Risk can suddenly and severely impact the financial well-being of an otherwise stable and profitable company. AIR provides sophisticated analytical tools and software systems to help companies manage that risk. AIR is the world's premier risk modeling and technology firm specializing in risks associated with natural and man-made catastrophes, weather and climate.
- Central London
|Address||3 St. Helens Pl|
|Phone Number||020 7588 7901|
From Our Website
It began with one small office in Boston and a vision. AIR pioneered the catastrophe modeling industry, creating the tools that changed how people think about risk management. Today, AIR has nine offices around the globe and is expanding our technology every day to make individuals, businesses, and societies more resilient. More than 400 organizations rely on AIR's models, software, and services to manage their risk from natural catastrophes, terrorism, cyber attacks, and pandemics. AIR is part of the Verisk Analytics family of companies, a leading data analytics provider.
A deep understanding of your risk is demanded by boards of directors, rating agencies, and regulators, and more robust tools are needed to refine risk estimates. AIR models, software, and services give you the insight you need to make better risk management decisions. As the complexity of insured risks continues to grow, brokers face an increasing demand for their services. AIR models, software, and services arm you with the information you need to give your customers the best advice and negotiate effectively on their behalf.
Our people, their passion, and the diverse backgrounds and perspectives they bring, make AIR unique. From developing new models to using the latest technologies to providing exceptional service, your talent is what sets us apart. Find your role. Research and Modeling develops, enhances, and validates AIR's state-of-the art extreme event models, and conducts field research in the aftermath of catastrophes-whenever and wherever they occur-to bring home findings that enhance the robustness of our models.