AI for Actuaries: Three key use cases

According to Carrier Management's October 2024 industry survey, 89% of insurance executives plan to invest in GenAI in 2025, with 92% having already allocated budgets for these initiatives. This represents a dramatic acceleration from previous years, with 78% of organizations now using AI in at least one function, up from 55% in 2023.

Coming from an actuarial background, including time spent building out and leading the predictive analytics team at Allianz, I've been exploring the implications of AI in insurance for many years. For those familiar with my views, you'll know I'm not a blind optimist. While AI holds immense potential, much of it remains just that: potential. The current state of AI is more constrained than its biggest proponents might suggest.

I also recognize that many insurers are still grappling with technical and data challenges, which are essential for maximizing the benefits of AI and ML. Fortunately, these are issues most are actively working to address.

That said, I'm genuinely excited about the possibilities AI and ML present for our industry. These technologies will empower insurers to maximize existing resources, harness previously untapped or underutilized data, and automate time-consuming tasks, to allow for more focus on value-driven work. Actuaries will be able to develop more robust pricing models, and ultimately drive business success.

Even better, the barriers to entry for these innovations have never been lower.

In this piece, I'll explore three key ways Generative AI in particular can help pricing actuaries overcome long-standing challenges.

1. Accelerate actuarial productivity to focus on building more effective pricing tools

When it comes to accelerating actuarial productivity, one of the key ways AI can help is by making the coding process faster and easier so we can focus on building more effective pricing tools.

For many actuaries, coding can be a steep learning curve, and there's a real concern in the industry about not having the right technical skills. The Society of Actuaries Research Institute has issued a 2025 Request for Proposals to "investigate how AI is reshaping the technical skills required for actuaries" and to "conduct a thorough review and analysis of the evolving technical skills required for actuaries in the context of AI," confirming that technical skills evolution is a strategic priority requiring immediate attention. This formal research initiative validates ongoing industry concerns about actuarial technical competencies in the AI era.

The solution with AI

This is where AI steps in. For those who aren't as confident in their coding skills, AI can help them get up to speed, both writing code for them or acting as a code reviewer to the first attempts at a new language. This significantly speeds up the historic process of try, debug, ask stack overflow! It can also help actuaries translate code between languages such as R to Python, making cross-platform work easier and more accessible.

Even for seasoned developers, AI-powered co-pilots can take productivity to the next level by managing simpler coding tasks, allowing them to focus on solving more complex and strategic problems. This mirrors what insurance actuaries are experiencing: AI tools are automating routine coding and data preparation work, freeing actuarial professionals to focus on strategic modeling and risk assessment. PwC's Global Actuarial Modernization Survey 2025 found that 73% of professionals realized enhanced capacity for additional work after automating data-intensive activities. It's like having an assistant who takes care of the groundwork, so you can dedicate your energy to innovation.

That's not to say learning to code or understanding your technical frameworks won't remain important. AI is there to assist, not take over the entire model-building process, and that's not likely to change any time soon. But as an accelerator and enhancer of your workflows and knowledge, it can provide a real edge.

The possibilities in this area really gripped me when I first started experimenting with language models. While at a previous company, we ran a test with ChatGPT to see how well it could generate predictive modeling code with minimal input.

The results were impressive: strong, functional code in both R and Python, with only minor tweaks needed. It still required oversight from someone who had a good grounding in Data Science to ensure it wasn't 'hallucinating' methods (which it definitely did when pushed to more cutting edge approaches). But it showed instant potential for speeding up some of the more mundane coding work. This aligns with research findings that AI tools are driving efficiency gains in routine actuarial programming tasks, though domain expertise remains essential for validating outputs and preventing accuracy issues in advanced modeling approaches.

The real surprise came when we asked it to add some humor to the code commentary, and it delivered both solid code and genuinely amusing comments linked to the nuances of various Python packages. That may not be a key use case for AI in the insurance industry, but it made for a fun afternoon!

2. Equip underwriters with more sophisticated models to improve pricing insights

Building the strongest possible models is one thing; ensuring that they are used, and used to their best effect, is another.

One of the key challenges for S&C pricing is that underwriters either don't trust the pricing tools available or don't use them effectively, leading to poor adoption. Capgemini's 2024 research confirms this challenge persists, explicitly identifying that "underwriter confidence lags" behind executive optimism, despite 62% of executives viewing AI/ML positively for underwriting quality.

Finding ways to leverage AI to improve the underwriter experience with Pricing Tools and provide greater insight at the point of pricing is a great way to address this risk of poor adoption.

The solution with AI

AI can help by:

• Improving risk assessment by analysing unstructured submissions. With AI-powered tools, everything from photographs to scattered PDFs can be incorporated seamlessly and automatically into your models and pricing workflows. AI-powered extraction can dramatically reduce processing times while maintaining high accuracy, even for complex policies with multiple documents. Without rekeying or manual data preparation, it becomes easier and less time-intensive to include more risk factors that improve the quality of the pricing tool.

• Allowing underwriters to delve deeper into the context of the risk they're pricing with natural language. For example, an underwriter could "ask" their pricing tool "What rate change have I achieved on this account for the last 4 years?". Presenting this information is something that can be solved for today without AI, however it requires the Pricing Tool developer to build out that insight and interface. AI could enable underwriters to access this insight without increasing the developer burden on Actuaries or IT.

• Automatically providing points of comparison. AI-powered tools that automatically surface the most relevant information are an effective way to enhance pricing workflows. Research from Deloitte on actuarial pricing shows how this works in practice: advanced AI enables actuaries to rapidly test multiple pricing models, identify key rating variables, and simulate different economic scenarios. This represents a fundamental shift from sequential model testing to parallel scenario analysis. This capability to flag risks and automatically surface comparisons is one example of how AI can enhance decision-making in practice.

3. Data preparation

Data preparation has long been one of the most time-consuming tasks for actuaries, consuming 31% of actuarial modernization efforts and ranking as a top-four priority. Issues like incomplete or inconsistent data, siloed systems, and manual data handling can create significant bottlenecks that divert actuarial capacity from higher-value analytical work.

It's vital work. Portfolio analysis and predictive modelling both rely on clean, well-structured data. But actuaries are often left spending more time on data wrangling than on meaningful analysis. PwC's Global Actuarial Modernization Survey 2025 confirms the scale of this challenge: 31% of global insurers identify data preparation and data management as a key area requiring actuarial involvement in modernization initiatives. This makes it the fourth-highest priority behind actuarial model design, model testing, and model maintenance.

The solution with AI

AI can streamline this process by automatically extracting and cleaning data from both structured and unstructured sources, such as claims descriptions, loss notes, or even external datasets.

This can aid in merging claims with exposure data for modeling purposes. AI can also generate peril-and segment-specific insights that are often missing from traditional datasets.

This type of work would historically have required a summer intern to come in and manually work through multiple claims descriptions, policy files etc or for a modeler to spend significant amounts of time writing code to parse the various data sources. Simplifying this work is a significant win and allows Actuaries to more quickly move onto the fun part of the work: the analysis.

The impact is measurable: 73% of survey participants realized enhanced capacity for additional work after outsourcing data-intensive activities, indicating that data preparation consumes substantial capacity that could be redirected to higher-value analytical work.

Ultimately, leveraging these tools can give actuaries richer data to work with, faster and with fewer manual interventions.

Further applications for AI

Pricing model development is just one facet of AI for actuaries. Accelerating productivity across all work responsibilities, from data preparation to model maintenance and risk assessment, is driving the broader actuarial transformation.

Here are just a few of the possible use cases:

• Explaining actuarial techniques. Actuaries who want to develop professionally, stay on top of industry shifts and embrace new techniques can benefit from using AI to simplify complex actuarial methodologies. Leveraging LLMs to generate summaries of technical papers can make new methods easier to grasp.

• Day-to-day administrative tasks. AI can assist with routine tasks such as drafting emails, conducting peer reviews during busy periods like year-end, or even writing actuarial opinions, freeing up time for more strategic work.

Next steps

AI seems to be sparking excitement and apprehension in equal measures. From my perspective, actuaries who can leverage AI will find themselves processing information faster, summarizing complex analyses faster, communicating more effectively and building more tools and applications that would have been impossible 5 years ago.

Far from being replaced, this means they will be adding significantly more value to the business.

While AI is easier than ever to get started with, the competitive edge is going to belong to actuaries who best understand and leverage it. However, it's important to recognize that we're currently in what PwC characterizes as a foundation-building phase, where carriers need significant foundational investment before realizing tangible benefits. Forrester warns that only a fraction of insurers will harness AI's full potential in the near term, emphasizing that successful transformation requires comprehensive organizational change, not just technology deployment.

Embracing training opportunities, staying on top of industry news and innovation, and building for the future will be key.

To learn more about the foundational requirements for AI and ML with hyperexponential, the only end-to-end pricing platform built by actuaries and powered by AI, get in touch with us here.