An actuary and a data scientist

"walk into a bar..."

I've been reflecting on conversations with consultants and pricing teams over the past year, and it's clear our industry is changing significantly. There's a noticeable shift toward embracing data science and bringing data scientists into insurance teams, alongside actuaries who are learning advanced analytics and programming. This isn't about replacing actuaries but enhancing our abilities to better handle modern risk assessment.

A key part of this change is the growing use of open-source tools like Python and R to build advanced pricing models. These languages offer great flexibility and a vast ecosystem of libraries, allowing teams to develop custom models tailored to their specific needs. By moving away from relying solely on platforms with pre-built models, insurers can build proprietary models in-house and develop their own intellectual property. This approach encourages innovation and helps companies adapt quickly to changing market conditions and regulations.

There's also increasing interest in advanced machine learning techniques in insurance pricing, especially the shift from traditional generalized linear models (GLMs) to gradient boosting machines (GBMs). GLMs have been fundamental in insurance pricing because they're easy to interpret and explain to stakeholders. However, they have limitations in capturing complex, nonlinear relationships in data.

GBMs excel at handling complex interactions and patterns without needing manual feature engineering. They offer better predictive accuracy by modeling intricate relationships that GLMs might miss, leading to more precise risk assessments and pricing strategies. However, adopting GBMs presents challenges. Their complexity can make them less transparent, raising concerns about interpretability—important in an industry where regulatory compliance and stakeholder trust are critical. Integrating GBMs into existing workflows also requires new tools and expertise, which can be a hurdle for some organizations.

This transition highlights the need for platforms and frameworks that can help adopt advanced models like GBMs while addressing these challenges. By providing solutions that work regardless of how models are built—whether in Python, R, or other tools—we can enable teams to easily test, deploy, iterate, and optimize their pricing models. Such platforms can manage the complexities of model integration, ensure compliance requirements are met, and offer tools for model interpretability. This allows actuaries and data scientists to focus on improving their models without getting bogged down by technical obstacles.

Industry trends support this direction. Studies have shown that insurers investing in advanced analytics and data science are outperforming their peers in growth and profitability. Integrating data science into core business functions drives innovation and helps companies stay competitive.

By supporting collaboration between actuaries and data scientists—combining industry expertise with advanced analytical skills—we can create more robust and responsive pricing strategies. Platforms that facilitate this collaboration can speed up time-to-market and foster innovation by allowing teams to experiment with new modeling techniques and data sources, all while ensuring thorough testing and compliance.

Looking ahead, I believe embracing this direction will be crucial to staying ahead. Integrating data science into insurance isn't just a passing trend; it's becoming a fundamental part of how we assess risk and serve our customers. By supporting this transition with the right tools and platforms, we can unlock new levels of efficiency and effectiveness in our pricing models.

Our industry is evolving quickly, and it's an exciting time to be part of this change. Embracing advanced modeling techniques like GBMs will be key to staying ahead, and I'm optimistic about the opportunities that lie ahead for all of us.

Sources:

• McKinsey & Company (2022). Advanced analytics in insurance: Driving value from data-driven insights.
• The Actuary Magazine (2023). "Price wars: The rise of Gradient Boosting Machines".
• Celent (2021). Insurance Data Analytics Trends.