AI Driven Real Time Fraud Detection and Prevention Workflow

Real-Time Fraud Detection and Prevention

1. Data Collection

1.1. Sources of Data

• Transaction records
• User behavior data
• Geolocation data
• Device information

1.2. Tools for Data Collection

• API integrations with banking systems
• Web scraping tools for market data
• Data lakes for large-scale data storage

2. Data Preprocessing

2.1. Data Cleaning

• Remove duplicates
• Handle missing values
• Standardize formats

2.2. Data Transformation

• Normalization of transaction amounts
• Encoding categorical variables

3. Feature Engineering

3.1. Identifying Key Features

• Transaction frequency
• Average transaction amount
• Time of transaction

3.2. Tools for Feature Engineering

• Python libraries (e.g., Pandas, Scikit-learn)
• Feature extraction algorithms

4. Model Development

4.1. Selecting Machine Learning Algorithms

• Random Forest
• Gradient Boosting Machines
• Neural Networks

4.2. AI-Driven Products

• IBM Watson for Fraud Detection
• Google Cloud AutoML for custom model development

5. Model Training

5.1. Training the Model

• Split data into training and test sets
• Use cross-validation techniques

5.2. Tools for Model Training

• TensorFlow
• Keras

6. Model Evaluation

6.1. Performance Metrics

• Accuracy
• Precision and Recall
• F1 Score

6.2. Tools for Evaluation

• Scikit-learn for metric calculations
• Tableau for visualization of results

7. Real-Time Deployment

7.1. Integration into Existing Systems

• API deployment for real-time scoring
• Integration with transaction processing systems

7.2. Tools for Deployment

• AWS SageMaker for model hosting
• Docker for containerization

8. Monitoring and Feedback

8.1. Continuous Monitoring

• Real-time alert systems for suspicious activities
• Feedback loop for model retraining

8.2. Tools for Monitoring

• Splunk for log analysis
• Grafana for real-time dashboards

9. Compliance and Reporting

9.1. Regulatory Compliance

• Ensure adherence to GDPR and PCI DSS
• Regular audits and compliance checks

9.2. Reporting Tools

• Power BI for reporting and analytics
• Custom dashboards for stakeholder insights