Hedge funds and banks are accelerating hiring in insurance-linked securities (ILS) and catastrophe modelling as climate volatility, extreme weather events and emerging tail risks reshape how financial institutions assess portfolio exposure, according to a report by Bloomberg.
The expansion reflects growing demand for specialists capable of modelling the probability and financial impact of events such as hurricanes, wildfires and floods, alongside broader applications in areas including civil unrest and cyber risk.
Recruiters say demand for professionals with insurance-linked securities expertise has intensified sharply, with a limited global pool of qualified candidates.
ILS specialists traditionally focus on modelling natural disaster probabilities and their impact on insurance and reinsurance portfolios. These skills have historically been concentrated in the catastrophe bond market, a segment valued at roughly $70bn.
However, hedge funds are increasingly seeking to integrate this expertise into broader trading and risk strategies, creating competition for talent with both quantitative modelling capabilities and investment experience.
Compensation for experienced hires can range from roughly $400,000 to more than $600,000 annually, according to industry recruiters, with top candidates attracting significantly higher packages.
Large hedge funds are expanding dedicated teams focused on climate and catastrophe risk, as firms seek to better understand how physical climate events could affect asset prices and market volatility.
Firms including Millennium Management and Squarepoint Capital have been among those hiring specialists in weather modelling and catastrophe risk analytics, while other platforms are building similar capabilities in-house.
At Citadel, teams of weather and commodity specialists are already being used to inform trading strategies across energy and other climate-sensitive markets.
The move reflects a broader shift in which hedge funds are increasingly incorporating environmental and physical risk modelling into investment decision-making, rather than treating it solely as an insurance or actuarial function.
Investment banks are also strengthening their catastrophe and climate risk capabilities. JPMorgan Chase is among institutions hiring specialists to lead implementation of catastrophe modelling frameworks designed to assess physical climate risk across portfolios.
The roles are intended to support both risk management and broader strategic planning, including the transition toward lower-carbon and more climate-resilient investment frameworks.
Elsewhere, trading firms such as Jane Street have been recruiting weather analysts to support commodities and systematic trading strategies, highlighting the growing overlap between climate science and financial markets.
The growth of catastrophe modelling within finance is being reinforced by advances in satellite data and alternative datasets that allow institutions to monitor physical risks at a highly granular level.
Firms such as ICEYE are providing real-time flood and storm impact data, enabling banks and investors to combine observational inputs with internal risk models to better estimate potential losses and pricing dynamics.
This integration of real-world climate data with financial modelling is increasingly viewed as critical for understanding tail risks in a world of more frequent and severe weather events.
While artificial intelligence is being used to improve catastrophe risk modelling techniques, industry participants emphasise that human expertise remains essential for interpreting outputs and validating assumptions, particularly in low-probability, high-impact scenarios.
Experts note that many AI systems are not yet optimised for modelling extreme tail events, reinforcing continued demand for specialists capable of bridging quantitative methods and real-world risk assessment.
The global pool of experienced catastrophe modellers remains relatively small, estimated at only a few thousand professionals worldwide, according to recruitment specialists.
