Why in the News
Sharper catastrophe models now show which street, factory or farm a disaster will hit, but most of India’s disaster losses remain uninsured. A deficient 2026 monsoon under a very strong El Niño shows how weather becomes a financial problem. The question is whether better data will only raise prices or also cut risk.
What do catastrophe models and insurance do?
- What insurance does: A household, farmer or firm pays a premium so an insurer absorbs much of a disaster loss, making a large, uncertain loss manageable.
- What a catastrophe model does: It does not predict the next flood or cyclone. It estimates how likely losses of different sizes are, like a credit score for a place’s disaster risk.
- Pricing role: Insurers use the models to set prices, so when modelled risk rises, premiums rise too.
- Uninsured exposure: Swiss Re estimates that 93% of India’s natural catastrophe exposure was uninsured in 2022.
- The takeaway: Climate risk is not only physical damage; it is also the rising price of living and doing business in exposed places.
How have the models changed?
- From regions to locations: Flood and cyclone models have moved from broad risk zones to precise sites, capturing intense rainfall, urban drainage, local topography, river systems and building design.
- Street level risk: A factory beside a river carries more risk than one on higher ground nearby.
- National flood model: RMSI, an Indian risk firm, simulates 80,000 flood events across 28 major river basins.
- Limited use: Aon‘s 2025 survey found models used mostly for reinsurance (insurance that insurers buy) and risk concentration, less in everyday underwriting (deciding whom to cover and at what price).
Why can better information hurt the insured?
- Worse numbers: A site a model finds more exposed has not become riskier; only the knowledge has changed. Yet its premiums may rise, and insurers may cover less of the loss.
- Reinsurance cushion: Insurers with enough reinsurance need not withdraw, but a surprise rise in risk can cut cover and raise prices quickly.
- Aggressive pricing: Insurers racing for market share kept prices low as risks changed, warns a former head of Raheja QBE General Insurance. Models are improving faster than careful pricing (underwriting discipline).
- Cross category losses: A disaster’s losses spread beyond one policy type into health, pensions, agriculture, supply chains and future generations.
How does India share disaster losses?
- Crop insurance scale: The Sixteenth Finance Commission says the Union and States pay 85 to 90% of premiums under the Pradhan Mantri Fasal Bima Yojana (PMFBY), the national crop insurance scheme.
- Low penetration: Only about 40% of farmers were covered in the eight years to 2024-25.
- Nagaland’s parametric cover: Nagaland pioneered parametric disaster insurance, which pays automatically once rainfall crosses a set threshold. It built the scheme with its disaster management authority, SBI General, Munich Re and GIC Re.
- Speed, not prevention: Parametric payouts reach people without assessing each loss, but cannot prevent the disaster itself.
- Risk pool: The United Nations Development Programme (UNDP) has worked with the National Disaster Management Authority (NDMA) on a National Catastrophe Risk Insurance Pool to pool public resources for wider protection.
Challenges
- Thin non crop cover: Disaster insurance beyond crops is at an early stage, held back by limited products, low awareness, and poor affordability and access.
- Global protection gap: Uninsured disaster losses, the protection gap, reached $424 billion worldwide in 2025, and India sits at the exposed end.
- Who pays: Models measure risk but cannot decide who should bear its cost.
Way Forward
- Invest in adaptation: Fund better drainage, stronger buildings and resilient farming to cut the risk that must be insured.
- Risk based land use: Use street level flood maps to keep costly infrastructure out of harm’s way.
- Operationalise the pool: Launch the national catastrophe pool so governments, insurers and reinsurers share large losses.
- Extend parametric cover: Other disaster prone States should adopt Nagaland’s model.
Conclusion
India is learning to measure disaster risk precisely, but measurement alone leaves the cost on households, farmers and governments. The test is whether the new risk maps are used to build safer places and cheaper protection, not just to send a bigger bill.
Key numbers
- Property exposed to natural catastrophes in India (Swiss Re): $26 trillion to $29 trillion.
- RMSI flood model coverage: 3,400 sub basins; more than 19,000 pin codes.
- Adaptation returns (World Resources Institute): 320 investments in 12 countries yield over $10.50 per dollar over 10 years.
Matching Previous Year Question
“[2026, GS3, 10 marks] Discuss how the contradiction between ‘rapid infrastructure development’ and ‘disaster-risk reduction’ in ecologically-sensitive areas of India can be managed, with suitable examples.”
