Insurtech startups raised a record $8.2 billion globally in the first half of 2026, surpassing the previous full-year record set in 2021. The surge is driven almost entirely by a single factor: the insurance industry’s urgent need to model, price, and transfer climate-related risk using tools that legacy systems were never designed to provide.
The numbers reflect a broader transformation in how the insurance industry thinks about catastrophic risk. Traditional actuarial models, built on decades of historical loss data, are increasingly unreliable in a climate environment where the frequency and severity of extreme weather events are departing from historical patterns. Insurtech companies that can offer better risk modeling, faster claims processing, and more flexible policy structures are attracting capital at a pace not seen since the initial insurtech boom.
Several companies are leading the charge. Kettle, which uses machine learning to model wildfire risk, has expanded from reinsurance into primary insurance markets and now underwrites policies in California, Oregon, and Colorado. The company’s models ingest satellite imagery, weather data, vegetation maps, and building-level information to generate risk scores that update in near real-time, a capability that traditional insurers struggle to replicate.
FloodFlash, a UK-based parametric insurance provider, has raised a $45 million Series B to expand its rapid-payout flood insurance product into the US market. Unlike traditional flood insurance, which requires lengthy claims adjustment processes, FloodFlash policies pay out automatically when sensors detect that water levels at an insured property have exceeded a predetermined threshold. The company says average payout time is under 48 hours, compared to weeks or months for conventional flood claims.
On the data and analytics side, companies like Jupiter Intelligence and Cervest are selling climate risk intelligence to insurers, real estate investors, and corporate risk managers. These platforms combine climate science, geospatial data, and machine learning to project how physical climate risks will evolve at specific locations over time horizons ranging from one year to several decades.
The capital flowing into the sector reflects a recognition that climate risk is becoming uninsurable through traditional methods in many markets. State Farm and Allstate’s withdrawal from California’s homeowners insurance market, driven by wildfire risk that their models could not adequately price, highlighted the gap between the insurance industry’s current capabilities and the risk environment it faces.
Reinsurers are among the most active investors and partners. Munich Re, Swiss Re, and Berkshire Hathaway’s General Re have all established venture arms or partnership programs focused on insurtech companies that address climate-related risk. Their interest is strategic: reinsurers sit at the top of the risk transfer chain and need better tools to manage the concentration of climate exposure in their portfolios.
The challenge for insurtech startups is proving that their models perform better than traditional approaches when actual losses occur. Several companies have built impressive backtesting results, but the true test comes when their models face a catastrophic event that falls outside historical patterns. That test, given current climate trends, is likely coming sooner rather than later.
