AI-Powered Financial Fraud Detection Solution

Overview

This project implements an end-to-end AI-powered financial fraud detection system that leverages both traditional machine learning and advanced Large Language Models (LLMs) to identify and prevent fraudulent transactions in real-time. The solution combines the power of OpenAI's GPT models with traditional ML approaches to provide comprehensive fraud detection capabilities.

Key Features

• Real-time transaction monitoring and fraud detection
• Advanced feature engineering for fraud pattern recognition
• Multiple ML model support (XGBoost, LightGBM, Random Forest)
• LLM-powered transaction analysis and anomaly detection
• Natural language processing for fraud pattern identification
• Automated fraud investigation and reporting
• Model monitoring and drift detection
• Cloud-native architecture with AWS integration
• Comprehensive logging and monitoring
• RESTful API for integration

AI/LLM Integration

OpenAI Integration

• Transaction Analysis:

  • Natural language understanding of transaction descriptions
  • Pattern recognition in merchant names and locations
  • Contextual analysis of transaction sequences
  • Anomaly detection in transaction narratives

• Fraud Investigation:

  • Automated report generation
  • Natural language explanations of fraud alerts
  • Historical pattern analysis
  • Risk assessment summaries

• Customer Communication:

  • Automated fraud alert notifications
  • Natural language responses to customer queries
  • Personalized security recommendations
  • Multi-language support

LLM Capabilities

• Transaction Classification:

  • Categorization of transactions using natural language
  • Identification of suspicious patterns
  • Context-aware risk assessment
  • Behavioral pattern recognition

• Document Analysis:

  • Processing of financial documents
  • Extraction of relevant information
  • Verification of document authenticity
  • Cross-reference with transaction data

• Risk Assessment:

  • Natural language risk scoring
  • Contextual analysis of customer behavior
  • Historical pattern matching
  • Real-time risk level adjustment

Integration Architecture

• API Layer:

  • OpenAI API integration
  • Rate limiting and cost management
  • Response caching
  • Fallback mechanisms

• Processing Pipeline:

  • Text preprocessing
  • Context enrichment
  • Response validation
  • Result aggregation

• Security Measures:

  • Data encryption
  • PII handling
  • Access control
  • Audit logging

Core Components

Dataiku DSS Integration

The project leverages Dataiku DSS for data processing and model development:

Custom Plugins

• Feature Engineering Plugin: Processes transaction data to create meaningful features
  • Time-based features (hour, day of week, weekend indicators)
  • Amount-based features (log transform, z-scores)
  • Behavioral features (transaction frequency, velocity)
  • Risk scores (merchant, customer, location)
• Model Training Plugin: Implements multiple ML models
  • XGBoost with hyperparameter tuning
  • LightGBM for fast training
  • Random Forest for interpretability
  • Gradient Boosting for ensemble learning
• Model Monitoring Plugin: Tracks model performance and data drift
  • Statistical drift detection
  • Performance metrics tracking
  • Feature importance monitoring
  • Automated retraining triggers

Dataiku Flows

• Data Processing Flow: Handles data ingestion and preprocessing
• Model Training Flow: Orchestrates model training and evaluation
• Monitoring Flow: Manages model monitoring and alerts
• Deployment Flow: Handles model deployment and versioning

Infrastructure as Code (Terraform)

AWS Infrastructure

• Compute Resources:
  • EC2 instances for model serving
  • EKS cluster for containerized deployment
  • Lambda functions for serverless components
• Storage:
  • S3 buckets for data and model artifacts
  • EFS for shared storage
  • RDS for metadata storage
• Networking:
  • VPC with public and private subnets
  • Security groups and NACLs
  • Application Load Balancer
• Security:
  • IAM roles and policies
  • KMS for encryption
  • Secrets Manager for credentials

Terraform Modules

• Network Module: VPC and networking components
• Compute Module: EC2 and EKS resources
• Storage Module: S3 and RDS configurations
• Security Module: IAM and security settings
• Monitoring Module: CloudWatch and logging setup

GitHub Actions Workflows

CI/CD Pipeline

• Code Quality:
  • Linting (flake8, black)
  • Type checking (mypy)
  • Unit test execution
  • Integration test validation
• Security Scanning:
  • Dependency vulnerability checks
  • Code security analysis
  • Container scanning
  • Infrastructure security validation
• Deployment:
  • Automated testing
  • Infrastructure validation
  • Staging deployment
  • Production deployment

Automated Workflows

• Pull Request Validation:
  • Code review automation
  • Test coverage reporting
  • Documentation updates
• Release Management:
  • Version tagging
  • Changelog generation
  • Release notes
• Monitoring:
  • Performance testing
  • Load testing
  • Health checks

Data Quality and Governance

Data Quality Monitoring

The project implements comprehensive data quality monitoring through a custom Dataiku plugin:

Quality Checks

• Completeness Monitoring:

  • Missing value detection
  • Threshold-based validation
  • Column-level completeness tracking
  • Automated reporting

• Consistency Validation:

  • Rule-based data validation
  • Custom validation rules
  • Violation tracking
  • Automated alerts

• Accuracy Verification:

  • Reference data comparison
  • Key column validation
  • Match ratio calculation
  • Quality scoring

• Freshness Tracking:

  • Timestamp-based monitoring
  • Age calculation
  • Update frequency tracking
  • SLA monitoring

Data Lineage Tracking

The solution includes a robust data lineage tracking system:

Lineage Features

• Dataset Creation Tracking:

  • Source dataset recording
  • Transformation documentation
  • Parameter logging
  • Version tracking

• Modification History:

  • Change tracking
  • Modification type recording
  • Timestamp logging
  • Impact analysis

• Dependency Management:

  • Upstream dependency tracking
  • Downstream impact analysis
  • Relationship visualization
  • Change propagation analysis

Data Catalog

A comprehensive data catalog implementation provides:

Catalog Features

• Dataset Registration:

  • Metadata management
  • Schema documentation
  • Owner assignment
  • Tag management

• Search and Discovery:

  • Text-based search
  • Tag-based filtering
  • Sensitivity level filtering
  • Schema browsing

• Metadata Management:

  • Description updates
  • Tag management
  • Schema versioning
  • Access control

Integration Example

# Initialize components
dq_checker = DataQualityCheck("transactions")
lineage_tracker = DataLineageTracker()
catalog = DataCatalog()

# Process data with quality checks
quality_results = dq_checker.run_all_checks([
    {'column': 'amount', 'condition': '>= 0'},
    {'column': 'timestamp', 'condition': '<= pd.Timestamp.now()'}
])

# Track lineage
lineage_tracker.track_dataset_creation(
    dataset_name="processed_transactions",
    source_datasets=["raw_transactions"],
    transformation_type="processing",
    parameters={"method": "standard_processing"}
)

# Update catalog
catalog.update_dataset_metadata(
    dataset_name="processed_transactions",
    updates={
        "last_processed": datetime.now().isoformat(),
        "quality_score": calculate_quality_score(quality_results)
    }
)

Project Structure

.
├── application/              # Main application code
│   ├── src/                 # Source code
│   ├── tests/              # Application tests
│   ├── Dockerfile          # Container definition
│   └── requirements.txt    # Python dependencies
├── dataiku/                 # Dataiku DSS integration
│   ├── plugins/            # Custom Dataiku plugins
│   ├── flows/              # Dataiku flow definitions
│   ├── tests/              # Test suites
│   ├── plots/              # Generated plots
│   ├── test_flow.py        # Test flow implementation
│   └── requirements.txt    # Python dependencies
├── infrastructure/          # Infrastructure as Code
│   ├── modules/            # Terraform modules
│   ├── main.tf             # Main Terraform configuration
│   ├── variables.tf        # Terraform variables
│   ├── outputs.tf          # Terraform outputs
│   ├── validate.tf         # Validation rules
│   └── terraform.tfvars    # Variable values
├── scripts/                 # Utility scripts
├── docs/                    # Documentation
├── .github/                 # GitHub workflows and templates
└── tests/                   # Global test suites

Prerequisites

• Python 3.8+
• AWS Account with appropriate permissions
• OpenAI API key
• Terraform 1.0+
• Docker
• Dataiku DSS (optional, for development)

Installation

1. Clone the repository:

git clone https://github.com/pxkundu/ai-financial-fraud-detection-solution.git
cd ai-financial-fraud-detection-solution

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

# Create .env file
cp .env.example .env

# Add your OpenAI API key
echo "OPENAI_API_KEY=your_api_key_here" >> .env

5. Set up AWS credentials:

aws configure

Usage

Running the Application

1. Start the API server:

python application/src/main.py

2. Access the API documentation at http://localhost:8000/docs

Testing LLM Integration

# Test OpenAI integration
python tests/test_llm_integration.py

# Test fraud detection with LLM
python tests/test_fraud_detection.py

Running Tests

python -m pytest tests/

Deploying Infrastructure

cd infrastructure/terraform
terraform init
terraform apply

Components

Feature Engineering

The system processes transaction data to create meaningful features:

• Time-based features (hour, day of week, weekend indicators)
• Amount-based features (log transform, z-scores)
• Behavioral features (transaction frequency, velocity)
• Risk scores (merchant, customer, location)

Model Training

Multiple models are trained and evaluated:

• XGBoost
• LightGBM
• Random Forest
• Gradient Boosting

Model Monitoring

Continuous monitoring of:

• Data drift detection
• Model performance metrics
• Prediction distributions
• Feature importance changes

API Endpoints

• /predict - Real-time fraud prediction
• /batch-predict - Batch prediction processing
• /model/status - Model health check
• /model/metrics - Performance metrics

Development

Adding New Features

1. Create a new branch:

git checkout -b feature/your-feature-name

2. Make your changes
3. Run tests:

python -m pytest tests/

4. Submit a pull request

Code Style

• Follow PEP 8 guidelines
• Use type hints
• Write docstrings for all functions
• Include unit tests for new features

Monitoring and Maintenance

Logging

• Application logs are stored in CloudWatch
• Model performance metrics in CloudWatch Metrics
• Custom dashboards available in Grafana

Alerts

• Model drift alerts
• Performance degradation alerts
• System health alerts

Security

• All API endpoints are secured with JWT authentication
• Sensitive data is encrypted at rest
• Network traffic is encrypted in transit
• Regular security audits and updates

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Support

For support, please open an issue in the GitHub repository or contact the maintainers.

Acknowledgments

• AWS for cloud infrastructure
• Dataiku for DSS integration
• Open source ML libraries
• Contributors and maintainers

Test Output

The following plots were generated during the Dataiku feature engineering tests:

Transaction Amount Distribution

Time-based Features

Risk Score Distribution

Customer Statistics

Data Quality and Governance Visualizations

The project includes automated visualization of data quality metrics and governance information through GitHub Actions workflows. These visualizations are generated daily and provide insights into the health and status of our data pipeline.

Quality Metrics Dashboard

Daily tracking of data quality metrics including completeness, consistency, accuracy, and freshness

Data Lineage Graph

Visual representation of data flow and transformations across the pipeline

Data Catalog Dashboard

Comprehensive view of dataset metadata, including size, records, sensitivity, and update frequency

These visualizations are automatically generated and updated through our GitHub Actions workflow, which runs daily to ensure we have up-to-date insights into our data quality and governance metrics. The workflow:

1. Runs data quality checks
2. Generates quality metrics visualization
3. Creates data lineage graph
4. Produces catalog dashboard
5. Uploads all visualizations as artifacts

To view the latest visualizations:

1. Go to the GitHub Actions tab
2. Select the "Data Quality and Governance Visualization" workflow
3. Click on the latest successful run
4. Download the visualization artifacts