Predictive Risk Management Solution for Health Insurer

This case study outlines the implementation of a predictive risk management solution developed by Tiger Analytics for a major US health insurer. The primary objective was to enhance care and disease management processes by predicting risk events and identifying impacted patients for timely interventions. The solution involved creating a robust analytics platform through a structured approach that included exploratory data analysis, feature engineering, and modeling. The team developed three models: Risk of Hospitalization, Risk of Emergency Room Visits, and Risk of High-Cost Claims, utilizing advanced techniques such as Hyper Parameter Tuning and SHAP for model interpretability. The implementation resulted in a 25% reduction in expected payments due to early intervention models, demonstrating the effectiveness of the analytics platform in preventing avoidable hospitalizations and managing clinical and economic burdens. The technology stack included Azure Databricks, PySpark, and Power BI, facilitating the sharing of insights through dashboards and reports.