AI Applications in Life & Health Insurance
On Saturday, 18 July, 2026, from 11 a.m. Event has passed
[Recorded] click here to Watch on Youtube
Basics of R Programming for Data Analytics & Hands-on Predictive Analytics
Resources
Dataset 1 (Forecast):
https://docs.google.com/spreadsheets/d/1iuSkCkflenBJo-PILgJ-5YyWk5wTuIUe/edit
Dataset 2 (Health):
https://docs.google.com/spreadsheets/d/1zkGc-gQWUJ6yYtlGgUtX0ooByuD_AaIt/edit
Basic Lab Document:
https://docs.google.com/document/d/1gkadWqAROPRm0GChX1DrrtgYau0RdDob/edit
Analytics Lab Document:
https://docs.google.com/document/d/18CpYsSEg-Q8jKkXlC05BQ-VT8BSz4FBg/edit
Insurance Analytics Ratio:
https://docs.google.com/document/d/1GH-7Xr5Dp6e-ZFnQqSGp644jrpX94YNzdIW6yJszQUI/edit?usp=drivesdk
About the Workshop
This hands-on workshop is designed for Life and Health Insurance Underwriters to develop practical skills in data analytics using R Programming. Participants will work with real insurance datasets through guided laboratory exercises covering underwriting, pricing, claims analysis, and business decision-making.
The programme covers data import, cleaning, exploratory analysis, data visualization, predictive modelling, regression, clustering, and time-series forecasting. Participants will analyse insurance business problems, including claim frequency, claim severity, premium trends, customer segmentation, and premium forecasting.
The session concludes with a short introduction to Python for Predictive Analytics, demonstrating how AI and advanced analytics can support insurance operations.
The focus is on practical implementation and business interpretation, enabling participants to apply analytical insights confidently in underwriting and strategic decision-making.
Event Details
| Session | Basics of R Programming for Data Analytics & Hands-on Exercises |
|---|---|
| Date | 18 July 2026 (Saturday) |
| Time | 11:00 AM – 3:00 PM |
| Target Audience | Life & Health Insurance Underwriters |
| Mode | Instructor-led Practical Workshop |
| Prerequisites | Basic understanding of insurance concepts. No prior knowledge of R Programming is required. |
- Introduction to Data Analytics in Life & Health Insurance
- Lab Setup and R Programming Basics
- Data Import and Data Quality Checks
- Data Preparation and Exploratory Data Analysis
- Health Claims Descriptive Analytics
- Data Visualization and Business Insights
- Health Claims Clustering and Segment Profiling
- Predictive Analytics for Health Claims using Logistic Regression
- Regression Modelling for Life Insurance Premium Analysis
- Time Series Forecasting and GDP–Premium Correlation
- Introduction to Python for Predictive Analytics
- Group Work, Case Studies, Review Questions, and Instructor Discussion
- Use R to import, clean, profile, visualize, and model insurance datasets.
- Perform data quality assessment and exploratory analysis on insurance data.
- Apply descriptive analytics to derive meaningful insights from health claims data.
- Build predictive analytics models using logistic regression for insurance use cases.
- Perform customer and claim segmentation using cluster analysis.
- Develop regression models for life insurance premium analysis.
- Build time-series forecasting models for premium prediction and analyze GDP–premium relationships.
- Connect analytical outputs to key insurance business questions such as claim frequency, claim severity, premium growth, and macroeconomic sensitivity.
- Prepare management-ready interpretations and dashboards suitable for analyst teams, underwriting teams, and senior management.
- Understand the comparative advantages of Python for advanced predictive analytics and AI applications in the insurance industry.
Session Agenda
Session Outcomes
Upon successful completion of the session, participants will be able to:
The session includes practical exercises using real-world Health Claims and Life Insurance datasets, enabling participants to gain hands-on experience in applying analytics techniques to insurance business scenarios.
