AI Driven Predictive Analytics Workflow for Policy Pricing

Predictive Analytics for Policy Pricing

1. Data Collection

1.1 Identify Data Sources

Gather data from internal and external sources, including:

• Historical claims data
• Customer demographics
• Market trends
• Social media sentiment

1.2 Data Integration

Utilize tools such as:

• Apache Kafka for real-time data streaming
• Talend for data integration and ETL processes

2. Data Preparation

2.1 Data Cleaning

Employ AI-driven tools to clean and preprocess data:

• Trifacta for data wrangling
• Pandas (Python library) for data manipulation

2.2 Feature Engineering

Identify and create relevant features using:

• Automated machine learning tools like H2O.ai
• Featuretools for feature extraction

3. Model Development

3.1 Select Modeling Techniques

Choose appropriate AI models, such as:

• Regression models (e.g., Linear Regression)
• Decision trees and ensemble methods (e.g., Random Forest)
• Neural networks for complex patterns

3.2 Model Training

Utilize platforms like:

• Google Cloud AI Platform for scalable training
• Azure Machine Learning for model management

4. Model Evaluation

4.1 Performance Metrics

Assess models using metrics such as:

• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)
• Accuracy and Precision

4.2 Model Validation

Implement cross-validation techniques to ensure model reliability.

5. Implementation

5.1 Deployment

Deploy the model using:

• Docker containers for microservices architecture
• Amazon SageMaker for deployment and monitoring

5.2 Integration with Pricing Systems

Integrate predictive models into existing pricing systems to automate policy pricing.

6. Monitoring and Maintenance

6.1 Continuous Monitoring

Utilize tools for monitoring model performance:

• Prometheus for real-time monitoring
• Grafana for visualization

6.2 Model Retraining

Establish a schedule for periodic retraining of models with new data to maintain accuracy.

7. Reporting and Analysis

7.1 Insights Generation

Use dashboards to present insights derived from predictive analytics:

• Tableau for data visualization
• Power BI for interactive reporting

7.2 Stakeholder Communication

Prepare reports and presentations for stakeholders to communicate findings and recommendations.