AI Driven Fraud Detection Workflow for Enhanced Security

AI-Driven Fraud Detection and Prevention System

1. Data Collection

1.1 Sources of Data

• Telecommunications transaction logs
• Customer account information
• Geolocation data
• Call detail records (CDRs)

1.2 Data Integration Tools

• Apache Kafka for real-time data streaming
• Apache NiFi for data flow automation

2. Data Preprocessing

2.1 Data Cleaning

• Removing duplicates and irrelevant data
• Handling missing values

2.2 Feature Engineering

• Creating new features from existing data
• Normalization of data for model training

3. Model Development

3.1 Selection of AI Techniques

• Machine Learning Algorithms (e.g., Random Forest, SVM)
• Deep Learning Techniques (e.g., Neural Networks)

3.2 Tools for Model Development

• TensorFlow for building neural networks
• Scikit-learn for traditional machine learning models

4. Model Training

4.1 Training Process

• Splitting data into training and testing sets
• Utilizing cross-validation techniques

4.2 Performance Evaluation

• Metrics: Accuracy, Precision, Recall, F1 Score
• Tools: Jupyter Notebooks for analysis and visualization

5. Deployment

5.1 Model Deployment Strategies

• Cloud-based deployment (e.g., AWS SageMaker)
• On-premises deployment for sensitive data

5.2 Integration with Existing Systems

• API development for real-time fraud detection
• Integration with Customer Relationship Management (CRM) systems

6. Monitoring and Maintenance

6.1 Continuous Monitoring

• Real-time monitoring of fraud alerts
• Utilizing dashboards (e.g., Tableau, Power BI) for insights

6.2 Model Retraining

• Scheduled retraining based on new data
• Feedback loops for improving model accuracy

7. Reporting and Compliance

7.1 Generating Reports

• Automated reporting tools for compliance audits
• Customizable dashboards for stakeholders

7.2 Regulatory Compliance

• Ensuring adherence to GDPR and other regulations
• Data privacy and security measures implementation