Why Gaspatchio?¶
A Modern, AI Enabled, Approach to Actuarial Modeling¶
Actuarial modeling has evolved significantly over the past few decades, from spreadsheets to specialized commercial software to programming languages. Gaspatchio represents the next step in this evolution – a Python-native, life insurance actuarial modeling framework that combines high performance with genuine actuarial model developer-friendly features, and most importantly is set up to benefit from all the wonderful capabilities that AI has to offer actuaries. The longer term goal is to enable actuaries to develop and use models largely through prompts.
Alternatives in the Actuarial Modeling Space¶
Several open source options exist for actuarial modeling (life and non-life):
Some Open Source Alternatives¶
- lifelib: Python library for life insurance modeling, leverages modelx
- chainladder-python: P&C reserving library in Python
- OasisLMF: Open framework for catastrophe modeling
- ActuarialR packages: Collection of R packages for various actuarial tasks
- JuliaActuary: A collection of packages for actuarial science in Julia, providing inspiration for modern, open-source actuarial tooling.
- Heavylight: A lightweight Python library that allows you to run heavy modeling workloads using a familiar recursive syntax.
Custom In-House Solutions¶
- Spreadsheets: Still widely used but limited in scale and governance
- Generic Python/R: Powerful and flexible, but requires engineering type skills to create a robust, high performance and transparent model
When to Choose Gaspatchio¶
Gaspatchio shines when you prioritize a modern, Python-native development experience backed by high-performance execution.
Choose Gaspatchio if you:
-
Want a Seamless Python Experience:
- Build models using standard Python syntax, functions, and libraries, while being transparent.
- Leverage your existing Python ecosystem (NumPy, SciPy, etc.) directly within your models.
- Enjoy a familiar and flexible development workflow without learning a proprietary language.
- Leverage the power of existing python tooling and use LLMs to build models alongside you.
-
Are Building for the Future with AI/LLM Integration:
- Utilize a framework designed with AI co-pilots in mind, featuring intuitive Python syntax and clear feedback mechanisms.
- Easily integrate Gaspatchio models into larger AI-driven workflows for tasks like:
- AI-assisted model building and validation.
- Automated model documentation generation.
- Intelligent scenario generation and analysis.
- Model reconciliation using AI agents.
- Check out llms.txt and llms-full.txt for more information on how Gaspatchio can be used with LLMs - especially if you ARE and LLM expert.
-
Need Excellent Debugging Capabilities:
- Utilize standard Python debugging tools (like
pdb
, IDE breakpoints,print
statements) directly in your model code. - Benefit from clear, contextual error messages during development.
- Easily inspect intermediate results and understand model logic step-by-step in Debug Mode.
- Utilize standard Python debugging tools (like
-
Demand High Performance for Production:
- Switch seamlessly to Optimize Mode for production runs, leveraging a high-performance Rust core and the Polars engine.
- Achieve significant speedups through automatic batching of operations, parallel execution, and Numba JIT compilation for custom Python functions where possible.
- Benefit from native vector operations for efficient handling of time-series projections and large datasets, avoiding common bottlenecks like data explosion.
In essence, Gaspatchio is ideal for teams that embrace Python, require both strong debugging tools and production speed, and see AI/LLM integration as a key part of their future modeling toolkit.
When to Consider Alternatives¶
Gaspatchio might not be the best choice when:
-
You require extensive GUI-based modeling
- Gaspatchio is code-first, requiring Python knowledge
- Visual modeling tools might be more suitable for less technical users
-
Your team lacks Python expertise
- Proficiency in Python is necessary to use Gaspatchio effectively
- R-based tools might be better if your team is R-focused
-
You need extensive life insurance libraries immediately
- While Gaspatchio is expanding, some commercial platforms offer more out-of-the-box life insurance templates