The insurance industry has more experience pricing climate risk than any other sector in the financial system. Generations of underwriting data on weather events, catastrophe losses, and long-tail liabilities have accumulated with actuaries that no other industry maintains. Insurers, therefore, possess expertise that makes them uniquely positioned to lead the ESG transition, albeit also uniquely exposing them to the financial consequences when those historical patterns change.
Natural disaster losses have totalled close to USD 7 trillion through the four decades ending in 2024, with around two-thirds uninsured. For every insured dollar in weather-related losses, governments, businesses, and communities absorb a further USD 3 to 4 in uninsurable costs. ESG analytics and reporting services are giving insurers the tools to narrow this protection gap. It is a strategic capability reshaping underwriting, investment, and long-term financial resilience.
The limits of historical loss data
Underwriting has always depended on historical loss patterns as a reliable guide to future exposure. Climate change does not fit into that assumption. Insured catastrophe losses exceeded USD 137 billion globally in 2024, driven not just by individual event severity but by the accelerating frequency with which high-cost events are now occurring. In 2023, total economic losses from natural catastrophes reached USD 380 billion, with only USD 118 billion insured. The pricing models built on historical actuarial data cannot account for the tipping points and non-linear escalation that climate science identifies.
Scenario analysis frameworks model potential climate futures across a range of warming pathways and socioeconomic trajectories, supporting more defensible pricing, more accurate reserve calibration, and a more credible basis for the disclosures that supervisors and investors increasingly expect to scrutinise.
Physical and transition risk
Insurers deal primarily with acute physical risks, such as hurricanes, wildfires, and floods, that strain claims operations and test reserve adequacy under short timelines. Granular risk assessment is the core requirement: flood or fire exposure between two neighbouring properties in the same postcode can differ substantially depending on elevation, construction materials, and surrounding conditions. Pricing that cannot account for this granularity systematically misprices exposure.
Life and health: chronic physical risk
Life and health insurers face a distinct set of challenges. Chronic physical risks, such as heat-induced mortality, deteriorating air quality, and shifting disease burden, often develop over longer timescales and carry sustained implications for life expectancy assumptions, product pricing, and claims development. These are not well served by catastrophe modelling frameworks designed for acute events. Localised data studies that refine morbidity and mortality projections remain an analytical gap for many life and health insurers.
Asset management: transition risk
As economies shift toward lower-carbon systems, the value of carbon-intensive assets in insurer investment portfolios can deteriorate through regulatory pressure, market repricing, or demand shifts. ESG tools that map which holdings face transition exposure, and under which scenarios, give investment teams a more complete view of long-term portfolio risk.
The three planning horizons
Effective climate risk modelling requires different approaches across different time horizons.
Over a short horizon of zero to three years, the primary objective is refining underwriting practices and pricing to reflect near-term physical risk accumulation.
Over a medium horizon of four to eight years, the focus shifts to reserving decisions and risk-adjusted capital planning under stress. Over a long horizon of ten years and beyond, the outputs inform strategic planning, capital return expectations, and alignment with regulatory net-zero pathways. Treating climate scenario analysis as a single-output compliance exercise rather than a multi-horizon strategic planning input limits its value and misrepresents what regulators are now expecting to examine.
Beyond risk management: product innovation
The protection gap that climate change is widening is also an addressable market. Parametric insurance, where payouts are triggered by a measured physical parameter rather than assessed loss, offers a mechanism for covering perils where traditional indemnity products cannot be written sustainably.
Products like catastrophe bonds depend on the same analytical infrastructure as climate risk modelling. This includes scenario outputs, hazard projections, and data-driven trigger calibration. ESG analytics and reporting services not only support compliance but also enable the product innovation that creates viable coverage in otherwise retreating markets.
Governance, disclosure, and the ESG reporting challenge
Governance determines whether analytical capability translates into operational decisions. In a global survey of more than 200 insurance professionals conducted by the UNEP Finance Initiative, only one in four respondents had any internal guidance on ESG issues in place. That governance gap remains a structural constraint even as modelling capability advances.
Disclosure requirements are tightening across major jurisdictions. Frameworks such as the TCFD (consolidated into ISSB in 2023-24) form the regulatory baseline in North America, Europe, and Asia-Pacific. Supervisors are now assessing how insurers’ climate risk models integrate into strategy, what board-level oversight governs them, and how disclosures evolve as analytical maturity develops. The burden of proof has shifted from demonstrating awareness of climate risk to demonstrating how it shapes decisions, documented and auditable across the full underwriting and investment cycle.
How can Infosys BPM help with insurance climate risk analytics and ESG modelling?
Through its ESG analytics and reporting services expertise, Infosys BPM helps insurance organisations build the operational infrastructure that makes climate risk modelling actionable. It embeds analytical outputs into core business processes and sustains the governance discipline that regulators and investors expect to see in every review of ESG performance.
Frequently asked questions
Climate risk analytics in insurance is the process of using climate, exposure, and portfolio data to understand how physical and transition risks affect underwriting, reserving, and investments. It helps insurers price risk more accurately and prepare for long-term changes in loss patterns.
Insurers already have deep historical data, actuarial expertise, and risk-pricing experience, which makes them well placed to connect climate science with financial decision-making. That position allows them to improve underwriting, strengthen resilience, and support more credible ESG disclosure.
Climate change makes historical loss patterns less reliable because severe weather events are becoming more frequent and more costly. Insurers need scenario analysis and granular exposure data to avoid systematic underpricing in regions or segments with rising physical risk.
ESG modelling helps insurers identify which assets are exposed to transition risk, such as policy shifts, carbon pricing, or stranded asset risk. This gives investment teams a clearer view of portfolio vulnerability under different climate scenarios.
Insurers should start by linking climate scenarios, underwriting data, and governance processes across short-, medium-, and long-term planning horizons. They should also ensure climate insights are auditable, embedded into decision-making, and aligned with disclosure expectations.
Climate risk analytics in insurance is the process of using climate, exposure, and portfolio data to understand how physical and transition risks affect underwriting, reserving, and investments. It helps insurers price risk more accurately and prepare for long-term changes in loss patterns.
Insurers already have deep historical data, actuarial expertise, and risk-pricing experience, which makes them well placed to connect climate science with financial decision-making. That position allows them to improve underwriting, strengthen resilience, and support more credible ESG disclosure.
Climate change makes historical loss patterns less reliable because severe weather events are becoming more frequent and more costly. Insurers need scenario analysis and granular exposure data to avoid systematic underpricing in regions or segments with rising physical risk.
ESG modelling helps insurers identify which assets are exposed to transition risk, such as policy shifts, carbon pricing, or stranded asset risk. This gives investment teams a clearer view of portfolio vulnerability under different climate scenarios.
Insurers should start by linking climate scenarios, underwriting data, and governance processes across short-, medium-, and long-term planning horizons. They should also ensure climate insights are auditable, embedded into decision-making, and aligned with disclosure expectations.
