The insurance sector thrives on complex data analysis and modeling. Actuaries play an important role in working with complex mathematical models to assess risk, price products, and manage financial reserves. As actuarial science and processes evolve and move out of the “Spreadsheet Era” they are increasingly looking to programming languages and tools that can build and implement great solutions. The choice of tools and programming languages can greatly impact the performance and productivity of actuaries. Julia, a powerful language, is fast gaining traction owing to its speed, expressiveness, and flexibility.
Why Julia for Actuaries?
According to a recent white paper published by Millman, “An actuary’s guide to Julia: Use cases and performance benchmarking in insurance”, authored by experts in the domain, Julia is a compelling option for actuaries, boosting speeds up to 9 times faster than Python in some instances.
The authors have explored Julia’s potential in the insurance sector by comparing and contrasting its capabilities with other popular coding languages including Python, Rust, NET (C#), and C++. They focus specifically on factors like development efficiency and processing performance. The whitepaper highlights challenges associated with these languages, sharing insights into their optimal use cases. The whitepaper makes a strong case for Julia as a potential language that actuaries and other professionals in the insurance sector can use to balance the trade-offs between development and run-time costs when building new products.
More importantly, the white paper shows how Julia can alleviate the common frustrations that users face when dealing with third-party package dependencies as seen in other languages. This is where Julia gets an edge because a large portion of Julia’s packages are written in native Julia, making the integration easy with Julia’s features. This native development improves performance, simplifies package management and dependency resolution, making it an ideal choice for a variety of use cases.
Unlocking Julia’s potential for Actuaries
use casesJulia is a modern, dynamic programming language designed for ease of use and high-performance execution. Among its features that make the language stand out, is the expressive syntax combined with speed. Additionally, Julia’s emphasis on performance and usability makes it a reliable choice for professionals across industries, including actuarial science. Here are some reasons why it outperforms many popular coding languages:
Expressiveness and Syntax
Expressiveness is the ability in which the scope of ideas and concepts can be presented using a programming language. Syntax is how the code looks on the screen and its readability. Julia’s syntax is user-friendly making it accessible to users in any domain, including actuaries. This allows users to go from ideas to the final product faster, express concepts intuitively, write concise functions, and ultimately focus on the end goal.
Efficient Performance
Julia is not only easy to read and write, it is highly performant for large amounts of data and modeling. Unlike traditional languages like Python or R, Julia offers high-performance capabilities, crucial for complex actuarial computations.
Cutting-edge Ecosystem
With modern tooling and a vast array of packages, Julia streamlines actuarial workflows, from data manipulation to machine learning.
Core concepts and Data Analytics Tools
Julia solves the two-language problem, that is writing the algorithm in a slow language and implementing it in a fast language. This problem slows down development and causes roadblocks for developers in scientific computing, including data analytics. Julia’s simpler syntax and advanced compiler mechanism allow users to implement concepts quickly without sacrificing performance. Julia offers a rich set of data manipulation and visualization packages along with support for reading and writing data in various formats.
Applications for Actuaries There are several key areas where Julia excels in insurance-related fields. Right from risk segmentation and customer classification to fraud detection and parameter estimation, Julia is versatile and effective across a wide range of use cases such as predictive modeling, scholastic simulations, catastrophe modeling, optimizing investment analyses, and data wrangling.
Julia vs. others
Julia can be a potential tool in the actuary’s arsenal to combat the key challenges faced by the insurance industry. Its advantage of expressiveness and syntax, along with performance has the potential to streamline actuarial workflows, improve model accuracy, and enable data-driven insights that fuel better business decisions.
The white paper includes a number of benchmarks. For example:
“If we compare the performance between Julia LinearAlgebra and Python bit array, the boost multiplier in performance becomes approximately nine times.“
The white paper also includes an investigation of Mojo, finding it faster than Python but slower than Julia. The authors’ recommendation is: “Based on our initial exploration of Mojo in this paper, we recommend keeping an eye on it as it continues to grow.”
The white paper further notes:
“Julia’s ecosystem comes with rich libraries in many aspects, and the easy-to-use syntax system allows users to implement algorithms in a straightforward manner. Its support for Unicode symbols also allows easier transliteration of formulas to code, which also creates easy-to-consume documentation. Moreover, the ease of use does not sacrifice performance. If there are two equally expressive languages but one is faster than the other, it is recommended to use the faster one. The speed improvement would not only benefit in production but also everywhere in the development, test, and release cycles.”
Getting started with Julia and JuliaHub
If you are interested in trying Julia for your specific use case, the JuliaActuary GitHub is a good place to start. Additionally, you can refer to the official Julia language documentation website here.
For enterprise users, the JuliaHub platform is designed to speed up the development and deployment of Julia programs by technical users. JuliaHub offers a free tier to get you started and Team and Enterprise environments to grow as you do.
