected data is initially cleaned and standardized to remove noise data, complete missing data, and perform data format conversion to ensure data integrity and consistency. The processed data is stored in a high-performance data warehouse or distributed file system for subsequent analysis and processing. The data enhancement module 200 extracts meaningful features from the original data by adopting the most advanced algorithms and technologies, and improves the robustness and generalization ability of the model by means of data enhancement. The training optimization module 300 constructs a training model based on the aforementioned extracted feature data using a variety of machine learning and deep learning algorithms. An anti-money laundering model is built. The early warning assessment module 400 ensures real-time monitoring of all transaction activities through advanced real-time data stream processing technology and dynamic risk assessment mechanism, and provides accurate risk assessment results to help financial institutions make decisions and respond in the first time. The data learning module 500 ensures that the recognition model always remains efficient through self-learning and model update mechanism. After the data generation module 600 identifies suspicious transactions, it will automatically generate a detailed anti-money laundering report to help financial institutions and regulators make decisions. By introducing machine learning and deep learning technologies in this way, it has the characteristics of dynamic adaptability. Traditional anti-money laundering systems usually rely on static rule sets, which often lag behind in the face of evolving money laundering methods. The system of the present invention can dynamically adapt to new money laundering patterns and complex transaction behaviors by continuously learning new data, updating and optimizing recognition models. This flexibility enables the system to maintain efficient recognition capabilities in the face of unknown threats.

[0055] Furthermore, the data enhancement module 200 includes a feature extraction unit 201, a feature prediction unit 202, an associated grid processing unit 203, a natural language processing unit 204 and a data enhancement unit 205, the feature prediction unit 202 is connected to the feature extraction unit 201, the associated grid processing unit 203 is connected to the feature prediction unit 202, the natural language processing unit 204 is connected to the feature extraction unit 201, and the data enhancement unit 205 is connected to the feature extraction unit 201.

[0056] Furthermore, the feature extraction unit 201 is used to identify hidden fund flows and potential abnormal behaviors between accounts;

[0057] The feature prediction unit 202 is used to analyze the time series characteristics of transaction behavior;

[0058] The association grid processing unit 203 is used to identify complex transaction structures, account relationships, potential money laundering groups and other network characteristics;

[0059] The natural language processing unit 204 is used to capture implicit information in the text to identify potential money laundering activities;

[0060] The data enhancement unit 205 is used to enhance the robustness of the model and improve the recognition ability of the model.

[0061] In this embodiment, the feature extraction unit 201: Transformer-based Embedding: Use a model based on the Transformer architecture (such as BERT, RoBERTa) to encode transaction behavior, and convert information such as transaction frequency, amount distribution, and transaction objects into high-dimensional embedding vectors. These embedding vectors can capture complex patterns in transaction behaviors and help the model identify abnormal transactions more accurately; Graph Neural Networks (GNNs): Use graph neural networks (GNNs), such as GraphSAGE or GAT, to model transaction behaviors between accounts and extract structured features of transaction networks. These features can identify hidden capital flows and potential abnormal behaviors between accounts, such as key nodes and transaction paths in money laundering networks.

[0062] The feature prediction unit 202: Temporal Convolutional Networks (TCNs): Temporal convolutional networks (TCNs) are used to analyze the time series characteristics of transaction behaviors. This model can handle long-term dependent transaction patterns, such as seasonal capital flows and sudden large-value transaction changes, and helps identify long-term latent money laundering activities by capturing complex patterns in time series. LSTM with Attention Mechanism: Serial analysis of transaction data is performed using a long short-term memory network (LSTM) with an attention mechanism. The attention mechanism can dynamically focus on key moments in a transaction sequence, allowing the model to better predict possible suspicious transaction patterns in the future.

[0063] The associated grid processing unit 203: Heterogeneous Graph Attention Network (HAN): Constructs a heterogeneous graph attention network to process different types of nodes and edges, such as accounts, transactions, locations, etc. This network can identify complex transaction structures and account relationships, and discover money laundering behaviors hidden in large-scale transaction networks by analyzing the attention weights of nodes. DeepWalk&Node2Vec: Use random walk techniques (such as DeepWalk and Node2Vec) to embed account association networks and generate high-dimensional node vectors. These vectors can capture the relative position and importance of nodes (accounts) in the network, which helps to identify potential money laundering groups and their network characteristics.

[0064] The natural language processing unit 204: BERT-based Text Classification: The BERT model is used to perform text classification on transaction notes and customer interaction records, and semantic features related to money laundering are extracted from them. The BERT model performs well in processing contextual information and polysemous words, and can accurately capture implicit information in the text, such as suggestive language suspected of illegal transactions. Topic Modeling with Latent Dirichlet Allocation (LDA): Topic models (such as LDA) are used to extract topics from large-scale text data (such as customer service records) to identify core topics related to money laundering. These topic information can provide additional context to help identify potential money laundering activities.

[0065] The data enhancement unit 205Generative Adversarial Networks (GANs): Generate synthetic data similar to real transaction data using generative adversarial networks (GANs), such as Wasserstein GAN (WGAN) or Conditional GAN ​​(CGAN) to expand the training set. The data generated by GAN can effectively enhance the robustness of the model, especially when dealing with rare money laundering samples, it can significantly improve the recognition ability of the model. SMOTE & ADASYN for Imbalanced Data: When dealing with unbalanced data sets, SMOTE (Synthetic Minority Oversampling Technology) and ADASYN (Adaptive Synthetic Sampling Method) are used to generate minority class samples to balance the ratio of normal transactions to suspicious transactions. These technologies enhance the model's recognition ability for minority classes by generating synthetic samples close to the real distribution.

[0066] Furthermore, the training optimization module 300 includes a machine learning model 301, a deep learning model 302, a fusion unit 303, a diagram unit 304 and a test unit 305, the machine learning model 301 is connected to the deep learning model 302, the fusion unit 303 is connected to the deep learning model 302, the diagram unit 304 is connected to the fusion unit 303, and the test unit 305 is connected to the deep learning model 302.

[0067] Furthermore, the machine learning model 301 is used to provide a machine learning algorithm and perform modeling;

[0068] The deep learning model 302 is used to process complex nonlinear relationships and massive data;

[0069] The fusion unit 303 is used to improve the overall recognition performance;

[0070] The diagram unit 304 is used to help analysts understand the decision logic of the model;

[0071] The test unit 305 is used to tune the hyperparameters of the model and to cross-validate and evaluate the performance of the model.

[0072] In this embodiment, the machine learning model 301 first models the data using a variety of traditional machine learning algorithms, such as random forest, support vector machine (SVM), XGBoost, etc. These models train historical data through supervised learning to identify potential money laundering behavior patterns.

[0073] In order to process complex nonlinear relationships and massive data, the system introduces deep learning models such as convolutional neural networks (CNN) and long short-term memory networks (LSTM) into the deep learning model 302. CNN is mainly used for image and sequence data analysis, and can be used to process transaction graph data in the financial field; LSTM is used to process time series data and can effectively capture the time dependency of transaction behavior.

[0074] The fusion unit 303 combines the prediction results of multiple models through model fusion technology (such as stacking, weighted average) to improve the overall recognition performance. Ensemble learning methods such as bagging and boosting are also used to build powerful anti-money laundering models.

[0075] In order to meet the requirements of financial regulators for transparency of AI models, the graphical unit 304 integrates model explanation technologies such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) to help analysts understand the decision logic of the model and provide reasonable explanations.

[0076] During the model training process, the test unit 305 uses grid search, random search, Bayesian optimization and other technologies to tune the model's hyperparameters, evaluate the model's performance through cross-validation, and select the optimal model for actual deployment.

[0077] Furthermore, the early warning assessment module 400 includes a data flow processing unit 401, a risk analysis unit 402, a threshold early warning unit 403 and a data backtracking analysis unit 404, the risk analysis unit 402 is connected to the data flow processing unit 401, the threshold early warning unit 403 is connected to the risk analysis unit 402, and the data backtracking analysis unit 404 is connected to the threshold early warning unit 403.

[0078] Furthermore, the data stream processing unit 401 is used to remove redundant information, complete missing values ​​and process inconsistencies in data formats;

[0079] The risk analysis unit 402 is used to evaluate the risk of the transaction and output the risk value of the transaction;

[0080] The threshold warning unit 403 is used to autonomously adjust the scoring criteria according to the risk changes in different time periods and different market conditions;

[0081] The data backtracking analysis unit 404 is used to identify potential money laundering networks, discover hidden connections behind seemingly normal transactions, and backtrack and analyze transaction records.

[0082] In this embodiment, the design and implementation of the stream processing architecture of the data stream processing unit 401: This system adopts a distributed stream processing architecture, such as Apache Kafka, Apache Flink or Apache Storm, to achieve real-time reception and processing of massive transaction data. These tools can process high-throughput transaction data streams and support low-latency real-time analysis. The system accesses the transaction link of the financial institution and instantly captures the detailed information of each transaction through the data pipeline, including the transaction amount, timestamp, account information, transaction type, etc. 2. The stream processing architecture ensures that the data is processed within milliseconds to avoid risk management failure caused by delays. Real-time data cleaning and preprocessing After entering the real-time monitoring system, the data first passes through the data cleaning and preprocessing module. This module is responsible for removing redundant information, filling missing values, processing inconsistencies in data formats, and performing basic legitimacy checks. The cleaned data is standardized and converted into a format that can be directly used by the system to facilitate subsequent feature extraction and analysis. To ensure the robustness of the system, the data preprocessing module is also responsible for processing sudden data flow peaks, and by dynamically adjusting the priority and resource allocation of the processing queue, the system can be ensured to run stably under any circumstances.

[0083] The risk analysis unit 402 is mainly composed of 1. AI model-driven risk scoring: the cleaned data is transmitted to the AI ​​model module in real time, which performs risk scoring on each transaction based on pre-trained deep learning and machine learning models. The models used by the system include random forest, deep neural network (DNN) and support vector machine (SVM), etc., which can comprehensively consider the multi-dimensional characteristics of transactions, such as transaction frequency, amount fluctuation, account history behavior, etc., and accurately assess the risk of each transaction. When processing data, the model will dynamically consider the weights of different features, and these weights are adjusted based on the latest financial crime trends to ensure that the risk score is highly consistent with the actual situation. 2. Interpretation and output of risk scores: The risk scores generated by the system will be accompanied by model explanation information, such as which features have the greatest impact on the score and which historical transaction patterns are similar to the current transaction. This information is generated through LIME (Local Interpretable Model-agnosticExplanations) or SHAP (SHapleyAdditive exPlanations) technology to help the risk control team of financial institutions quickly understand the potential risks of each transaction. The output risk score is quantified on a scale of 0 to 100, with higher values ​​indicating a greater likelihood of money laundering in the transaction.

[0084] The threshold warning unit 403 is divided into 1. Threshold dynamic adjustment mechanism: the system no longer uses static risk scoring thresholds, but dynamically adjusts the thresholds according to the distribution of real-time transaction data and the risk tolerance of financial institutions. This adjustment mechanism relies on an adaptive algorithm and can autonomously adjust the scoring standards according to the risk changes in different time periods and different market conditions. Financial institutions can set basic threshold adjustment strategies based on their own operating characteristics, such as raising the warning line during market fluctuations or relaxing some standards during stable periods. Based on these strategies, the system will automatically adjust the risk assessment standards of transactions to ensure that potential risks can be effectively identified and responded to under any circumstances. 2. Multi-level warning mechanism: the system divides the risk scores of transactions into three levels: low, medium, and high, and triggers different levels of warning measures according to the scores: Low-risk transactions: routine transaction processing, the system automatically archives, and no manual intervention is required. Medium-risk transactions: recorded and included in continuous monitoring, the system may re-evaluate its risks based on changes in subsequent transactions. High-risk transactions: trigger system alarms immediately and automatically push relevant information to the manual review team. The system also supports automatic further measures, such as freezing accounts, restricting transactions, etc., until the manual review is completed.

[0085] The data backtracking analysis unit 404 is divided into 1. Tracking of suspicious transactions and network association analysis: the system not only conducts risk assessment on a single transaction, but also tracks the entire transaction link through multi-dimensional association analysis. Using graph analysis technology (such as Graph Neural Networks, GNNs), the system can identify potential money laundering networks and discover hidden connections behind seemingly normal transactions. By constructing a transaction network graph, the system analyzes the flow of funds between accounts, nodes of frequent transactions, and abnormal transaction paths. Graph analysis can reveal complex money laundering behaviors such as cross-border transfers and abnormal fund flows between accounts with the same name. 2. Historical data backtracking and trend analysis: after a high-risk transaction is marked, the system will automatically perform a backtracking analysis to find the historical transaction records of the relevant accounts and their behavior patterns in the entire transaction network. This backtracking analysis can help discover long-term secret money laundering activities and include them in the scope of continuous monitoring. The system also provides a trend analysis function to help users understand the evolution of an account or a type of transaction behavior. By comparing historical data, the system can predict possible money laundering activities in the future and issue early warnings.

[0086] The data backtracking analysis unit 404 uses suspicious transaction tracking and network association analysis: the system not only conducts risk assessment on a single transaction, but also tracks the entire transaction link through multi-dimensional association analysis. Using graph analysis technology (such as Graph Neural Networks, GNNs), the system can identify potential money laundering networks and discover hidden connections behind seemingly normal transactions. By constructing a transaction network graph, the system analyzes the flow of funds between accounts, nodes of frequent transactions, and abnormal transaction paths. Graph analysis can reveal complex money laundering behaviors such as cross-border transfers and abnormal fund flows between accounts with the same name. Historical data backtracking and trend analysis: After high-risk transactions are marked, the system will automatically perform backtracking analysis to find the historical transaction records of related accounts and their behavior patterns in the entire transaction network. This backtracking analysis can help discover long-term secret money laundering activities and include them in the scope of continuous monitoring. The system also provides a trend analysis function to help users understand the evolution of an account or a type of transaction behavior. By comparing historical data, the system can predict possible money laundering activities in the future and issue early warnings.

[0087] Furthermore, the data learning module 500 includes a self-learning unit 501 , an incremental update unit 502 and a model evaluation unit 503 , wherein the incremental update unit 502 is connected to the self-learning unit 501 , and the model evaluation unit 503 is connected to the incremental update unit 502 .

[0088] Furthermore, the self-learning unit 501 is used to automatically collect feedback from the manual review team, update the training set and continue learning;

[0089] The incremental update unit 502 is used to quickly adapt to emerging transaction patterns and money laundering methods;

[0090] The model evaluation unit 503 is used to regularly evaluate the performance of the model and decide whether to update the deployed model version according to the evaluation result.

[0091] In this embodiment, the self-learning unit 501 automatically collects feedback from the manual review team and uses the feedback as new data input to update the training set for continuous learning. For example, when the review team confirms that certain transactions are money laundering, the system will mark these transactions as positive samples to enhance the model's ability to identify similar behaviors.

[0092] The incremental update unit 502 supports online learning and optimizes the model in real time through incremental update technology. This method enables the model to quickly adapt to emerging transaction patterns and money laundering methods without waiting for a large amount of new data to accumulate before retraining the model.

[0093] The model evaluation unit 503 system regularly evaluates the performance of the model and decides whether to update the deployed model version based on the evaluation results. By introducing a version control mechanism, the system ensures the traceability and stability of each model update.

[0094] Furthermore, the data generation module 600 includes a report generation unit 601 and a decision processing unit 602 , and the decision processing unit 602 is connected to the report generation unit 601 .

[0095] Further, the report production unit 601 is used to automatically generate a detailed anti-money laundering report;

[0096] The decision processing unit 602 is used to provide specific decision suggestions and generate compliance documents that meet regulatory requirements.

[0097] In this embodiment, the report generation unit 601 includes detailed information of each suspicious transaction, risk score, transaction history of related accounts, and description of potential money laundering patterns. Visual analysis: Provides rich charts and network relationship diagrams to intuitively display the correlation between transaction behaviors and accounts, helping decision makers to quickly understand and judge suspicious behaviors.

[0098] The decision processing unit 602 provides specific decision suggestions based on the system analysis results, such as further account freezing, in-depth investigation suggestions, etc. Compliance documents: Generate compliance documents that meet regulatory requirements to help financial institutions meet the requirements of anti-money laundering regulations and reduce compliance risks.

[0099] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. An AI-based anti-money laundering pattern recognition system, characterized in that: It includes a data acquisition and processing module, a data enhancement module, a training optimization module, a warning evaluation module, a data learning module and a data generation module, wherein the data acquisition and processing module is connected to the data enhancement module, the training optimization module is connected to the data enhancement module, the warning evaluation module is connected to the data acquisition and processing module, the data learning module is connected to the data enhancement module, and the data generation module is connected to the warning evaluation module; The data acquisition and processing module is used for data acquisition, cleaning and standardization processing; The data enhancement module is used to extract meaningful features from the original data and improve the robustness and generalization ability of the model; The training optimization module is used for anti-money laundering models constructed by various machine learning and deep learning algorithms; The early warning assessment module is used for real-time monitoring of financial transactions and money laundering risk assessment; The data learning module is used for self-learning and model updating mechanism; The data generation module is used to identify suspicious transactions and automatically generate detailed anti-money laundering reports to help financial institutions and regulatory agencies make decisions.

2. The AI-based anti-money laundering pattern recognition system according to claim 1, characterized in that: The data enhancement module includes a feature extraction unit, a feature prediction unit, an associated grid processing unit, a natural language processing unit and a data enhancement unit, the feature prediction unit is connected to the feature extraction unit, the associated grid processing unit is connected to the feature prediction unit, the natural language processing unit is connected to the feature extraction unit, and the data enhancement unit is connected to the feature extraction unit.

3. The AI-based anti-money laundering pattern recognition system according to claim 2, characterized in that: The feature extraction unit is used to identify hidden fund flows and potential abnormal behaviors between accounts; The feature prediction unit is used to analyze the time series characteristics of transaction behavior; The association grid processing unit is used to identify complex transaction structures, account relationships, potential money laundering groups and other network characteristics; The natural language processing unit is used to capture implicit information in the text to identify potential money laundering activities; The data enhancement unit is used to enhance the robustness of the model and improve the recognition ability of the model.

4. The AI-based anti-money laundering pattern recognition system according to claim 1, characterized in that: The training optimization module includes a machine learning model, a deep learning model, a fusion unit, a graphical unit and a test unit. The machine learning model is connected to the deep learning model, the fusion unit is connected to the deep learning model, the graphical unit is connected to the fusion unit, and the test unit is connected to the deep learning model.

5. The AI-based anti-money laundering pattern recognition system according to claim 4, characterized in that: The machine learning model is used to provide a machine learning algorithm and perform modeling; The deep learning model is used to process complex nonlinear relationships and massive data; The fusion unit is used to improve the overall recognition performance; The graphical unit is used to help analysts understand the decision logic of the model; The test unit is used to tune the hyperparameters of the model and to cross-validate and evaluate the performance of the model.

6. The AI-based anti-money laundering pattern recognition system according to claim 1, characterized in that: The early warning assessment module includes a data flow processing unit, a risk analysis unit, a threshold early warning unit and a data backtracking analysis unit. The risk analysis unit is connected to the data flow processing unit, the threshold early warning unit is connected to the risk analysis unit, and the data backtracking analysis unit is connected to the threshold early warning unit.

7. The AI-based anti-money laundering pattern recognition system according to claim 6, characterized in that: The data stream processing unit is used to remove redundant information, fill in missing values ​​and process inconsistencies in data formats; The risk analysis unit is used to evaluate the risk of the transaction and output the risk value of the transaction; The threshold warning unit is used to autonomously adjust the scoring criteria according to the risk changes in different time periods and different market conditions; The data backtracking analysis unit is used to identify potential money laundering networks, discover hidden connections behind seemingly normal transactions, and backtrack and analyze transaction records.

8. The AI-based anti-money laundering pattern recognition system according to claim 1, characterized in that: The data learning module includes a self-learning unit, an incremental update unit and a model evaluation unit, wherein the incremental update unit is connected to the self-learning unit, and the model evaluation unit is connected to the incremental update unit.

9. The AI-based anti-money laundering pattern recognition system according to claim 8, characterized in that: The self-learning unit is used to automatically collect feedback from the manual review team, update the training set and continue learning; The incremental update unit is used to quickly adapt to emerging transaction patterns and money laundering methods; The model evaluation unit is used to regularly evaluate the performance of the model and decide whether to update the deployed model version based on the evaluation results.

10. The AI-based anti-money laundering pattern recognition system according to claim 1, characterized in that: The data generation module includes a report production unit and a decision processing unit, and the decision processing unit is connected to the report production unit; The report production unit is used to automatically generate a detailed anti-money laundering report; The decision processing unit is used to provide specific decision suggestions and generate compliance documents that meet regulatory requirements.

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