Claims Scenario Analysis & AI Evaluation (Project-Based)
Evaluated auto insurance claim scenarios for coverage accuracy and logical consistency to support AI evaluation. Developed FNOL-style test cases containing contradictions and decoy information to assess the robustness of AI decision logic. Reviewed and documented scenario outcomes with clear answers, rationale, and calculation steps for downstream evaluation workflows. • Coverage-scope determination for collision vs. comprehensive • Fraud indicator identification (e.g., late reporting, inconsistent damage narratives) • Payout-calculation validation using deductible and coverage-limit reasoning • Structured test-case documentation focused on compliance and accuracy