Introduction
The insurance industry relies heavily on accurate renewal forecasting to manage their risk and maintain profitability. Inaccurate renewal forecasting can result in inadequate pricing and underwriting decisions, leading to financial losses for the insurance company. Therefore, developing a predictive model for accurate Insurance Renewal forecasting is essential.
Client Challenges
- Data quality: The accuracy of the predictive model depends on the quality of the data. The data obtained from client CRM may have missing fields or inaccurate data, which can affect the model’s accuracy.
- Data consolidation: Consolidating the data from different sources can be a challenge, and it requires expertise in data modeling and migration.
- Statistical modeling: Developing a predictive model using advanced statistical techniques requires a thorough understanding of the statistical concepts and their application to the insurance domain.
- Query optimization: Utilizing SPARQL to query across multiple patients and prescriptions requires query optimization to ensure fast and efficient data retrieval.
Opportunities
- Improved accuracy
- Providing better insights
- Efficient data processing
Toolset & Technologies
- PostgreSQL
- SPARQL
- GIT-based version control
Results
- Developed a predictive model using advanced statistical techniques, enabling accurate insurance renewal forecasting.
- Used SPARQL for comprehensive analysis and querying across multiple patients to derive common patterns and prescriptions providing better insights into the patient journey and scenarios that generate SA renewals.
- Implemented data models and migration using PostgreSQL enabling efficient data processing, reducing the time and effort required to generate insights.
