OSINT Money Laundering Detection Application

Requirements and Implementation Plan

1. Executive Summary

This document outlines the requirements and implementation plan for an Open Source Intelligence (OSINT) application designed to identify potential money laundering red flags associated with individuals and businesses. The application will leverage CrewAI as the agent orchestration framework, Brave MCP for web searches, and frontier Large Language Models (LLMs) for information analysis and structured output generation. The user interface will be built using Gradio.

2. Project Goals

Create an OSINT tool that gathers comprehensive information about individuals and businesses from publicly available sources
Identify potential money laundering indicators based on analysis of the gathered information
Present findings in a structured, actionable format to assist in financial crime investigations
Provide an intuitive user interface that allows for easy input and clear presentation of results

3. Technical Architecture

3.1 Core Components

CrewAI Framework: Orchestrates autonomous agents to perform specialized tasks
Web Search Module: Utilizes Brave MCP for comprehensive web searches
LLM Analysis Engine: Leverages frontier LLMs to process and analyze gathered information
Gradio Frontend: Provides the user interface for interaction with the system

3.2 Architecture Diagram

┌───────────────────┐          ┌──────────────────────┐
│                   │          │                      │
│  Gradio Frontend  │◄────────►│  CrewAI Controller   │
│                   │          │                      │
└───────────────────┘          └──────────┬───────────┘
                                          │
                                          ▼
                    ┌──────────────────────────────────────┐
                    │                                      │
                    │      Agent Orchestration Layer       │
                    │                                      │
                    └───┬─────────────┬─────────────┬──────┘
                        │             │             │
            ┌───────────▼───┐ ┌───────▼───────┐ ┌───▼────────────┐
            │               │ │               │ │                │
            │ Search Agent  │ │ Analysis Agent│ │ Reporting Agent│
            │ (Brave MCP)   │ │ (LLM)         │ │ (LLM)          │
            │               │ │               │ │                │
            └───────────────┘ └───────────────┘ └────────────────┘

4. Detailed Requirements

4.1 Data Collection Requirements

4.1.1 Target Entities

Individual profile information
- Personal identifiers (name, age, location)
- Professional history
- Social media presence
- Public records (property ownership, legal filings)
Business profile information
- Corporate structure
- Ownership information
- Business registration details
- Financial disclosures if publicly available
- Business relationships and partnerships
- Industry reputation

4.1.2 Search Parameters

Primary identifiers (full name, business name)
Secondary identifiers (location, industry, associates)
Customizable search depth (standard, deep)
Date range filtering capabilities
Geographic focus areas

4.2 Analysis Requirements

4.2.1 Money Laundering Indicators

The system should detect and flag the following potential indicators:

Structural Red Flags
- Complex corporate structures with no clear business purpose
- Companies registered in high-risk jurisdictions
- Shell companies with minimal operational footprint
- Frequent changes in business structure or ownership
Transactional Red Flags
- Inconsistencies between public business activity and apparent resources
- Involvement with industries known for money laundering risks
- Connections to entities on sanction lists or watchlists
- Unusual growth patterns or business expansions
Reputational Red Flags
- Negative news coverage related to financial crimes
- Past investigations or regulatory actions
- Association with politically exposed persons (PEPs)
- Inconsistencies in public statements and actual business operations

4.2.2 LLM Analysis Capabilities

Extract and correlate information from diverse sources
Identify patterns and anomalies in collected data
Apply AML (Anti-Money Laundering) expertise to evaluate findings
Generate confidence scores for identified red flags
Explain reasoning behind flagged items

4.3 User Interface Requirements

4.3.1 Input Interface

Target entity input fields (individual name, business name)
Search parameter configuration options
Investigation depth selector
Search history functionality

4.3.2 Results Display

Summary dashboard with key findings
Detailed report section with evidence
Visualization of entity relationships
Red flag severity indicators
Source citations for all information
Option to export findings in various formats (PDF, CSV, JSON)

4.3.3 User Experience

Progress indicators during search and analysis
Responsive design for desktop and tablet use
Clear navigation between different report sections
Ability to save and reload previous investigations

5. Agent Structure (CrewAI Implementation)

5.1 Agent Roles and Responsibilities

5.1.1 Research Agent

Objective: Gather comprehensive information about target entities
Tools: Brave MCP search API
Capabilities:
- Execute multi-faceted search queries
- Follow information trails across multiple sources
- Filter and prioritize relevant information
- Store and organize gathered data

5.1.2 Analysis Agent

Objective: Process gathered information to identify potential money laundering indicators
Tools: Frontier LLM API
Capabilities:
- Apply AML expertise to evaluate gathered information
- Cross-reference findings against known money laundering patterns
- Identify and categorize potential red flags
- Assign confidence scores to findings

5.1.3 Reporting Agent

Objective: Create structured, clear reports from analysis findings
Tools: Frontier LLM API
Capabilities:
- Organize findings in a logical structure
- Generate concise summaries of complex information
- Create visualizations of entity relationships
- Format reports for readability and impact

5.2 Agent Communication Workflow

User initiates search through Gradio interface
Research Agent conducts initial search based on provided parameters
Research Agent iteratively refines search based on initial findings
Analysis Agent processes gathered information from Research Agent
Analysis Agent identifies potential red flags and areas of concern
Reporting Agent structures findings into comprehensive report
Gradio interface displays final report to user

6. Implementation Plan

6.1 Phase 1: Core Framework Setup (Weeks 1-2)

Set up development environment
Implement basic CrewAI framework configuration
Create agent templates and communication protocols
Establish Brave MCP integration for basic searches
Implement LLM API connections

6.2 Phase 2: Agent Development (Weeks 3-5)

Develop and test Research Agent capabilities
Implement Analysis Agent with basic AML pattern recognition
Create Reporting Agent with standard report templates
Test agent communication and data handoffs

6.3 Phase 3: Frontend Development (Weeks 6-7)

Design and implement Gradio interface
Create input forms and configuration options
Develop results display components
Implement export functionality

6.4 Phase 4: Integration and Testing (Weeks 8-9)

Integrate all components into unified system
Conduct performance testing
Optimize search algorithms and analysis pipelines
Perform security review

6.5 Phase 5: Refinement and Launch (Weeks 10-12)

Conduct user acceptance testing
Refine UI/UX based on feedback
Optimize LLM prompts for improved analysis
Prepare documentation and launch materials

7. Technical Requirements

7.1 Development Requirements

Python 3.9+ environment
CrewAI framework (latest version)
Brave MCP API access credentials
Access to frontier LLM APIs (Claude, GPT-4, etc.)
Gradio UI framework

7.2 Deployment Requirements

Server environment with Python support
Minimum 8GB RAM, 4 CPU cores recommended
API key management system
Secure credential storage
Rate limiting implementation for API calls

7.3 Security Requirements

Encrypted storage of search results
Secure API key management
User authentication for accessing the application
Audit logging of all searches conducted
Compliance with relevant data protection regulations

8. Evaluation Metrics

8.1 Performance Metrics

Search completion time
Analysis accuracy (compared to expert review)
System resource utilization
API cost efficiency

8.2 Quality Metrics

Red flag detection accuracy
False positive rate
Source diversity
Explanation quality for identified red flags

9. Limitations and Ethical Considerations

9.1 Technical Limitations

Reliance on publicly available information only
API rate limits may affect search depth
LLM hallucination risks require human verification
Limited to text-based information analysis

9.2 Ethical Guidelines

System should be used as an investigative aid, not as sole decision basis
All findings require human verification before action
Use limited to legitimate AML and financial crime prevention purposes
Compliance with privacy laws and regulations required
Application should not be used for harassment or unauthorized surveillance

10. Code Structure Overview

10.1 Main Components

# Project structure
osint_aml_app/
├── app.py                      # Main application entry point
├── config/                     # Configuration files
│   ├── config.yaml             # General configuration
│   └── agent_configs.yaml      # Agent-specific configurations
├── agents/                     # CrewAI agent implementations
│   ├── research_agent.py       # Web search agent
│   ├── analysis_agent.py       # AML analysis agent
│   └── reporting_agent.py      # Report generation agent
├── tools/                      # Tool implementations
│   ├── brave_search.py         # Brave MCP search integration
│   ├── llm_interface.py        # LLM API interfaces
│   └── data_processor.py       # Data processing utilities
├── ui/                         # Gradio UI components
│   ├── input_forms.py          # Input interfaces
│   ├── results_display.py      # Results visualization
│   └── export_tools.py         # Report export functionality
├── models/                     # Data models
│   ├── entity.py               # Entity representation
│   ├── red_flag.py             # Red flag classification
│   └── report.py               # Report structure
└── utils/                      # Utility functions
    ├── validators.py           # Input validation
    ├── parsers.py              # Content parsing
    └── security.py             # Security utilities

11. Budget and Resource Requirements

11.1 Development Resources

Developer time: 12 weeks (1-2 developers)
LLM API costs: Estimated $500-1000 for development and testing
Brave MCP API costs: Based on search volume (approximately $200-500)
Infrastructure costs: $100-200/month for development servers

11.2 Operational Resources

Ongoing API costs: Dependent on usage volume
Maintenance: 10-15 hours per month
Infrastructure: $200-400/month depending on scale

12. Expansion Possibilities

Integration with financial database APIs
Addition of document analysis capabilities
Implementation of temporal analysis (tracking changes over time)
Development of collaborative investigation features
Integration with case management systems
Support for additional languages and jurisdictions

13. Success Criteria

The application will be considered successful if it:

Accurately identifies at least 85% of known money laundering indicators in test cases
Maintains a false positive rate below 15%
Completes standard searches in under 5 minutes
Receives positive usability feedback from AML professionals
Provides clear, actionable intelligence that enhances investigation capabilities