├── docs ├── index.md ├── .DS_Store └── index.html ├── src ├── main │ ├── resources │ │ ├── application.yml │ │ ├── .DS_Store │ │ └── mapper │ │ │ ├── .DS_Store │ │ │ └── CustomerMapper.xml │ ├── .DS_Store │ └── java │ │ ├── .DS_Store │ │ ├── util │ │ ├── .DS_Store │ │ └── IOUtil.java │ │ ├── config │ │ ├── .DS_Store │ │ └── TokenConfig.java │ │ ├── domain │ │ ├── .DS_Store │ │ ├── RiskAssessment.java │ │ └── Customer.java │ │ ├── mapper │ │ ├── .DS_Store │ │ └── CustomerMapper.java │ │ ├── service │ │ ├── .DS_Store │ │ ├── impl │ │ │ ├── .DS_Store │ │ │ └── CustomerServiceImpl.java │ │ └── ICustomerService.java │ │ └── controller │ │ ├── .DS_Store │ │ └── CustomerController.java ├── .DS_Store └── test │ ├── .DS_Store │ └── unit │ ├── .DS_Store │ └── CustomerServiceTest.java ├── .DS_Store ├── Intelligent Anti-Money Laundering Behavior Monitoring Platform ├── .DS_Store ├── Intelligent Anti-Money Laundering Behavior Monitoring Platform Code.pdf └── Intelligent Anti-Money Laundering Behavior Monitoring Platform Software Design Plan.pdf ├── Anti-Money Laundering Early Warning System Based on Deep Learning ├── .DS_Store ├── Anti-Money Laundering Early Warning_detailed.pdf ├── Anti-Money Laundering Early Warning System Based on Deep Learning Code.pdf ├── Anti-Money Laundering Early Warning System Based on Deep Learning Software Design Plan.pdf └── README.md ├── Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software ├── .DS_Store ├── Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software Code.pdf └── Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software Design Plan.pdf ├── Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform ├── .DS_Store ├── Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform Code.pdf └── Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform Software Design Plan.pdf └── README.md 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-------------------------------------------------------------------------------- /src/main/java/domain/RiskAssessment.java: -------------------------------------------------------------------------------- 1 | package com.chengyu.amlewsbdl.domain; 2 | 3 | import lombok.Data; 4 | import java.util.Date; 5 | 6 | @Data 7 | public class RiskAssessment { 8 | private Integer assessmentId; 9 | private String factors; 10 | private String riskLevel; 11 | private Integer riskScore; 12 | private Date assessmentDate; 13 | } -------------------------------------------------------------------------------- /Anti-Money Laundering Early Warning System Based on Deep Learning/Anti-Money Laundering Early Warning System Based on Deep Learning Software Design Plan.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jeremiahcheng1206/AML-Monitoring-Engine/HEAD/Anti-Money Laundering Early Warning System Based on Deep Learning/Anti-Money Laundering Early Warning System Based on Deep Learning Software Design Plan.pdf -------------------------------------------------------------------------------- /src/main/java/domain/Customer.java: -------------------------------------------------------------------------------- 1 | package com.chengyu.amlewsbdl.domain; 2 | 3 | import lombok.Data; 4 | import java.math.BigDecimal; 5 | 6 | @Data 7 | public class Customer { 8 | private Integer customerId; 9 | private String name; 10 | private String address; 11 | private String email; 12 | private BigDecimal accountBalance; 13 | private String phoneNumber; 14 | } -------------------------------------------------------------------------------- /src/main/java/mapper/CustomerMapper.java: -------------------------------------------------------------------------------- 1 | package com.chengyu.amlewsbdl.mapper; 2 | 3 | import com.chengyu.amlewsbdl.domain.Customer; 4 | 5 | import java.util.List; 6 | 7 | public interface CustomerMapper { 8 | List selectAllCustomers(); 9 | void insertCustomer(Customer customer); 10 | void updateCustomer(Customer customer); 11 | void deleteCustomer(int id); 12 | } -------------------------------------------------------------------------------- /Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software/Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software Code.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jeremiahcheng1206/AML-Monitoring-Engine/HEAD/Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software/Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software Code.pdf -------------------------------------------------------------------------------- /src/main/java/service/ICustomerService.java: -------------------------------------------------------------------------------- 1 | package com.chengyu.amlewsbdl.service; 2 | 3 | import com.chengyu.amlewsbdl.domain.Customer; 4 | 5 | import java.util.List; 6 | 7 | public interface ICustomerService { 8 | List getAllCustomers(); 9 | void saveCustomer(Customer customer); 10 | void updateCustomer(Customer customer); 11 | void deleteCustomerById(int id); 12 | } -------------------------------------------------------------------------------- /Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software/Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software Design Plan.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jeremiahcheng1206/AML-Monitoring-Engine/HEAD/Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software/Machine Learning-Driven Anti-Money Laundering Investigation Assistant Software Design Plan.pdf -------------------------------------------------------------------------------- /src/main/java/util/IOUtil.java: -------------------------------------------------------------------------------- 1 | package com.chengyu.amlewsbdl.util; 2 | 3 | import java.io.*; 4 | 5 | public class IOUtil { 6 | public static void close(Closeable closeable) { 7 | if (closeable != null) { 8 | try { 9 | closeable.close(); 10 | } catch (IOException e) { 11 | e.printStackTrace(); 12 | } 13 | } 14 | } 15 | } -------------------------------------------------------------------------------- /Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform/Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform Code.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jeremiahcheng1206/AML-Monitoring-Engine/HEAD/Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform/Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform Code.pdf -------------------------------------------------------------------------------- /Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform/Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform Software Design Plan.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jeremiahcheng1206/AML-Monitoring-Engine/HEAD/Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform/Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform Software Design Plan.pdf -------------------------------------------------------------------------------- /src/test/unit/CustomerServiceTest.java: -------------------------------------------------------------------------------- 1 | package com.chengyu.amlewsbdl.unit; 2 | 3 | import com.chengyu.amlewsbdl.domain.Customer; 4 | import com.chengyu.amlewsbdl.service.ICustomerService; 5 | import org.junit.jupiter.api.Test; 6 | 7 | import java.math.BigDecimal; 8 | 9 | import static org.mockito.Mockito.*; 10 | 11 | public class CustomerServiceTest { 12 | @Test 13 | public void testSaveCustomer() { 14 | ICustomerService customerService = mock(ICustomerService.class); 15 | Customer customer = new Customer(); 16 | customer.setName("John Doe"); 17 | customer.setAccountBalance(new BigDecimal("1000.00")); 18 | customerService.saveCustomer(customer); 19 | 20 | verify(customerService).saveCustomer(customer); 21 | } 22 | } -------------------------------------------------------------------------------- /src/main/resources/mapper/CustomerMapper.xml: -------------------------------------------------------------------------------- 1 | 2 | 5 | 6 | 7 | INSERT INTO customers (name, address, email, account_balance, phone_number) 8 | VALUES (#{name}, #{address}, #{email}, #{accountBalance}, #{phoneNumber}); 9 | 10 | 11 | 12 | UPDATE customers 13 | SET name=#{name}, address=#{address}, email=#{email}, account_balance=#{accountBalance}, phone_number=#{phoneNumber} 14 | WHERE customer_id=#{customerId}; 15 | 16 | 17 | 18 | DELETE FROM customers WHERE customer_id=#{customerId}; 19 | 20 | 21 | -------------------------------------------------------------------------------- /src/main/java/config/TokenConfig.java: -------------------------------------------------------------------------------- 1 | package com.chengyu.amlewsbdl.config; 2 | 3 | import org.springframework.context.annotation.Bean; 4 | import org.springframework.context.annotation.Configuration; 5 | import org.springframework.security.oauth2.provider.token.TokenStore; 6 | import org.springframework.security.oauth2.provider.token.store.JwtAccessTokenConverter; 7 | import org.springframework.security.oauth2.provider.token.store.JwtTokenStore; 8 | 9 | @Configuration 10 | public class TokenConfig { 11 | private final String SIGNING_KEY = "mq123"; 12 | 13 | @Bean 14 | public TokenStore tokenStore() { 15 | return new JwtTokenStore(accessTokenConverter()); 16 | } 17 | 18 | @Bean 19 | public JwtAccessTokenConverter accessTokenConverter() { 20 | JwtAccessTokenConverter converter = new JwtAccessTokenConverter(); 21 | converter.setSigningKey(SIGNING_KEY); 22 | return converter; 23 | } 24 | } -------------------------------------------------------------------------------- /src/main/java/controller/CustomerController.java: -------------------------------------------------------------------------------- 1 | package com.chengyu.amlewsbdl.controller; 2 | 3 | import org.springframework.beans.factory.annotation.Autowired; 4 | import org.springframework.web.bind.annotation.*; 5 | 6 | import java.util.List; 7 | 8 | @RestController 9 | @RequestMapping("/customer") 10 | public class CustomerController { 11 | @Autowired 12 | private ICustomerService customerService; 13 | 14 | @GetMapping("/list") 15 | public List listCustomers() { 16 | return customerService.getAllCustomers(); 17 | } 18 | 19 | @PostMapping 20 | public void addCustomer(@RequestBody Customer customer) { 21 | customerService.saveCustomer(customer); 22 | } 23 | 24 | @PutMapping 25 | public void updateCustomer(@RequestBody Customer customer) { 26 | customerService.updateCustomer(customer); 27 | } 28 | 29 | @DeleteMapping("/{id}") 30 | public void deleteCustomer(@PathVariable int id) { 31 | customerService.deleteCustomerById(id); 32 | } 33 | } 34 | -------------------------------------------------------------------------------- /src/main/java/service/impl/CustomerServiceImpl.java: -------------------------------------------------------------------------------- 1 | package com.chengyu.amlewsbdl.service.impl; 2 | 3 | import com.chengyu.amlewsbdl.domain.Customer; 4 | import com.chengyu.amlewsbdl.mapper.CustomerMapper; 5 | import com.chengyu.amlewsbdl.service.ICustomerService; 6 | import org.springframework.beans.factory.annotation.Autowired; 7 | import org.springframework.stereotype.Service; 8 | 9 | import java.util.List; 10 | 11 | @Service 12 | public class CustomerServiceImpl implements ICustomerService { 13 | @Autowired 14 | private CustomerMapper customerMapper; 15 | 16 | @Override 17 | public List getAllCustomers() { 18 | return customerMapper.selectAllCustomers(); 19 | } 20 | 21 | @Override 22 | public void saveCustomer(Customer customer) { 23 | customerMapper.insertCustomer(customer); 24 | } 25 | 26 | @Override 27 | public void updateCustomer(Customer customer) { 28 | customerMapper.updateCustomer(customer); 29 | } 30 | 31 | @Override 32 | public void deleteCustomerById(int id) { 33 | customerMapper.deleteCustomer(id); 34 | } 35 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # AML-Monitoring-Engine 2 | 3 | ## General Information 4 | - **Full Software Name**: Intelligent Financial Transaction Anti-Money Laundering Identification Tool Platform 5 | - **Version**: V1.0 6 | 7 | ## Development Environment 8 | - **Development Hardware Environment**: 9 | - CPU: Intel(R) Core(TM) i5-4200H, 2.8GHz 10 | - RAM: 8GB 11 | - Hard Drive: 1TB 12 | - **Development Operating System**: Windows 11 Enterprise Edition 13 | - **Development Tools**: IntelliJ IDEA V2023 14 | - **Programming Language**: Java 15 | - **Source Code Size**: 25,466 lines 16 | 17 | ## Runtime Environment 18 | - **Hardware Requirements**: 19 | - Quad-core CPU 20 | - 8GB RAM 21 | - Integrated Graphics 22 | - Gigabit Network 23 | - SSD with 500GB or more 24 | - **Operating System**: Anolis OS 25 | - **Supporting Software**: 26 | - JDK 9 or above 27 | - MySQL 8.0 28 | - Apache Tomcat 29 | - Redis 30 | 31 | ## Purpose of Development 32 | The primary goal is to enhance the efficiency of anti-money laundering (AML) efforts in financial transactions, reduce risks, and ensure the security of funds. 33 | 34 | ## Target Domain/Industry 35 | - **Domain**: Financial Technology (FinTech) 36 | 37 | ## Key Features 38 | 1. **Real-Time Monitoring**: 39 | Utilizes big data, machine learning, and artificial intelligence to analyze large volumes of financial transaction data in real-time. 40 | 2. **Suspicious Pattern Detection**: 41 | Automatically identifies unusual patterns, such as large fund transfers or frequent cross-border transactions. 42 | 3. **Model Optimization**: 43 | Continuously improves detection accuracy using deep learning algorithms. 44 | 4. **Risk Alert Mechanism**: 45 | Initiates investigation procedures immediately upon detecting potential money laundering activities. 46 | 5. **Data Integration and Sharing**: 47 | Enables cross-bank and cross-border data sharing to strengthen institutional collaboration. 48 | 6. **Compliance Support**: 49 | Provides compliance reports and audit tools to meet international AML standards. 50 | 7. **Efficiency Improvement**: 51 | Enhances precision and effectiveness of AML processes to prevent financial crimes and ensure system stability. 52 | 53 | ## Technical Characteristics 54 | - **Type**: Big Data Software 55 | - **Key Features**: 56 | - Employs AI deep learning and big data analysis for real-time monitoring of financial transactions. 57 | - Supports anomaly detection and risk level evaluation. 58 | - Facilitates multi-dimensional data integration and automated workflows to improve risk control efficiency. 59 | - Offers visualized reports for clear understanding of risk scenarios, enabling precise and efficient risk management. 60 | ## Websites 61 | - https://jeremiahcheng1206.github.io/AML-Monitoring-Engine/ 62 | ## Software Classification 63 | - **Category**: Application Software 64 | -------------------------------------------------------------------------------- /docs/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | AML Monitoring Engine 7 | 103 | 104 | 105 | 106 |
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AML Monitoring Engine

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Welcome to the official documentation site for the AML Monitoring Engine

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Overview

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The AML Monitoring Engine is a cutting-edge system that leverages deep learning to detect and combat suspicious financial transactions. It enhances the accuracy and efficiency of anti-money laundering (AML) processes, providing robust tools for financial institutions to manage risks.

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System Architecture

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The system comprises six main components: Data Collection, Model Training, Real-Time Monitoring, Risk Assessment, Compliance, and Integration.

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Each module works seamlessly to provide a comprehensive AML monitoring solution.

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Modules

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Data Collection: Processes raw data from multiple financial systems.

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Feature Extraction: Identifies key patterns for training models.

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Model Training: Builds predictive deep learning models.

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Early Warning: Detects anomalies and provides decision support.

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Technologies

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We utilize advanced machine learning techniques, including:

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  • Convolutional Neural Networks (CNNs)
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  • Recurrent Neural Networks (RNNs)
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  • Deep Neural Networks (DNNs)
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Testing

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Our rigorous testing plan includes:

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  • Unit Testing
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  • Integration Testing
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  • System Functionality Testing
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  • Performance and Security Testing
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Maintenance

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Regular updates ensure the system remains compliant with regulations and optimizes performance over time.

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168 | 169 | 170 | 173 | 174 | 175 | 176 | -------------------------------------------------------------------------------- /Anti-Money Laundering Early Warning System Based on Deep Learning/README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning-Based Anti-Money Laundering Early Warning System V1.0 2 | 3 | ## Design Specification Manual 4 | 5 | ### Author: Yu Cheng 6 | 7 | --- 8 | 9 | ## Table of Contents 10 | 1. [System Overview](#1-system-overview) 11 | 2. [Overall Architecture](#2-overall-architecture) 12 | 3. [Functional Module Design](#3-functional-module-design) 13 | - [Data Collection and Preprocessing Module](#31-data-collection-and-preprocessing-module) 14 | - [Feature Extraction Module](#32-feature-extraction-module) 15 | - [Model Training Module](#33-model-training-module) 16 | - [Early Warning and Risk Assessment Module](#34-early-warning-and-risk-assessment-module) 17 | 4. [Key Technologies and Algorithms](#4-key-technologies-and-algorithms) 18 | 5. [User Interface Design](#5-user-interface-design) 19 | 6. [Database Design](#6-database-design) 20 | 7. [Testing Plan](#7-testing-plan) 21 | - [Unit Testing](#71-unit-testing) 22 | - [Integration Testing](#72-integration-testing) 23 | - [System Functionality Testing](#73-system-functionality-testing) 24 | - [Performance Testing](#74-performance-testing) 25 | - [Security Testing](#75-security-testing) 26 | 8. [Software Maintenance](#8-software-maintenance) 27 | 28 | --- 29 | 30 | ## 1. System Overview 31 | The financial transaction landscape is evolving with globalization and advancements in information technology. Traditional anti-money laundering (AML) monitoring techniques, such as rule-based analysis and anomaly detection, face challenges due to increasingly sophisticated money laundering techniques. 32 | 33 | Deep learning technology, known for its robust pattern recognition and complex data processing capabilities, is applied in this AML system to build intelligent, automated models. This system leverages deep learning to extract patterns from massive datasets, enabling the identification of previously undetectable anomalies and adapting to new money laundering methods. 34 | 35 | --- 36 | 37 | ## 2. Overall Architecture 38 | The deep learning-based AML early warning system is designed with the following components: 39 | 40 | ### 2.1 Data Collection and Preprocessing Module 41 | - **Function**: Collects data from financial activities like bank transactions, cross-border transfers, and financial product purchases. 42 | - **Data Sources**: Banking systems, third-party payment platforms, exchanges, etc. 43 | - **Preprocessing**: 44 | - Data cleaning (removal of invalid/erroneous data). 45 | - Format conversion (ensures uniformity). 46 | - Feature extraction (extracts key information for model training). 47 | 48 | ### 2.2 Deep Learning Model Training Module 49 | - **Function**: Constructs and trains AML models using deep learning algorithms (e.g., deep neural networks, CNNs, RNNs). 50 | - **Requirement**: Requires labeled datasets for distinguishing normal and suspicious transactions. 51 | 52 | ### 2.3 Real-Time Monitoring and Anomaly Detection Module 53 | - **Function**: Processes real-time data streams through trained models. 54 | - **Output**: Identifies suspicious transactions, generates alerts, and triggers further actions like manual review. 55 | 56 | ### 2.4 Decision Support and Risk Assessment Module 57 | - **Function**: Assists financial institutions in taking actions like freezing accounts or reporting to regulators. 58 | - **Performance Metrics**: Evaluates false positive/negative rates for system optimization. 59 | 60 | ### 2.5 Compliance and Privacy Protection Module 61 | - **Function**: Ensures adherence to privacy laws (e.g., GDPR) and safeguards user privacy with data security and anonymization techniques. 62 | 63 | ### 2.6 Integration and Interface Module 64 | - **Function**: Connects the system with internal bank systems and regulatory platforms, ensuring seamless data exchange. 65 | 66 | --- 67 | 68 | ## 3. Functional Module Design 69 | 70 | ### 3.1 Data Collection and Preprocessing Module 71 | - **Description**: Collects and preprocesses data from multiple financial systems. 72 | - **Interface Design**: Provides APIs for data input and formatting. 73 | 74 | ### 3.2 Feature Extraction Module 75 | - **Description**: Extracts meaningful patterns for training models. 76 | - **Interface Design**: Supports custom feature configuration. 77 | 78 | ### 3.3 Model Training Module 79 | - **Description**: Builds predictive models using historical data. 80 | - **Interface Design**: Exposes APIs for training and testing models. 81 | 82 | ### 3.4 Early Warning and Risk Assessment Module 83 | - **Description**: Generates alerts for suspicious activities. 84 | - **Interface Design**: Enables integration with monitoring systems. 85 | 86 | --- 87 | 88 | ## 4. Key Technologies and Algorithms 89 | - Utilizes deep learning techniques like CNNs, RNNs, and DNNs. 90 | - Optimized for large-scale, labeled datasets. 91 | - Adapts models to new money laundering patterns with self-learning capabilities. 92 | 93 | --- 94 | 95 | ## 5. User Interface Design 96 | - A user-friendly dashboard for monitoring transaction alerts. 97 | - Customizable views for risk assessment reports and model performance metrics. 98 | 99 | --- 100 | 101 | ## 6. Database Design 102 | - Schema optimized for high-volume transaction data. 103 | - Supports relational and non-relational models for scalability. 104 | 105 | --- 106 | 107 | ## 7. Testing Plan 108 | 109 | ### 7.1 Unit Testing 110 | - Validates individual components for data collection and model performance. 111 | 112 | ### 7.2 Integration Testing 113 | - Ensures seamless integration across all modules. 114 | 115 | ### 7.3 System Functionality Testing 116 | - Verifies complete workflows for AML detection and alerting. 117 | 118 | ### 7.4 Performance Testing 119 | - Measures system throughput and latency under high-volume data streams. 120 | 121 | ### 7.5 Security Testing 122 | - Tests for compliance with privacy laws and protection against breaches. 123 | 124 | --- 125 | 126 | ## 8. Software Maintenance 127 | - Provides guidelines for periodic updates, including model retraining and interface adjustments. 128 | - Ensures the system remains compliant with evolving regulations. 129 | 130 | --- 131 | 132 | ## License 133 | This system is proprietary software and subject to applicable laws and restrictions. 134 | --------------------------------------------------------------------------------