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Digital EditionFrom Reactive to Proactive: How AI and IoT Can Shape the Future of Risk Management
Peter L. Miller, CPCU, MS, MBA, is president and chief executive officer of The Institutes Risk & Insurance Knowledge Group and a member of its Senior Management Team.
As president and CEO of The Institutes, I’ve been deeply involved in exploring how applying artificial intelligence, analytics and IoT sensors can transform the insurance industry. During a recent Travelers Institute webinar, I focused on how these technologies are not just evolutionary but revolutionary in their potential to reshape how we approach risk management.
The insurance industry stands at a critical juncture. Globally, insurers paid more than $135 billion for natural disaster losses in 2024, according to Swiss Re. As pressure builds on insurers and insureds from more severe and frequent secondary perils — including wildfires, flooding and severe storms — we must embrace innovative solutions.
As I emphasized during the discussion, “The best loss is the one that never happens.” This principle drives the Predict & Prevent® model, which leverages the convergence of three technologies to detect and prevent losses before they occur: IoT to supply risk data; AI to rapidly make sense of that data; and blockchain to store and share risk data.
Real-world applications are already proving the model’s effectiveness. Consider the commercial building owner I interviewed who installed water sensors costing $6,000 to prevent losses averaging $75,000.
This represents the kind of return on investment that makes the business case for embracing risk prevention technology. Similarly, telematics in vehicles and workplace safety sensors demonstrate how technology can actively prevent losses while providing valuable data for risk assessment.
The NFL’s use of sensors to predict injuries and NASA’s development of technologies to prevent in-flight failures further illustrate the broad applicability of these approaches. The implementation of these technologies extends beyond simple monitoring.
Advanced AI algorithms can now analyze patterns in real-time data streams, identifying potential risks before they materialize. For example, smart sensors can detect subtle changes in electrical systems that might indicate impending equipment failure, while connected vehicles can alert drivers to dangerous road conditions or potential mechanical issues before an accident.
However, we must address legitimate concerns about AI implementation. Data quality and ethical considerations are paramount. As I noted during the webinar, bias in training data can lead to biased outcomes, making it crucial to implement proper governance frameworks and regular audits.
Privacy concerns must also be balanced against the benefits of loss prevention, requiring transparency in how we collect and use data. The development of new AI liability products by reinsurance companies illustrates how the industry is already adapting to these challenges.
Looking ahead, I believe risk management and insurance professionals must embrace these technologies to remain viable and provide better service to policyholders. The rising costs of losses and increasing frequency of catastrophic events make it imperative that we move from a reactive to a proactive model.
At The Institutes, we’re developing new courses and certifications focused on AI and data analytics to help professionals navigate this transformation. These educational initiatives include online courses about AI tools, case studies of successful implementations, and guidance on integrating innovative technologies into existing business models.
I encourage risk management and insurance professionals to invest in understanding and applying these technologies, to the benefit of insurance and society as a whole. &
