Bridging the Gap Between Actuaries and IT with AI in Insurance

The insurance industry faces mounting pressure to modernize pricing and reserving operations, as traditional systems struggle to respond to market volatility and evolving regulatory requirements. With climate-related risks on the rise and increased scrutiny over compliance, insurance leaders are seeking technology-driven solutions to maintain financial accuracy, compliance, and speed to market.

Many leading insurers have moved past AI experimentation—such as using machine learning for predictive pricing—but enterprise-wide adoption remains challenging. According to Boston Consulting Group, only about 5% of global enterprises see substantial value from scaling AI, citing manual processes and legacy systems as primary barriers. Experts highlight that the most significant challenge is no longer developing accurate models, but operationalizing them—embedding these tools into processes that are auditable, transparent, and easy to govern under emerging frameworks like the U.S. NAIC’s FACTS doctrine.

A major operational hurdle is the gap between actuarial teams, who create pricing models, and IT, which implements these models. Traditionally, this process has relied heavily on manual spreadsheets and multiple layers of recoding, raising the risk of errors and inconsistent results.

Key modernization strategies emerging from industry conversations include:

1. Prioritizing Actuarial Soundness Actuarial standards demand rigor and explainability. Thomas Holmes, Chief Actuarial Officer at Akur8, cautions that generic machine learning algorithms often fall short for insurance, as they can obscure data inconsistencies and lack necessary domain context. For successful adoption, AI tools must preserve the integrity and transparency required by the actuarial profession, avoiding any risk of deprioritizing accuracy for novelty.

"Actuarial usage is very specific. A lot of these algorithms are generic, off-the-shelf, and they will fail when hitting insurance problems, because there's a lot of nuance, there's a lot of real-world considerations, there's a lot of situations where the data lies to you. ... And that's one of the reasons that actuaries have jobs, is because the data lies, and we have to point out where that's happening, adjust the model, how to fix it, and when it's all within an AI, we get skeptical.”

2. Implementing Opinionated AI Frameworks When moving from experimentation to enterprise deployment, insurance firms are adopting 'opinionated frameworks': structured, pre-defined workflows for implementing AI solutions. These frameworks set default pathways for addressing insurance-specific problems, incorporating necessary checks and balancing automation with expert oversight. This approach prioritizes governance, transparency, and regulatory alignment over broad flexibility, making it difficult to overlook potential errors.

3. Establishing a Single Source of Truth To reduce operational risk, insurers are centralizing pricing logic into a "single source of truth." Instead of manually recoding actuarial models into disparate systems, the accepted model becomes the standard that IT ingests directly. This improves accuracy, version control, and ensures the deployed model reflects actuarial intent, reducing costly mistakes.

4. Adopting Workflow Rails Barbara Stacer, Vice President and Head of Underwriting Operations at Utica National Insurance Group, advocates for 'workflow rails': standardized, automated processes that handle non-differentiating operational infrastructure, enabling underwriters to focus on strategic pricing and risk segmentation. This hybrid model allows carriers to track versioning, manage approvals transparently, and streamline documentation, avoiding premium leakage caused by workflow delays.

5. Closing the Translation Gap The 'translation gap' arises when rate changes sit unimplemented due to bottlenecks in documentation or unclear ownership. Mapping the pricing cycle to expose these workflow lags and delegating responsibilities across actuarial, IT, and compliance teams is essential for faster and more consistent market responses.

These modernization efforts, underpinned by targeted AI and automation strategies, aim to create reliable, scalable, and compliant pricing systems. By bridging the operational chasm between actuarial science and IT, insurance firms position themselves to better withstand market challenges and regulatory demands while enhancing efficiency and revenue retention.

[Source: emerj.com]