The popularity of Python means insurance and financial services companies have a growing body of actuaries, quantitative developers, and software engineers capable of building innovative and customized solutions for both data management and modeling.

Many of these same organizations leverage cloud services to maintain data and execute models and may be challenged to improve efficiency to minimize cloud-related expenses and reduce carbon footprints to align with sustainability goals. Can an organization capitalize on employees’ knowledge of Python and simultaneously address performance concerns? We believe one potentially powerful tool to accelerate Python is Numba, a technology for transforming Python code to native machine code.

This session will explore:

• A brief overview of use cases in insurance and financial services
• Considerations in algorithm design to optimize performance
• The background of Numba as a just-in-time (JIT) compiler
• The key benefits and challenges of Numba
• The underlying mechanics of Numba
• Techniques for customizing or extending Numba to navigate challenges
• Performance improvements from utilizing Numba