Julia: Helping to Prevent the Next Global Economic Crisis
In 2008-2009, the global economy went into meltdown.
Millions of Americans lost their homes, banking systems imploded in Iceland and Ireland, unemployment in Greece skyrocketed from 7% to 28%, property prices in Spain declined by 30-50% and the total global cost has been estimated at up to $22 trillion, more than US GDP.
One of the hardest hit sectors was insurance. The European Union responded by passing strict new Solvency II requirements to ensure the insurance industry has the necessary resources to avoid and mitigate any future such crisis.
This meant that insurers across Europe had to rebuild their risk analysis models.
So one of Europe’s largest insurance companies called on Julia.
The new Solvency II requirements determine everything from how much capital each insurance company is required to maintain to how their assets can be distributed. This has a major impact on the firm’s asset portfolio and expected financial performance.
Furthermore, the complexity and quantitative requirements increased substantially with the new regime, along with the demand for speed, transparency and effective risk management, requiring daily simulations and a massive increase in computations.
They chose Julia for its superior speed, productivity, functionality and ability to handle dramatic increases in data volumes and model complexity – all leading to faster, improved performance.
What was the result?
According to the insurance firm:
The Julia model is 20 times faster than a similar model in R, and 1,000 times faster than IBM Algorithmics
They managed to reduce the number of lines of code from 14 thousand lines in IBM Algorithmics to just one thousand lines in Julia
o Fewer lines of code means fewer mistakes, less programming time and more efficiency, transparency and simplicity for updates, analysis and error checking
o Elegant, readable, inspectable code
According to the firm’s Director of Financial Solutions Modeling, “Solvency II compliant models in Julia are one thousand times faster than IBM Algorithmics, use 93% fewer lines of code and took one-tenth the time to implement.”