An overview of the latest in the OASIS LMF (Loss Modeling Framework) platform and a new video in our gallery.
Do not miss our presentation “Agile, the right answer for the ‘next normal’ in the insurance sector” at IDS2021.
Mirai Solutions, together with Swiss Re Institute, is sponsoring the online Insurance Data Science Conference next 16 - 18 June 2021.
The Insurance Data Science Conference 2019 took place on June 14th at ETH Zurich and Mirai Solutions was ready to contribute with a sponsorship and two talks.
Oasis Loss Modelling Framework has been building an active community, which seeks to unlock and revolutionize the world of CAT modelling.
Excitement is building up for the second edition (7th overall) of the Insurance Data Science (former R in Insurance)!
Mirai presenting TensorFlow Probability and Solvency Contagion Modeling at Insurance Data Science 2019 in Zurich.
We are pleased to share that the 2019 OASIS LMF conferences will again take place in Zurich and London, just one week apart in the month of June.
Mirai Solutions is proud to be one of the sponsors for this year’s edition of Insurance Data Science Conference.
Mirai Solutions was invited to participate to the second 2018 OASIS LMF conference: “The good, the bad and the ugly”
Insurance Data Science was held on 16 July 2018 at Cass Business School in London.
We are sponsoring again the Insurance Data Science, formerly known as R in Insurance, on 16 July 2018 at Cass Business School in London.
On Thursday 8th June the 5th edition of the R in Insurance Conference took place in Paris. Once again we were delighted to attend and support this year’s event.
Mirai Solutions has been actively participating in various conferences and workshops this autumn. Here are the highlights.
Mirai Solutions was actively present at the 4th R in Insurance Conference in London, which was a great opportunity to meet both practitioners and academics active in the field.
The programme for the 4th R in Insurance conference is finalised. Mirai Solutions will present latest advancements on how to achieve parallel Monte Carlo simulations that are efficient, flexible and reproducible.