Disaster Risk Transfer Parametric Insurance Solution (DRTPIS) (2024): Assessing Nagaland’s Shift Towards Disaster Risk Financing (2024

Policy Update
Ayan Bordoloi

Background

Nagaland has implemented Disaster Risk Transfer Parametric Insurance Solution (DRTPS), commonly known as DRTPIS, which represents a shift from a mostly public expenditure approach on disaster towards the creation of disaster risk capacity beforehand. The distinction matters. Disaster risk financing is not about not having the rain or flooding or landsliding happen, it’s about whether governments have already pre-arranged the liquidity when a hazard occurs that puts the fiscal system under pressure. The programme for Nagaland 2024-2027 is thus an intersection of disaster management, public finance, insurance and climate adaptation.

The policy is a structural exposure response. Nagaland is a hilly multi-hazard state with frequent and high intensity rainfalls during monsoons that can result in flashfloods, landslides, road failures, house flooding and damages in agricultural fields and public structures.

Nagaland is a state that is vulnerable to many natural calamities; according to the damage reports of the Nagaland State Disaster Management Authority (NSDMA) in 2024, during heavy rainfall episodes, roads, bridges, houses, paddy fields, jhum cultivation and other assets suffered damages. For instance, in July 2024, NSDMA had reported damage and disruption in Tuensang, Kiphire, Mokokchung, Noklak, Zunheboto, Shamator and Peren districts. But the problem with public finance is not just the physical risk, it is the need to mobilise funds quickly for rescue, relief, restoration and reconstruction.

Conventional disaster funds fall short of the institutional issue. The State Disaster Response Fund (SDRF) along with the National Disaster Response Fund (NDRF) might not be able to provide resources in large quantities and at the same speed after a major disaster, as noted in the NSDMA’s 2024 procurement document.

The idea of the proposed insurance was thus meant to complement and not supplant public disaster funds. The purposes it was designed for were to provide a more speedy response in emergency situations, more meaningful compensation, and the opportunity to rebuild infrastructure ‘build back better’. The framing is crucial given the importance of evaluating parametric insurance as a component of a larger financing framework, not as a replacement for prevention, preparedness or fiscal transfers.

Nagaland was not directly transitioned from no insurance to a state-based insurance program. It initially tried out a sub-sovereign extreme-precipitation insurance programme with the support of the InsuResilience Solutions Fund (ISF) for the three-year pilot phase from 2021 to 2023, which included NSDMA, Tata AIG, Swiss Re and Faber Consulting.

The pilot was implemented in selected disaster prone areas at a cost of around ₹5 crore, as compared to an annual premium of ₹70 lakh. Although there was heavy rainfall and flooding during the pilot period, it did not produce any payout. A follow-on study uncovered a major deficit – the data set and triggers were not aligned with the local rainfall. Data choice and trigger calibration were key aspects of the redesign that emerged from the experience.

The 2024 programme was then expanded on a geographical and institutional field basis. In February 2024, NSDMA announced an international procurement process to seek a multi-year parametric weather insurance solution. The top priorities for the EOI were automated ground weather stations, multi-year coverage, experience account or no/low-claims bonus and a goal of getting to NSDMA within 2 weeks of a triggered payout. The final stage of the procurement process involved selecting the SBI General Insurance as an insurer, and GIC-Re India and Munich Re as reinsurers, all identified by NSDMA. The formal MoU was signed on 2 August 2024, and NSDMA’s programme note indicates that it will be effective from 1 June 2024 to 30 May 2027.

In 2024, the public announcement stated that the total insurance cover for three years was ₹150 crore and the annual insurance cover by the government is ₹4.20 crore, which means after 3 years, it will be ₹12.60 crore. According to another report by Mongabay India, the premium for the arrangement was ₹4.5 crore per year as of 2025.

The two figures should not be assumed to be the same (silently) because the executed policy schedule is not attached to the core NSDMA documents reviewed in this article. The ₹4.20 crore figure is taken here as the reported financial architecture for 2024, the latter ₹4.5 crore figure as a later reported value that needs to be substantiated with the terms of the executed policy. This distinction is also relevant for public accountability: the requirements for disaster-risk financing call for transparency in disclosing premiums, limits, triggers and payout rules.

Functioning

The parametric insurance contract is not an indemnity insurance, but rather pays through an objective measure rather than a detailed measurement of each loss. For the case of Nagaland, rainfall is the key measurable trigger. After a rainfall amount is agreed upon, a set schedule of payments is made. This method can thus provide immediate “liquidity” without a full loss survey after the disaster. This is especially useful when roads are closed, and there is little physical damage assessment and administrative capacity.

With the data in the middle of the product, the design of the procurement for 2024 had data at the heart of the product. NSDMA wanted to apply the maximum possible relevance to ground weather station observations, and specifically requested the new automated weather stations developed under the previous ISF backed programme to be used for the new insurance. Subsequent descriptions of the scheme re-design indicate usage of the gridded rainfall data from the IMD and the state all-weather network of 34 stations.

The shift from a less locally calibrated to a state-level weather dataset is more than a mere technical change; it is an effort to lower the basis risk (the risk that the measured index does not correlate closely enough to the actual loss in a locality).

The scheme has a statewide risk architecture whilst the payments are organised via local risk differentiation. According to the revised product, the tehsils are divided into high flood risk, medium flood risk and low flood risk zones, each of which has an amount of sum insured predetermined. This design takes into account that it is not the same amount of rain that results in the same fiscal exposure throughout. The best use of a rainfall-index product will thus be when the trigger and insured amount are geographically fine-grained, such as to capture meaningful differences in risk.

Figure 1: Expansion in the scale of Nagaland’s parametric insurance cover.

The earlier 2021–2023 pilot covered selected disaster-prone areas for approximately ₹5 crore; the 2024–2027 DRTPS was publicly announced with ₹150 crore of three-year cover.

Sources: Policy Consensus Centre (2025); NSDMA (2024).

The revised trigger system has been reported to public domain, which would result in a first partial pay out of 10 per cent of the sum insured of the relevant tehsil if the monsoon rainfall reaches 1,500 mm. NSDMA officials have termed the monsoon trigger window as June to October, with monsoon rainfall thresholds for the non-monsoon period also being created. The actual wording of the contract should always be considered before the secondary descriptions, but the architecture shows the essence of the contract: the state will pre-agree relationship between the measurable intensity of the hazard and the quantity of liquidity released.

The role of the institutional chain is also important. NSDMA is the policyholder and is responsible for coordinating the mechanism of financing disasters, while the insurer accepts the contractual risk, reinsurers share part of the insurance policyholder’s risk, weather data serve as the evidence for triggering the disaster and the state’s Decentralised Relief Payment System has been established as a channel for bringing funds to the beneficiaries.

NSDMA’s larger digital disaster-management framework encompasses the Nagaland State Disaster Management Information System and the Decentralised Relief Payout System, which have been rolled out in advance of the 2024 insurance renewals. The latter employs the blockchain technology in its payment system. So while the use of DRTPS is tied to the insurance contract, it is also based on a link between data, trigger verification, transfer of finance and last-mile relief.

This architecture also provides an understanding of what the scheme does not do. It does not provide protection for each specific home against all events like a traditional homeowners’ insurance. It establishes the fiscal capacity of the state contingent to certain parameters and limits.

A payout may not be made if the disaster causes a lot of damage on the local level but does not result in the required rainfall or other index conditions specified in the contract. On the other hand, a trigger can be triggered even if individual losses vary significantly. The point is that parametric insurance takes the heavy lifting of loss assessment, in exchange for the uncertainty of imperfect correlation between the index and the real loss.

Performance

The key performance measure for 2024-2027 is that it is out of the design stage and now a payout. NSDMA has announced the settlement of the first Cover 1 claim for 2024-2025 in March 2025. The amount was ₹1,06,50,000, or ₹1.065 crore, associated with the Monsoon 2024 period. The money would be provided to the affected beneficiaries under the Decentralised Relief Payment System of the NSDMA, it said. It is a different policy to a ‘paper’ one—the contract was called into action, the claim was paid and the state had a way to pass on payments.

A first payout in itself does not provide any real idea of the overall effectiveness of the programme, and does not indicate whether the payout was enough to restore damaged assets or livelihoods. A key performance question is whether funding comes quickly enough, to the right target group, and eases the burden on SDRF/NDRF funds and supports recovery. An intensive assessment, therefore, requires data on the date of triggering, the date of settlement, the date of disbursement, the number and location of the beneficiaries, the average payout, administrative costs and the relationship between payout size and verified disaster impacts.

Figure 2: 2024 announced annual premium compared with the first reported Cover 1 payout for the Monsoon 2024–25 period. The premium figure is the amount reported in the 2024 public announcement; the claim figure was announced by NSDMA in March 2025. Sources: NSDMA (2024); MorungExpress (2025).

The scale of the scheme’s finances is impressive, nonetheless. The three-year agreement will be at a cover of ₹150 crore, whereas in the previous pilot, there was a small cover of ₹5 crore in selected areas. This is an enormous extension in geography and on the nominal side of the risk transferring capacity. It also brings a new twist to the policy debate: not that Nagaland can experiment with parametric insurance, but if a state-level product is to be sustainable, it needs to have credible triggers and have the fiscal needs gap minimized between insured liquidity and fiscal needs.

The first one is also a reminder of the role of payment system infrastructure. NSDMA has clearly said that the claim would be sent directly to the beneficiaries via its decentralised digital payment system. A parametric contract can raise up capital swiftly, but the speed is compromised with manual and disjointed verification and identification of the beneficiaries and payment processing. Subsequently, settlement of insurance claims is linked to a pre-existing digital relief system and hence governance value. It can provide traceability from hazard measurement to insurer payment to beneficiary receipt.

In the discussion on disaster risk financing, the Sixteenth Finance Commission has, in turn, acknowledged the experience of Nagaland. It says that its report covers a comprehensive insurance coverage of the state against extreme rainfall and how automated weather stations help determine excess-rainfall triggers. The Commission also reminds that insurance mechanisms must be reviewed for their affordability, applicability and customisation and that innovative means of financing premiums are needed. The takeaway is that the experience in Nagaland has moved from the state level to the national level, and is still in the early stages of implementation, so its cost and scalability remain to be assessed.

Impact

Financial resilience

DRTPS’ main effect is the shift to a new timing of public financial capacity. Traditional expenditure on disaster is activated primarily when it occurs after the damage is visible. By shifting a portion of the financing decision to before disaster, parametric insurance places a premium for a payout if a disaster of a particular magnitude occurs. This can help to lessen the burden upon the state’s resources right after a major rainfall event. It can also safeguard fiscal space to accommodate higher expenditure on other priorities during times of significant increases in disaster expenses. The policy is thus best interpreted as being for liquidity support, not full compensation for loss of assets.

Resilience of data and institutions

The insurance programme has also enhanced weather observation as a public policy asset. The first pilot identified that a technically sound index can be a poor financing tool if the data it is based on are not sufficiently reflective of local conditions. The product was redesigned in 2024 to focus on automated weather stations and official rainfall data. This would provide a positive incentive to ensure station coverage, data quality, calibration and continuity. Infrastructure so created are not only valuable for insurance purposes, but can also be useful for early warning, planning, emergency operations, and climate-risk analysis.

DRTPS relates to the state’s digital relief architecture, which results in a second institutional effect. NSDMA had already put in place NSDMIS and a decentralised payout system prior to the insurance arrangement in 2024. The insurance scheme can then be part of a larger disaster information chain, not be a separate financial contract. With proper management, this can enhance the audibility of the process: a trigger can be associated with a documented rainfall record, the insurer’s settlement can be noted down and transfers to beneficiaries can be matched to the approved settlement amount.

Resilience of communities and recovery

The programme’s declared purpose isn’t just to keep governments’ balance sheets in order. NSDMA presents the solution as a means to safeguard the people and critical infrastructure, and enhance the rapid response capability and resilience of rebuilding the state “better. The first payment indicates that people impacted by rainfall-related events may receive insurance benefits via decentralised payment system. This can be especially important in a mountainous state where physical access may rapidly decrease following landslides and flash floods. But the level of social protection will depend on the distribution formula and whether those who are able to access this protection via the payout mechanism will be those with the highest unmet needs for recovery.

The policy also has a wider demonstration effect. The Sixteenth Finance Commission’s deliberations on Nagaland suggest the sub-sovereign parametric insurance has been put on the agenda in the disaster-risk financing debate of India. However, the replication should not be of the kind mechanical replication of the Nagaland contract. It’s not just about having an insurance policy, it’s about the process: Risk Assessment, Local Data, Open and Transparent Procurement, Predefined Triggers, Reinsurance and Rapid Payment. States with different types of hazards, financial resources and weather data networks would require different designs.

Emerging Issues

Basis risk and trigger calibration: The key technical hurdle is linking the index and the loss. While rainfall can be measured and quickly confirmed, a house could be severely damaged in the absence of a measurement at the appropriate station or grid. The opposite is also possible: a threshold may be breached in an area where the losses are relatively small. The experience of the earlier pilot established the need for trigger calibration to be an ongoing process, rather than an actuarial exercise, in Nagaland. The density, representativeness, spatial interpolation, continuity and changes in rainfall should then be assessed periodically.
Affordability and fiscal value: The state pays the premium regardless of whether a payout is made. This is the only trade-off in insurance. The 2024 announcement recorded ₹4.20 crore as the annual amount and three years to cover the loss, which was later followed by ₹4.5 crore as the annual amount. In either case, the state will have to make a comparison of the price of premiums with the anticipated value of a quicker liquidity, the odds of triggering, the magnitude of the fiscal shock avoided and the administrative costs of other financing alternatives. It’s not about whether insurance has a positive return each year, it’s about how insurance transfers risk. The concern is whether that transfer cost is warranted based on the state’s risk level and budgetary limitations.
Parametric insurance is not about covering each and every rupee lost: It offers liquidity ahead of time, upon reaching a threshold. Even with a full payment, it is possible for a significant protection gap to remain in the event of a large disaster. The scheme should be made available as a component of a financing plan which makes use of SDRF/NDRF resources, budgetary contingencies, public infrastructure maintenance, etc. social protection and, if applicable, other insurance instruments. In overstating the scope of paramedic interventions, there can be unrealistic expectations for both the administrator and the affected community.
Governance and transparency: The key parameters are publicly financed, as the policyholder is a public body; the state is well motivated to publish the key parameters as a matter of accountability. A public disclosure should contain the following elements: amount of insurance per risk zone, triggering thresholds, source of information, premium, policy term, exclusions, payout layers, claims paid and the time from trigger to beneficiary transfer. This disclosure would enable a review of the insurance’s effectiveness and enable other states to understand the transferable lessons while also being able to understand the design choices made in Nagaland.
Equity and last-mile access: Recovery through rapid digital payment does not necessarily equate with equity. Residents of remote or disrupted areas may have less access to documentation, banking and/or administrative facilities. The state thus requires a grievance and verification process that will not give rise to an overly complex beneficiary procedure in the case of a parametric payment. The digital system must cut down administrative lag, but not make it harder to process. Monitoring should also include if women, small farmers and informal workers, and households in very exposed areas are able to access the insurance support that the insurance mechanism has generated in a timely manner.
Beyond 2027: The current programme will end on 30 May 2027. Don’t consider renewal as an automatic extension. The state should continue to develop an evidence-based case for claims, trigger frequency, appropriate payouts, quality of data, and changes in premiums and outcomes for beneficiaries during the policy period. Then a ‘renewal tender’ may take on lessons learnt from actual performance. This would create a learning cycle for the 2024-2027 term and not just another insurance year.

Way Forward

Establish a public dashboard of disaster risk financing: NSDMA should be publishing a short, simple dashboard outlining the policy period, the amount of premium paid, the sum insured by risk zone, the trigger level, the number of claims that were triggered, the number of claims that were settled, time between trigger and settlement, amount of money transferred, and geographic distribution of beneficiaries. It should be clear on the dashboard that the insurance proceeds are separate and apart from SDRF/NDRF and other government relief. This would enable citizens and policy-makers to determine if risk transfer is complementing public disaster finance.
Annualize the trigger calibration: Review rain thresholds against rainfall observations, flood reports and loss information after the event. The review should not simply be a mechanism for reducing thresholds simply because a payout does not happen, or risk becoming actuarially unsustainable. But NSDMA must consider the statistical association between rainfall intensity and fiscal loss, between tehsils and between monsoon and non-monsoon seasons. Confidence in the process can be enhanced by independent technical review.
Display the executed policy schedule and premium structure: The difference between the ₹4.20 crore per annum premium given in the public announcement in 2024 and the €4.5 crore per annum premium that was later reported indicates the need for a single authoritative source that is publically available. The government should release the policy schedule or an official summary detailing premium, insured limits, layers, exclusions, data sources and payout rules. It is especially crucial to highlight transparency in the purchase of private risk transfer with public funds.
Connect insurance with all of the disaster financing tools in the disaster financing toolbox: Connect DRTPS with SDRF/NDRF, Contingency funds, Departmental budgets, and reconstruction financing. There needs to be a clear role for each layer, such as immediate liquidity via parameters, public funds for wider relief needs and longer-term budgetary or external funding for reconstruction. This helps to avoid the duplication and overreliance on insurance.
Improve last mile payout control: The decentralised digital payment system should be connected to clear protocols for identification of beneficiaries, grievance redress, correction of bank details and audit trails. The state should issue overall data on the number of beneficiaries who have received payments and the speed of the payments. Even if a building physically prohibits access to the Internet, the district administration must continue to provide and maintain alternate assisted access to the Internet, which should be an enabler, not a barrier.
Consider social outcomes, not just financial transactions, when assessing the impact of insured liquidity over the 2024-2027 period: Assess whether insured liquidity decreases recovery time, eliminates the need for distress borrowing, restores critical services faster and keeps livelihoods safe throughout the 2024-2027 period in the future. These are more useful than simply the number of claims. A policy may have few claims, due to either limited disasters or high triggers, or due to good transfer of risk, which should be distinguished on the basis of evidence.
Formulate a calibrated plan for 2027 and beyond: By 2027, Nagaland may be able to consider renewing the current rainfall cover, tweaking the layers in the cover, adding carefully selected hazards, or create a standalone agro-parametric product. Any expansion should be based on hazard frequency, fiscal exposure, data quality and willingness to pay. The key takeaways from the experience of Nagaland are that the risk transfer is best delivered when the public authority is aware of its own risk, has control over the design requirements, has credible risk data and views insurance as a piece of a larger resilience system.

The 2024-2027 DRTPS of Nagaland is thus not disaster relief, but an exercise in prior arrangement of public funding. Its value is that it represents an institutional change—the state tried to turn part of an unknown and unpredictable disaster budget into a premium, and a source of quick liquidity that’s stipulated in the contract.

This architecture is demonstrated to be working in practice with the first payout. A much harder question is whether the state can maintain data accuracy, afford premiums, set the trigger, and ensure vulnerable beneficiaries and clearly show value by the end of the policy term. Under such circumstances, the experience can provide valuable lessons that can further the broader transition from post-disaster financing to risk-informed disaster-risk financing in India.

References

1. InsuResilience Solutions Fund. (2024, June 17). ISF continues to support Nagaland’s climate risk insurance program with further premium subsidy support. https://insuresilience-solutions-fund.org/2024/06/17/isf-continues-to-support-nagalands-climate-risk-insurance-program-with-further-premium-subsidy-support/

2. Ministry of Home Affairs. (2026). Annual report 2024–25. Government of India. https://www.mha.gov.in/sites/default/files/AREnglish_24032026.pdf

3. Mongabay India. (2024, June 20). India experiments with parametric insurance to mitigate costs of disasters. https://india.mongabay.com/2024/06/india-experiments-with-parametric-insurance-to-mitigate-costs-of-disasters/

4. Mongabay India. (2025, June). First payout under extreme-weather insurance triggers relief and intrigue. https://india.mongabay.com/2025/06/first-payout-under-extreme-weather-insurance-triggers-relief-and-intrigue/

5. MorungExpress. (2025, March 21). Nagaland settles 1st parametric insurance claim under DRTPS for Monsoon 2024. https://morungexpress.com/nagaland-settles-1st-parametric-insurance-claim-under-drtps-for-monsoon-2024

6. Nagaland State Disaster Management Authority. (2024, February 7). Expression of interest (EoI) for the implementation of a multi-year parametric weather disaster risk insurance scheme (PDRIS). Home Department, Government of Nagaland. https://nsdma.nagaland.gov.in/sites/default/files/2024-02/EOI%20(2).pdf

7. Nagaland State Disaster Management Authority. (2024, May). Announcement: Disaster Risk Transfer Parametric Insurance Solution (DRTPIS). Home Department, Government of Nagaland. https://nsdma.nagaland.gov.in/sites/default/files/2024-05/Announcement.pdf

8. Nagaland State Disaster Management Authority. (2024, July 4). NSDMA informs on damage caused by incessant rain in the State. Home Department, Government of Nagaland. https://ipr.nagaland.gov.in/node/13800

9. Nagaland State Disaster Management Authority. (2026). Disaster Risk Transfer Parametric Insurance Solution (DRTPS) Nagaland. Home Department, Government of Nagaland. https://nsdma.nagaland.gov.in/sites/default/files/2026-03/Disaster%20Risk%20Transfer%20Parametric%20Insurance%20Solution%20Nagaland_compressed_0.pdf

10. Nagaland State Disaster Management Authority. (2026). Towards a safer and resilient Nagaland: What do we do? Why we do? And how we learn? Southasiadisasters.net, 20(10). https://nsdma.nagaland.gov.in/sites/default/files/2026-04/Towards%20a%20Safer%20and%20Resilient%20Nagaland.pdf

11. National Institute of Disaster Management. (2025). Disaster Risk Transfer Parametric Insurance Solution (DRTPS) in Nagaland. Ministry of Home Affairs, Government of India. https://nidm.gov.in/PDF/TrgReports/2024/November/Report_28November2024ga.pdf

12. Sixteenth Finance Commission of India. (2026). Report of the Sixteenth Finance Commission, Volume I. Government of India. https://www.indiabudget.gov.in/doc/16fcvol1.pdf

13. Tripathi, B. (2025, May 16). Q&A: How a small Indian state overcame parametric insurance hurdles. Context / Thomson Reuters Foundation.

About the Contributor

Ayan Bordoloi is an intern with IMPRI, currently pursuing his master’s in political science at the University of Delhi. His research interests lie in federalism, tribal governance, and Northeast India’s political landscape.

Acknowledgements

The author extends sincere gratitude to the IMPRI team for their guidance and support, along with the reviewers Pritha Chowdhury and Prisha Sachdeva for their valuable feedback and insights.

Disclaimer: All views expressed in the article belong solely to the author and not necessarily to the organization.