Benchmarking Flood Insurance Instruments

Multiple parametric instruments for flood coverage exist, each employing unique triggers, payout speed, or coverage biases. This Quest demands a data-driven benchmark to assess how each solution performs against real flood event sets, cost feasibility, and distribution equity. Contributors do not rely on HPC references here but employ advanced data analytics or containerized batch scripts to systematically evaluate event matching.

Key Outputs

1. Benchmark Matrix: A comparative table with metrics such as false triggers, coverage overlap, average lead time, or cost ratio
2. Performance Logs: Summaries of each instrument’s event detection accuracy
3. RRI Reflection: Checking for social or geographic biases in coverage

10 Steps

• Instrument Collection: Retrieve 2–3 parametric scripts or CSV-based rule sets from the open finance library
• Historic Flood Data: Identify major events with recognized severity (discharge rates, inundation extents)
• Data Normalization: Align each parametric script’s expected input with the real event data, awarding eCredits for the initial setup
• Comparative Test Runs: Execute a batch-based simulation for each instrument across multiple flood events
• Scoring & Ranking: Evaluate success rates, missed payouts, or excessive triggers in a consolidated chart
• Peer Community: Post partial results for domain or local finance input, awarding partial pCredits upon collaborative refinement
• Sensitivity Analysis: Tweak thresholds for borderline events and observe performance shifts
• RRI Overlay: Summarize disclaimers or coverage illusions, especially if certain instruments systematically ignore less-documented flood zones
• Draft Benchmark Summary: Merge all findings into a final matrix with recommended instruments or disclaimers
• Validation & Publication: On acceptance by parametric finance leads, partial vCredits confirm your thorough multi-instrument benchmark’s success