The Life Modeling Problem: A Comparison of Julia, Rust, Python, and R

Link: https://juliaactuary.org/blog/life-modeling-problem/

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All of the submissions and algorithms above worked, and fast enough that it gave an answer in very little time. And much of the time, the volume of data to process is small enough that it doesn’t matter.

But remember the CUNA Mutual example from above: Let’s say that CUNA’s runtime is already as fast as it can be, and index it to the fastest result in the benchmarks below. The difference between the fastest “couple of days” run and the slowest would be overĀ 721 years. So it’s important to use tools and approaches that are performant for actuarial work.

So for little one-off tasks it doesn’t make a big difference what tool or algorthim is used. More often than not, your one-off calculatons or checks will be done fast enough that it’s not important to be picky. But if wanting to scale your work to a broader application within your company or the industry, I think it’s important to be perfromance-minded[4].

Author(s): Alec Loudenback

Publication Date: 16 May 2021

Publication Site: JuliaActuary

Python for Actuaries

Link: https://www.pathlms.com/cas/courses/15577/webinars/7402

Slides: https://cdn.fs.pathlms.com/p3Z78DJJRFWoqdziCQyf?_ga=2.2405433.801394078.1623949999-2118863750.1623949999#/

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Explaining why actuaries may want to use the language python in their work, and providing a demo. Free recorded webcast, from the CAS.

Author(s): Brian Fannin, John Bogaardt

Publication Date: 6 February 2020

Publication Site: CAS Online Learning