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With the concerns surrounding the COVID-19 outbreak, we wish to reassure our clients and contacts worldwide that Oasis remains fully operational and open for business.


Oasis Loss Modelling Framework

An open source catastrophe modelling platform, free to use by anyone.

It is also a community that seeks to unlock and change the world around catastrophe modelling to better understand risk in insurance and beyond.

While its development is largely driven by the global (re-)insurance community, it seeks to provide tools and utility to all.

It is constituted as a not for profit company, and our team believes passionately in empowering more people around the world to better understand risk and uncertainty.

Our ecosystem consists of more than 18 suppliers covering over 90 models.

Our Modelling Platform

The Oasis Loss Modelling Framework provides an open source platform for developing, deploying and executing catastrophe models. It uses a full simulation engine and makes no restrictions on the modelling approach. Models are packaged in a standard format and the components can be from any source, such as model vendors, academic and research groups. The platform provides:

  • A platform for running catastrophe models, including a web based user interface and an API for integration with other systems (Oasis Loss Modelling Framework)
  • Core components for executing catastrophe models at scale and standard data formats for hazard and vulnerability (Oasis ktools)
  • Toolkit for developing, testing and deploying catastrophe models (Oasis Model Development Toolkit)

Find out more about our platform >


Oasis Newsletter 2020 - Q4

Welcome to our newsletter. Feel free to send us your comments at any time via email...
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News update on International Climate Initiative (IKI) project

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Oasis Newsletter 2020 - Q2

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Webinar - Open Data Standards

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Online course on Catastrophe Modelling

The first MOOC on the use of models in natural catastrophe risk management is live.

Click here to access the course

Oasis Loss Modelling Framework is co-funded by

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 730381.