AI Driven Predictive Analytics for Premium Pricing Workflow

Predictive Analytics for Premium Pricing

1. Data Collection

1.1 Identify Data Sources

• Internal data (claims history, customer demographics)
• External data (market trends, economic indicators)

1.2 Data Acquisition

• Utilize APIs to gather real-time data from external sources.
• Implement data scraping tools for competitor analysis.

2. Data Preprocessing

2.1 Data Cleaning

• Remove duplicates and irrelevant information.
• Handle missing values through imputation techniques.

2.2 Data Transformation

• Normalize and standardize data for consistency.
• Convert categorical variables using one-hot encoding.

3. Model Development

3.1 Feature Selection

• Utilize tools like Python’s Scikit-learn for feature importance analysis.
• Identify key indicators influencing premium pricing.

3.2 Model Selection

• Choose appropriate machine learning algorithms (e.g., regression, decision trees).
• Examples of AI-driven products: IBM Watson, Microsoft Azure Machine Learning.

4. Model Training

4.1 Training the Model

• Split data into training and testing sets.
• Train the model using historical data to predict future premiums.

4.2 Model Evaluation

• Assess model performance using metrics such as RMSE and accuracy.
• Utilize cross-validation techniques to ensure robustness.

5. Implementation

5.1 Integration with Business Systems

• Incorporate the predictive model into existing pricing software.
• Use tools like Tableau for data visualization and insights.

5.2 Real-time Pricing Adjustments

• Implement AI algorithms for dynamic pricing based on real-time data.
• Utilize chatbots for customer interaction and pricing inquiries.

6. Monitoring and Optimization

6.1 Performance Tracking

• Set up dashboards to monitor key performance indicators (KPIs).
• Regularly review model predictions against actual outcomes.

6.2 Continuous Improvement

• Refine models based on new data and feedback.
• Stay updated with advancements in AI technologies to enhance predictive capabilities.

7. Reporting and Decision Making

7.1 Generate Reports

• Automate report generation for stakeholders using BI tools.
• Provide insights on pricing strategies and market positioning.

7.2 Strategic Decision Making

• Utilize predictive insights to inform underwriting decisions.
• Adjust marketing strategies based on pricing trends and customer behavior.