Executive Summary

Employee benefit programs are one of the largest controllable expenses for insurers and employers. Organizations often struggle to understand cost drivers or optimize plan performance because data is fragmented across HR systems, finance databases, and third-party reports.

The AI Benefits Cost & Utilization Analyzer prototype demonstrates how Predictive AI, Semantic Search, Computer Vision, and Conversational AI *could* bring structure and actionable insight to this challenge. The prototype operates within a local environment using Open Data sources and Bronze-level ingested datasets, enabling safe testing and early experimentation.

Overview

By combining structured and unstructured datasets in a local Data Lakehouse, the prototype provides visibility into utilization patterns, emerging cost drivers, and potential areas for savings. This approach gives teams hands-on experience with modern data tools including MinIO, Apache Spark, Project Nessie, and Dremio.

Open Data: All datasets used in this prototype are publicly available Open Data, ensuring transparency, privacy, and reproducibility.

Key Features (Planned)

  • Predictive AI: Forecast trends using historical claims, demographics, and seasonality
  • Computer Vision: Extract data from claim and invoice images
  • Semantic Search: Natural-language queries across benefit datasets
  • Conversational Analytics: Chatbot interface for benefits exploration
  • Generative Reporting: Produce executive summaries with charts and insights

Target Users

  • HR analytics and benefits teams seeking quick insights
  • Insurance finance analysts tracking cost drivers
  • Data science teams supporting benefits management
  • Executives responsible for cost optimization and plan design

Business Value

  • Improve cost visibility and forecasting accuracy
  • Reduce manual data preparation and reporting effort
  • Identify underutilized plans and cost-saving opportunities
  • Enable data-driven decision-making for benefits design and risk management

Technology Overview

The prototype leverages Open Data integrated into a local Data Lakehouse environment built with MinIO, Apache Spark, Dremio, and Project Nessie. Bronze-level datasets are ingested for profiling and experimentation. Predictive AI, Computer Vision, Streamlit interface, and Conversational AI are planned for future development.

Current Status

Local Data Lakehouse environment installed. Bronze-level datasets ingested and initially profiled. Development of predictive AI, computer vision, and generative reporting is in planning.

MVP Scope (Planned)

  • Local prototype demonstrating cost and utilization forecasting
  • Claims image extraction using Computer Vision
  • Natural-language query testing through chatbot interface
  • Generative executive summary output

Roadmap (Future Enhancements)

  • Phase 2: Expand chatbot and add automated reporting features
  • Phase 3: Integrate with enterprise systems like Workday and SAP SuccessFactors
  • Phase 4: Extend predictive models to underwriting and claims automation
  • Phase 5: Develop enterprise dashboard for real-time analytics and governance metrics