Is Python the right programming language for insurance?

The insurance industry is rapidly embracing programming languages to transform workflows and decision-making processes. Python for actuaries has established itself as the leading strategic choice, evidenced by its #1 TIOBE Index ranking and explicit endorsement from the Society of Actuaries for robust production use.

Whether you're an actuary looking to enhance your professional toolkit or an insurance executive seeking operational excellence, this guide will help you understand Python's role in modern insurance, from streamlining day-to-day processes to unlocking competitive advantages that drive measurable business outcomes.

Why Python for actuaries?

Python for actuaries represents more than a technical skill; it's a strategic career investment. The Society of Actuaries has explicitly recognized Python's emergence as the primary language for actuarial work, noting that actuaries will increasingly "replace models and processes built in Excel or SQL with Python models and processes."

For actuaries specifically, Python offers unique advantages: it enables ownership of the entire modeling workflow, facilitates collaboration with data science teams, and provides the foundation for AI-ready actuarial applications. With 22% projected growth in the actuarial sector through 2034 and employers prioritizing candidates with advanced analytical skills, Python proficiency for actuaries has shifted from optional to essential.

In this article, we'll explore why Python has become the leading choice for modern actuarial work, how it compares to alternatives like R and VBA, and how actuaries can quickly build Python expertise.

Python's dominance in insurance programming

Python achieved a new milestone in 2024, securing its sixth award as TIOBE Programming Language of the Year with a 9.3% ratings increase. Current rankings show Python maintaining clear leadership, with C and C++ in distant second and third positions.

In actuarial work specifically, Python's dominance is confirmed by hyperexponential's industry survey, where actuaries using Python from 15 independent companies ranked it first among programming languages they use, followed by Visual Basic and R.

This trend is supported by education patterns. Python dominates programming education in English secondary schools and continues through higher education. University actuarial programs increasingly incorporate Python as a core requirement, including the London School of Economics, which focuses on Programming for Data Science using Python in its Actuarial Science BSc.

This educational pipeline ensures a growing talent pool of Python-proficient professionals entering the insurance industry, further cementing Python's position as the dominant programming language for insurance applications.

Why coding skills matter for actuaries

Programming skills have transformed from optional qualifications to core competencies for actuaries in today's insurance landscape. According to The Actuary, the official publication of the Institute and Faculty of Actuaries, demand for coding-proficient actuaries continues to rise dramatically, yet most practitioners possess only rudimentary programming abilities.

• Enhanced autonomy and control: Actuaries with coding skills can manage their entire workflow, building and implementing models from start to finish without dependencies on IT or external teams.

• Career advancement: Recruiting firms report that employers increasingly prioritize candidates with advanced analytical and AI-compatible skills, with rapid sector growth creating strong opportunities.

• Cross-functional value: Programming expertise enables actuaries to collaborate more effectively with data science and technology teams, creating opportunities across departments.

• Future-proofing: As AI reshapes insurance, Python's role as the dominant language in AI and machine learning development positions coding-proficient actuaries for emerging opportunities.

For the analytical minds attracted to actuarial work, coding offers an intellectually rewarding extension of their skillset. Many actuaries find themselves voluntarily exploring personal coding projects after experiencing the problem-solving satisfaction it delivers professionally.

5 reasons to use Python for insurance

Python has established itself as the leading programming language for the complex, dynamic insurance industry due to its unique combination of technical capabilities and business benefits. With insurance companies facing increasing pressure to modernize operations and enhance decision-making, Python in actuarial work offers a strategic advantage that other languages struggle to match.

1. Readable and approachable

Python's clean syntax resembles English, making it exceptionally approachable for beginners and non-technical professionals. This accessibility enables actuaries and insurance analysts to learn quickly and contribute meaningfully, even without extensive programming backgrounds. The 2024 survey from Stack Overflow confirms this accessibility advantage, with Python achieving 51% adoption among respondents, representing a +7% year-over-year growth, the largest of any major language.

2. Versatile and scalable

Python excels at addressing the diverse technical challenges across insurance operations, from pricing models to claims analysis. With vast package availability and a large share of Python developers using it as their primary language, its ecosystem supports everything from routine reporting to sophisticated cloud computing implementations. For actuaries specifically, Python's modular approach allows complex insurance models to be developed, tested, and maintained more efficiently than traditional spreadsheet-based methods.

3. Seamless integration capabilities

Python's ability to integrate with existing systems makes it invaluable in insurance environments with complex legacy infrastructure. The language's popular requests package supports seamless API connections, enabling insurers to ingest data from internal databases, external sources, and even unstructured formats. This integration capability creates a bridge between legacy systems and modern environments, facilitating digital transformation without disrupting critical operations.

4. Strong community and library support

The extensive Python community ensures actuaries and insurance professionals have access to both resources and expertise. From free online tutorials to professional courses, learning opportunities abound. The ecosystem provides specialized libraries like SciPy for risk modeling, NumPy for scientific computation, Pandas for data manipulation, and visualization tools like Matplotlib, all tailor-made for the analytical needs of insurance professionals.

5. Future-proof technology investment

Python positions insurers for the future of the industry, particularly in AI and machine learning adoption. With 76% of insurers already implementing generative AI capabilities, Python's comprehensive ecosystem of data science libraries (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch) provides the foundation for innovation. The Society of Actuaries endorses Python for robust production capabilities, confirming its strategic role in enterprise actuarial work.

The strategic decision to adopt Python extends beyond immediate technical benefits to long-term competitive positioning. By embracing the industry's dominant language with its vast talent pool and AI capabilities, insurers can accelerate their digital transformation while ensuring they remain adaptable to the rapidly evolving insurance technology landscape.

Python vs other programming languages for insurance

Python is a leading choice for insurance applications, but it's worth evaluating how it compares to alternatives. Each language offers distinct advantages and limitations that impact their suitability for actuarial work, data processing, and insurance operations. Understanding these differences helps insurance professionals select the right tool for specific applications.

Python vs R for actuaries

For many actuaries, R has traditionally been their first coding language, offering statistical modeling packages and visualization tools like ggplot2. However, as the Society of Actuaries notes, Python provides broader applications from data collection to deployment, with superior cross-departmental collaboration potential. While R maintains strengths in specialized statistical computing, Python has become the strategic choice for modern insurance applications given its comprehensive ecosystem and mainstream adoption.

Python vs low/no code solutions for actuaries

Low/no-code platforms offer benefits including minimal entry barriers and restricted environments that reduce coding errors. According to Gartner, 65% of application development will use low-code platforms by 2024, with 66% of large companies implementing at least four such platforms.

However, these solutions come with significant limitations: dependence on external support, challenges with complex problems, limited system compatibility, scalability constraints, and missed skill development opportunities. Python programming for actuaries offers a more robust, flexible solution that grows with actuarial expertise and organizational needs.

Python vs C# for actuaries

C# offers notable integration with Microsoft's .NET framework and holds a strong position in programming language rankings. While both C# and Python are high-level, object-oriented languages, Python provides a gentler learning curve and broader adoption in insurance, creating a deeper talent pool. C#'s focus on building large-scale applications within the Microsoft ecosystem limits its flexibility for diverse insurance applications. While C# has moderate general adoption, it's unlikely to be needed in actuarial roles outside specific use cases.

Python vs Julia for actuaries

Julia is an emerging language gaining attention for offering Python-like readability while delivering superior performance for computationally intensive tasks. While Julia excels in high-performance computing and numerical analysis, it hasn't achieved Python's mainstream adoption. For actuaries today, Python represents the strategic choice endorsed by professional actuarial organizations, though Julia may offer specialized value for computationally intensive applications in the future.

Python vs C++ for actuaries

C++ is a lower-level, more 'machine-friendly' language compared to higher-level, 'people-friendly' languages like Python. While powerful for system-level programming and performance-critical applications, C++ presents a steep learning curve that's unnecessary for most actuarial work. Python's readability, extensive libraries, and sufficient performance for insurance applications make it a more practical choice for the majority of actuaries who need efficient data processing without the complexity of memory management.

Python vs VBA for actuaries

A focus on VBA typically suggests reliance on Excel, which industry research shows is increasingly inadequate for actuarial work. Actuarial Post noted 2024 has been "a busy year for spreadsheet and related data management errors" in insurance specifically, underscoring the governance risks that come with Excel-dependent workflows. Python for actuarial science represents a transition to a more robust methodology with proper version control, collaboration features, and scalability that Excel-based VBA solutions fundamentally lack.

Python vs SQL for actuaries

While "Python vs SQL" may be misleading as they serve different purposes and often work together, the Society of Actuaries notes Python is emerging as the strategic direction for comprehensive actuarial workflows. SQL remains essential for database management and data extraction, but Python's broader ecosystem makes it increasingly important for modern analytics and AI-driven systems. For beginners, focusing on mastering either language first based on immediate needs before expanding to the other allows for better integration of both into an actuarial skillset.

Python for actuaries: getting started guide

Actuaries can build Python proficiency relatively quickly given their analytical backgrounds, with many reaching basic modeling capabilities within a few weeks of consistent practice. For those ready to enhance their technical toolkit, four proven pathways can accelerate your Python journey.

1. Take advantage of online resources

Python learning doesn't require on-the-job training, with abundant resources available for all skill levels. Options range from free platforms like Udemy and YouTube to comprehensive, certified courses from Datacamp and Udacity. Many insurers support training costs, and resources like Statistical Learning with Python applications provide accessible approaches to key statistical concepts with practical labs.

2. Leverage training opportunities

Actuaries at organizations using the hx platform can access specialized training resources and expert-led courses. The Python for Insurance course is ideal for beginners or those transitioning from VBA or R, with certification opportunities available. As Aviva's Senior Pricing Analyst Twinkle Popat notes: "I'd never used Python before, but after the two-day training course and with the ongoing support from the hyperexponential team, it was quick and easy to get started with building in the platform."

3. Get hands-on experience

Consistent practice through real-world application is essential for Python mastery. Engaging with actual projects, whether work-related or personal ventures, helps solidify understanding and skills. Applying Python to solve genuine problems or automate routine tasks in your daily work provides practical reinforcement that accelerates the learning process and builds confidence in using the language for increasingly complex actuarial applications.

4. Stay involved

Python learning is an ongoing journey enhanced by community engagement. Participating in webinars, training events, and communities offered by organizations like hyperexponential provides valuable learning opportunities and peer networking. These connections help actuaries stay current with emerging best practices and innovative applications of Python specifically tailored to insurance industry challenges.

With these approaches, actuaries can develop Python proficiency that meaningfully enhances their work. The investment in these skills pays off through the ability to build more sophisticated models, automate processes, and contribute more directly to their organizations' strategic goals.

Transform your actuarial capabilities with Python today

Python represents the leading choice for modern insurance professionals. With widespread adoption, explicit industry endorsement, and a comprehensive ecosystem, Python empowers actuaries to build sophisticated models more efficiently, collaborate across departments, and deploy AI-ready solutions that improve loss ratios. Its versatility, learning accessibility, and integration capabilities make it a strong choice for insurance organizations seeking competitive advantage.

Ready to modernize your actuarial workflows? Discover how hx can accelerate your transformation journey, contact us today.

Frequently asked questions: Python for actuaries

Is Python the best programming language for actuaries?

Python is widely recognized as the leading choice for modern actuarial work, with professional endorsement from the Society of Actuaries and the largest ecosystem of data science libraries. Its readable syntax, extensive insurance-relevant packages, and AI-readiness make it particularly well-suited for the complex modeling and data analysis requirements of actuarial science.

Should actuaries learn Python or R first?

While R has traditionally been popular in actuarial circles for statistical analysis, the Society of Actuaries now recognizes Python as the emerging primary language for actuarial work. Python offers broader applications beyond statistical modeling, from data collection and processing to deployment and AI integration. For new actuaries entering the field, Python provides a more versatile foundation that aligns with industry trends. However, many successful actuaries eventually become proficient in both languages, using each where it excels.

How long does it take actuaries to learn Python?

The timeline varies by learning intensity and background. Dedicated study with daily practice can yield functional skills in a few weeks, while part-time learning alongside work responsibilities typically takes one to two months to reach comfortable competency. Actuaries generally report that the language's readable syntax and similarity to mathematical notation makes the learning curve relatively shallow compared to other programming languages. Mastery naturally develops over time through consistent application to real actuarial problems.