This article is written by James d’Ath

Nature-related degradation is increasingly material to insurance balance sheets. Biodiversity loss, ecosystem decline, water stress, soil degradation and land-use change are no longer distant environmental concerns. They are amplifiers of physical risk, liability exposure, supply-chain disruption, sovereign instability and ultimately insured loss. Yet while insurers are investing heavily in climate and catastrophe analytics, nature-related risk remains weakly integrated into core underwriting and risk management processes.

A central reason is not lack of data, but lack of forecast validation. Many nature-related risk assessments rely on static indicators, scenario narratives, or qualitative scores that describe exposure but do not test whether forward-looking assumptions are accurate. Models generate outputs, but there is limited infrastructure for learning which forecasts perform well under real-world conditions and which systematically misprice risk.

Insurance, however, is built precisely on this discipline. Actuarial credibility depends on calibration, back testing and continuous refinement. Models that fail to predict loss experience are adjusted or retired. Those that perform well are embedded into pricing, reserving and capital allocation. The challenge for nature-related risk is therefore not conceptual, it is methodological.

This is the context in which Prediction Markets for Nature-Related Risk: A Mechanism for Outcome Based Validation and Forward-Looking Governance proposes a new tool: accuracy-weighted forecasting systems designed to evaluate ecological risk in the same way insurers already evaluate catastrophe and climate risk.

Prediction markets as forecast validation infrastructure

In this framing, prediction markets are not speculative instruments. They are probabilistic inference systems designed to aggregate distributed information into a continuously updated probability estimate for a clearly defined future outcome. For example, whether mangrove cover will fall below a threshold, whether watershed quality will deteriorate, or whether restoration interventions will meet specified performance criteria. What distinguishes these systems from conventional ESG or nature-risk indicators is that forecasts are explicitly evaluated ex post. Forecast quality is assessed using proper scoring rules (such as Brier or CRPS), allowing insurers to measure calibration, bias and skill relative to declared baselines. Over time, this produces an empirical record of which indicators, data sources and modelling approaches actually predict outcomes under uncertainty.

For insurers, this mirrors familiar practice. It is analogous to:

• Model performance tracking in catastrophe risk
• Ensemble weighting in climate forecasting
• Experience-based credibility adjustments in pricing

The innovation is extending this discipline to nature related variables that are currently treated as descriptive rather than predictive.

Why incentives matter – from an underwriting perspective

Economic incentives within these systems are not designed to encourage risk-taking. They exist to ensure
truthful probability revelation and timely updating as evidence changes. Participants are rewarded for well
calibrated forecasts and penalised for systematic overconfidence or error. This mirrors how incentives already operate in insurance markets. Underwriters, modellers and reinsurers are not judged on optimism or pessimism, but on how closely expected losses align with realised outcomes. Prediction markets apply the same logic to ecological risk, creating a transparent mechanism for learning which assumptions deserve weight in underwriting decisions.

Strengthening parametric and index-based products

Parametric insurance has demonstrated the value of rules-based triggers for climate and catastrophe risk, particularly where rapid liquidity and low loss-adjustment friction are critical. Nature-linked parametric products – for reefs, mangroves, watersheds, or fire-prone landscapes – are now emerging, but face persistent challenges around basis risk and trigger calibration.

Accuracy-weighted forecasting systems offer a way to improve parametric design upstream. Rather than relying solely on historical correlations or static indices, insurers can draw on forward-looking probability estimates about whether ecological thresholds are likely to be breached.

These signals can inform:

• Trigger selection and attachment points
• Premium differentiation across sites or portfolios
• Capital buffers for correlated ecological risks
  

Crucially, post-event scoring allows insurers to learn which ecological indicators are genuinely predictive of loss relevant outcomes, improving product design over time.

Implications for underwriting, portfolio management and capital

For insurers and reinsurers, the value proposition is practical:

• Underwriting: More granular, site-specific probability signals for nature-exposed risks (e.g. agriculture, aquaculture, infrastructure, sovereign risk)
• Portfolio management: Improved differentiation between nominally similar risks with very different ecological trajectories
• Capital modelling: Better-informed assumptions for long-term nature-driven loss amplification
• Model governance: A transparent framework for comparing indicator performance and justifying model choices to internal risk committees and supervisors.

Rather than adding another layer of qualitative ESG assessment, this approach introduces measurable forecast performance into nature-risk analytics.

Regulatory and supervisory relevance

Supervisors are increasingly focused on forward looking risk, scenario credibility and model governance. A recurring challenge in nature-related supervision is that firms can disclose extensive information without demonstrating that underlying assumptions are reliable.

Prediction-informed systems address this gap by making assumptions testable. They allow insurers to show not only that they have identified nature related risks, but that they are actively evaluating the accuracy of their risk estimates over time. This aligns with supervisory expectations around model risk management, internal validation and prudent capital assessment.

Importantly, these systems are designed to complement, not replace, disclosure frameworks such as TNFD. Disclosure defines the scope of assessment whereas accuracy-weighted forecasting helps determine which assessments are decision-useful.

From exposure mapping to predictive discipline

Nature-related risk will increasingly shape insured loss through physical impacts, transition dynamics and liability pathways. Managing that risk effectively requires moving beyond exposure mapping toward predictive discipline.

Insurance has always been a learning system: prices, model, and capital evolve as evidence accumulates. Applying that discipline to nature-related risk is not a departure from insurance principles, it is a return to them.

The opportunity now is to extend the industry’s core strength – probabilistic reasoning under uncertainty – to one of the most complex and consequential risk domains it faces.

About the author

James d’Ath is a sustainable finance and nature-risk specialist working at the intersection of finance and biodiversity. He served as Technical Lead for Data & Analytics at the Taskforce on Nature-related Financial Disclosures (TNFD) Secretariat and led the Nature-related Data Catalyst, mapping over 200 nature-related data tools used by financial institutions.

With more than 30 years’ experience across asset management, investment research and financial product development, his work focuses on improving the decision-usefulness of nature-related information, particularly where uncertainty, data limitations and forward-looking assumptions are material. He is the co-author of Prediction Markets for Nature-Related Risk.