Open-source coding and AI: a gift or curse for actuarial modeling

Tech advancements are transforming actuarial practices, requiring modern tools. We explore open-source coding, specifically Python, and AI in actuarial functions.

In brief

• Actuarial departments are modernizing tech stacks to meet evolving accounting and regulatory demands, leveraging emerging technologies for innovation and efficiency.
• AI technologies, including generative AI, provide efficiency gains and insights that can streamline coding processes and enhance decision-making.

As technology continues to evolve, actuarial departments are modernizing their toolsets to tackle increasingly complex inquiries. This transformation includes the integration of actuarial valuation and projection models, spreadsheets, databases, process automation, and analytics applications. Recent advancements have led to the development of consolidated financial modeling platforms that address both valuation and projection needs, enhanced data warehousing capabilities, and automated processes with built-in controls. These modern tools not only help organizations adapt to recent accounting and regulatory changes but also pave the way for advanced analytics that provide deeper business insights.

Despite these advancements, challenges persist. The demand for customized solutions for innovative product designs, coupled with the need for rapid market entry, continues to grow. Outdated technologies, such as APL and traditional spreadsheets, remain in use, creating inefficiencies. Additionally, complex use cases like forecasting and asset-liability management (ALM) add to the operational costs as organizations strive to meet increasing demands.

To address these challenges, actuarial teams are encouraged to rethink their approaches, leveraging the rapid evolution of artificial intelligence (AI) and computing capabilities. A notable trend is the growing interest in combining open-source programming languages, such as Python, with AI technologies. This combination offers unique opportunities to enhance the actuarial toolkit, providing a transparent and user-friendly coding platform with extensive libraries.

Opportunities for AI in actuarial modeling

While modernization efforts have improved actuarial capabilities, there are still opportunities to further integrate AI and Python into the actuarial toolkit. Potential applications include:

1. Targeted solutions

For use cases requiring detailed modeling and quick processing, AI-enabled open architecture solutions can provide lightweight, focused code that meets specific demands.

2. Migration of outdated models

AI can facilitate the transition of legacy models from outdated platforms to more flexible open-source environments, reducing risks associated with unsupported technologies.

3. Enhanced research and development

Python's libraries enable robust statistical analysis and predictive modeling, supporting experience analysis and assumption setting.

4. Improved connectivity

Python can enhance end-to-end connectivity across various actuarial modeling processes, enabling seamless automation and data integration.

Considerations and responsibilities for actuaries

The adoption of new technologies brings both opportunities and responsibilities. As organizations embrace open architecture solutions, they must consider the following:

1. Model Development Lifecycle (MDLC) and governance

The flexibility of open architecture requires refined governance practices to maintain model integrity and functionality.

2. Maintenance challenges

The introduction of new models may complicate maintenance across existing systems, necessitating careful planning and adherence to governance guidelines.

3. Evolving talent pool

The demand for multidisciplinary talent will increase, requiring actuaries to possess coding skills and an understanding of AI while ensuring data security and compliance.