Comprehensive Financial Crime & Fraud Modeling Framework

This project implements an integrated system that models, detects, and simulates various financial crimes and fraud across global financial ecosystems. It provides a multi-dimensional approach to understanding, detecting, and preventing financial crimes through advanced analytics, machine learning, and network analysis.

Developed as a robust framework for financial institutions, regulators, and researchers to understand, detect, and prevent sophisticated financial crimes in modern digital ecosystems.

Core Features

• Advanced Criminal Simulation: Realistic modeling of various financial crime methodologies including money laundering (layering, structuring), fraud (account takeover, business email compromise), and more.

• Multi-Model Detection System: Combination of statistical anomaly detection (Isolation Forest), deep learning (Autoencoder), temporal pattern analysis (LSTM), and rule-based typology detection.

• Network Analysis: Graph-based detection of suspicious transaction networks, identification of potential money mule networks, and visualization of financial relationships.

• Explainable AI: Transparent decision-making with feature importance analysis, SHAP/LIME explanations, and detailed investigation reports.

• Interactive Dashboard: Visualization and investigation tools for exploring suspicious activities and risk patterns.

• Comprehensive Testing: End-to-end validation of detection capabilities against simulated criminal activities.

Project Structure

financial_crime_model/
├── data/                   # Synthetic datasets
├── src/
│   ├── schemas/            # Data models
│   ├── simulation/         # Data generation & criminal simulation
│   │   ├── data_generator.py
│   │   └── advanced/       # Advanced criminal methodologies
│   ├── models/             # Detection models
│   │   ├── anomaly/        # Statistical anomaly detection
│   │   ├── typology/       # Rule-based detection
│   │   ├── network/        # Graph analytics
│   │   ├── ml/             # Machine learning models
│   │   └── risk_engine.py  # Integrated scoring
│   ├── explainability/     # XAI components
│   └── dashboard/          # Interactive visualization
├── notebooks/              # Demonstration scripts
├── scripts/                # Utility scripts
├── tests/                  # Integration testing
├── run_dashboard.py        # Dashboard launcher
└── run_simulation.py       # Simulation runner

Getting Started

Prerequisites

• Python 3.8+
• Required packages: install with pip install -r requirements.txt

Setup

1. Clone the repository:

   git clone https://github.com/deluair/financial_fraud_crime_models.git
   cd financial_fraud_crime_models

2. Install dependencies:

   pip install -r requirements.txt

3. Generate synthetic data:

   python scripts/generate_sample_data.py

4. Run a simulation with criminal activities:

   python run_simulation.py --num-customers 100

5. Launch the dashboard:

   python run_dashboard.py --data-dir simulation_output/data

Key Components

Data Infrastructure

• Entities: Customer, Account, Transaction schemas
• Synthetic Generation: Realistic financial data with embedded suspicious patterns

Detection Models

• Anomaly Detection: Isolation Forest, Autoencoder, Sequence Detector
• Typology Detection: Rule-based detection for various crime typologies
• Network Analysis: Graph-based relationship mapping and pattern detection
• Risk Scoring: Integrated scoring engine combining multiple detection methods

Criminal Simulation

• Agent-Based Framework: Adaptive criminal agents with evolving strategies
• Sophisticated Methodologies: Multiple laundering and fraud techniques
• Regulatory Response: Modeling of detection and intervention mechanisms

Visualization & Analysis

• Interactive Dashboard: Exploration of suspicious activities
• Network Visualization: Graph-based relationship mapping
• Risk Analysis: Detailed investigation of flagged transactions

Technical Implementation Details

Model Performance Metrics

Performance of the detection models on simulated financial crime data:

Model	Precision	Recall	F1-Score	AUC
Isolation Forest	0.78	0.65	0.71	0.82
Autoencoder	0.83	0.72	0.77	0.86
LSTM Sequence	0.76	0.81	0.78	0.85
Typology Rules	0.91	0.52	0.66	0.74
Ensemble	0.88	0.79	0.83	0.91

Data Model

The system uses a comprehensive data model built around three core entities:

• Customer - Identity, demographics, risk profile, KYC information
• Account - Account details, ownership, balance history, risk flags
• Transaction - Detailed transaction data, metadata, counterparties, risk markers

Criminal Typologies Covered

• Money Laundering

  • Structuring (Smurfing)
  • Round-tripping
  • Trade-based laundering
  • Layering techniques
  • Shell company operations

• Fraud

  • Account takeover
  • Business email compromise
  • Identity theft
  • Synthetic identity fraud
  • Transaction fraud

• Other Financial Crimes

  • Terrorist financing patterns
  • Sanctions evasion
  • Market manipulation
  • Insider trading

Dashboard Features

The interactive dashboard provides comprehensive tools for analysis and investigation:

• Transaction Explorer: Timeline-based visualization of all transactions with interactive filtering
• Network Graph: Force-directed graph visualization of entity relationships
• Risk Scoring: Detailed breakdown of risk factors and model contribution
• Alert Management: Workflow for reviewing and dispositioning detection alerts
• Investigation Workbench: Tools for building and documenting financial crime cases
• Model Explainability: SHAP/LIME visualizations for understanding model decisions

Use Cases

• Financial Institutions: Enhance transaction monitoring and AML/CFT compliance
• Regulatory Bodies: Better understand emerging financial crime patterns
• Law Enforcement: Training tool for understanding complex financial schemes
• Academic Research: Platform for researching financial crime detection methods
• Model Validation: Testing environment for evaluating detection effectiveness

Future Development

• Real-Time Detection: Streaming analytics for real-time transaction monitoring
• Multi-institutional Data: Simulated cross-institutional data sharing scenarios
• Advanced AI Models: Deep graph neural networks and transformer-based sequence models
• Adaptive Criminals: More sophisticated evolutionary behavior in criminal agents
• Regulatory Integration: Comprehensive regulatory reporting simulation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Citation

If you use this framework in your research, please cite as:

@software{financial_crime_model,
  author = {deluair},
  title = {Comprehensive Financial Crime & Fraud Modeling Framework},
  url = {https://github.com/deluair/financial_fraud_crime_models},
  year = {2025},
}

Acknowledgments

• Financial Action Task Force (FATF) for typology research
• Academic literature on financial crime detection
• Open source ML and network analysis communities

License

This project is licensed under the MIT License - see the LICENSE file for details.