Injury Intelligence: Modernizing Safety and Loss Control

As with most of the world, the insurance industry is rapidly adjusting to the impact of artificial intelligence (AI) and machine learning which has researchers seeing a rapid transition from a “detect and repair” mentality to a “predict and prevent” one by 2030.

Historically, safety and loss control risk management has generally been reactive—as opposed to proactive—tending to spend more time and resources addressing issues after they are realized. For coverages such as workers’ compensation, this could mean waiting months or years for the true risk of a location to be known before beginning to address said risk. For even one location, this could conservatively mean thousands of dollars in claims leakage.

There are two (2) main areas of AI transformation which can shift a risk program from reactive to proactive in the safety and loss control sphere:

1. Identifying high-risk locations before they are high-risk

In the historical approach, a risk management program utilizes the collective experience in their risk careers and even historical data in their program. While the information present is largely enough, introducing AI into the picture allows for new relationships and insights between the data sets to be discerned. Further, AI can help digest new datasets significantly quicker than it could a team of risk and data professionals. All of this allows for the true risk propensity of each site and location to be known before claims and issue arise.

2. Efficient and appropriate allocation of resources

To properly address the new aggregate risk profile now in view, it is necessary to reassess the staffing and experience levels therein. While there is a plethora of considerations related to staffing outside the scope of this article, follow-up analysis to complement AI and machine learning can help quantify and suggest not only the number of head count necessary to handle the exposures and their true risk propensity, but arguably, they can imply the required expertise levels.

For example, lower risk locations can be paired with more junior staff, while higher risk locations can be paired with more experienced staff. Additionally, AI and machine learning can help optimize geographic and home office locations to minimize travel and other related costs. While it is not common, especially with the present shortage of experienced risk professionals, it is possible that the transformation of a risk and safety program drives better claims outcomes as well as lower program costs.

Insurance will always thrive best when there is a balance and coordination of human experience and innovative technology. While AI and machine learning can help address these two fundamental issues in the risk management framework, it is important to involve broker and actuarial partners from the outset such that they quickly help you realize more favorable renewals, procure less costly collateral, connect with higher rated paper, and the like.

Originally published in Workplace Health (Spring 2023 Edition). Reproduced with permission.