How iaa-aitf/actuarial-ai-case-studies works
This repository is a curated collection of actuarial AI case studies, rather than a single application. The principal workflow is for an actuary or practitioner to browse the catalog, choose a hosted study, and use its notebook, data, and accompanying materials to explore an actuarial problem and its AI-based analysis. The collection spans market comparison, damage classification, claim-cost prediction, multi-agent data analysis, and legacy-code migration; templates support creating additional studies. The README documents these capabilities, but implementation details and precise runtime connections are not available from the sampled source.
Main entry points: case-studies/README.md Study Catalog · README.md Repository Overview · GenAI-driven_market_comparison Market Comparison · car_damage_classification_and_localization Damage Classification
Components 10
Collection
- Repository Overview
README.md - Study Catalog
case-studies/README.md
Case Studies
- Market Comparison
case-studies/2025/GenAI-driven_market_comparison - Damage Classification
case-studies/2025/car_damage_classification_and_localization - Claim Cost Prediction
case-studies/2025/claim_cost_prediction_with_LLM-extracted_features - Multi-Agent Analysis
case-studies/2025/data_analysis_multi-agent_system - Legacy Code Migration
case-studies/2026/actuarial_legacy_code_migration_multi-agent_system - Study Materials
case-studies
Contributions
- Submission Templates
templates
Other components
- Practitioner
Connections 10
- Practitioner → Repository Overview: explores
- Repository Overview → Study Catalog: directs to
- Practitioner → Study Catalog: browses
- Study Catalog → Market Comparison: lists
- Study Catalog → Damage Classification: lists
- Study Catalog → Claim Cost Prediction: lists
- Study Catalog → Multi-Agent Analysis: lists
- Study Catalog → Legacy Code Migration: lists
- Practitioner → Study Materials: runs or reviews
- Practitioner → Submission Templates: uses to contribute