Smart Data Use Could Transform Global Flood Insurance Systems

As floods intensify worldwide, researchers at the University of Arizona have found that the choice of data in flood insurance programs can make the difference between timely, fair payouts and costly delays.

Their study, published in Earth’s Future, shows that combining multiple data sources—including artificial intelligence–powered satellite models—offers insurers and governments a clearer picture of disaster risks, potentially lowering costs and improving resilience for millions in flood-prone regions.

phys.org

1. Growing risk of floods – Floods are becoming more frequent and destructive, increasing demand for more reliable insurance solutions.
2. Research in Bangladesh – The team tested a simulated flood insurance program during monsoon seasons from 2004 to 2023, making Bangladesh an ideal case study.
3. Five data sources – Researchers compared rainfall records, river gauge levels, national flood maps, traditional satellite imagery, and an AI-based satellite model.
4. Limits of proxies – Insurance often relies on indirect proxies (like rainfall or river height), but these do not always accurately reflect real flood damage.
5. AI breakthroughs – The AI-powered model tracked floods even during heavy cloud cover, outperforming traditional satellite data.
6. Faster payouts – With AI, payouts could be triggered up to one week earlier, offering critical financial relief to vulnerable households.
7. Lower uncertainty – The AI model reduced payout prediction uncertainty by over 20%, cutting costs for insurers and customers alike.
8. No single dataset is perfect – Each data source has strengths and weaknesses; combining them provides the most reliable decisions.
9. Global insurance gap – Between 2000 and 2023, only 16% of $1.77 trillion in global flood damages were insured, underscoring the need for reform.
10. Policy recommendation – Index-based insurance programs should rigorously test and integrate multiple datasets, including AI, to ensure timely, fair, and cost-effective payouts.