An Actuarial Perspective on AI's Impact on Workers' Compensation

Frank Huang

Frank Huang, FCAS, MAAA, Head of  P&C Actuarial Solutions

May 29th 2024

Artificial intelligence (AI) and the latest development in technology presents intriguing possibilities for actuaries working in the Workers’ Compensation (WC) space. AI-powered solutions hold the potential to refine risk assessment and lower operational costs. However, success requires a comprehensive understanding of AI’s benefits and inherent limitations to leverage its strengths to your advantage.

A Powerful Tool for Workers’ Comp Actuaries

Workers’ Comp databases contain valuable insights for actuarial analysis, and AI can play a crucial and advantageous role. With AI-powered tools, the following solutions can take minutes whereas they previously may have taken weeks and months or weren’t possible at all.

Data-Driven Analysis

AI algorithms excel at analyzing large and complex WC datasets, identifying trends and particularly relationships that may not be obvious through traditional methods. This enhanced understanding allows end users to make more informed pricing, reserving, and overall risk quantification decisions.

Predictive Modeling

Fueled by AI, predictive models can assist end users in forecasting risk, identifying potential outliers, and optimizing outcomes. Examples of this are models that help identify potential large complex claims within days of their being reported, allocation of loss control and safety personnel to proactively mitigate losses, improving profitability at segment and aggregate levels through more informed pricing/renewal decisions, detecting potential fraudulent claims or third-party recovery opportunities, and more.

Considerations for Responsible AI Implementation

While AI presents transformative opportunities, careful consideration is required to realize its potential responsibly, and there are a few key areas that end users can be aware of.

Data Quality & Bias

The reliability of AI models depends entirely on the quality of the data upon which they are trained. Inconsistent data collection practices, data silos, and incomplete data contribute to challenges with data quality. Actuaries and modelers must advocate for improved data collection and standardization practices to ensure data integrity, identify potential biases, and take measures to mitigate the risk of biased outputs within AI models.
That said, many companies have been hesitant to start the process towards AI models due to a feeling that they lacked the requisite amount of data. As technology and computing power has improved, so too has the ability to do more with less.

Model Validation & Explainability

It is imperative that actuaries can validate AI models and understand how they generate results. This alignment with actuarial principles builds trust in AI output and prepares actuaries to justify using AI-powered insights to relevant stakeholders. Said more simply, it’s far easier to get buy-in on something that is clearly communicated and reasonably easy to understand. This is a crucial part of the success of a model.

Ongoing Validation and Maintenance

Like with most things in life, ongoing maintenance is essential to ensure predictions and observations remain accurate and reliable in the context of evolving risk factors, internal processes, and external environments. End users should count the cost and time necessary to maintain their chosen services.

Where Can AI Take Your Risk Group?

Integrating AI into your Workers’ Compensation program involves a combination of technical expertise and an understanding of the domain-specific challenges. Are you looking to enhance data-driven decision-making within Workers’ Comp? Explore the resources and insights Davies offers in this complex landscape.

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