Establishing a Federation for Advanced Analytics With AI for Corrective Action
In March’s Crane Hot Line, we provided an overview of current (global insurance and reinsurance) factors that are contributing to today’s crane insurance market challenges. These market dynamics are materially driving premium rate and self-insured retention level increases. Additionally, in many cases, crane industry policyholders have significantly reduced the insurance limit capacity available to them to protect their company interests at any price.
Material cost of risk increases, coupled with reduced insurance limit capacity available — at a time when there are jaw-dropping, record court verdicts occurring in crane accident litigation — equates to the definition of a potential market crisis. Some industry leaders believe that it is time to change the overall paradigm in crane risk treatment because the old, existing insurance underwriting risk control model needs upgrading. Sadly, more often than in other industries, crane accidents often result in fatality, and this inherent exposure in crane operations can be difficult to underwrite in any insurance market cycle.
Advanced Analytics and AI to Reduce Fatality Risk
Fatality risk projections are key driving factors for insurance underwriters of our crane industry. I have been a (Lloyds of London) underwriter of crane risk personally, and I have built a few crane insurance underwriting companies over the past three decades. A couple key lessons learned from these years is that there is proven power in numbers, and there is an equally proven power in collective data sharing when underwriting a broad spectrum of crane related risk segments. Projecting crane risk fatality exposure is one of the most critical functions in crane risk underwriting. Over the years, as our industry has evolved, there have been many risk data variables to consider in the crane risk underwriting process.
• Examples of certain analytics to consider when underwriting crane risk include:
a) Generating forecasts for critical lift scenarios in comparison to general taxi service lift projects.
b) Evaluating operator qualification levels with type/level of certifications in accordance with equipment lifting capacity.
c) Aggregating key project metrics across variable exposures (commercial; urban - industrial/plant; wind/rural).
d) Assessing historical accident causation factors in accordance with published duties and responsibilities (ASME B30.5).
e) Review and operational requirement for utilization of proper crane work ticket systems in the field for risk mitigation.
• One variable is that there is no singular magic pill or cookie cutter methodology in underwriting crane risk. More real-time/real-life data that underwriters can evaluate and share within their engineering and data science resources, the better the long-term outcome for all parties involved. Underwriters having access to advanced analytics with associated AI resources to navigate the continually growing mountains of data flowing daily will make a difference in the future of crane risk underwriting.
• The laws of competitive advantage in crane risk underwriting are changing, rewarding those who have the most robust, data-driven insights. To be in a position to improve current insurance market conditions, crane organizations and their insurance providers should recognize that real-time data with generative AI applications in risk engineering can ultimately convey predictive analytics to improve short- and long-term insurance purchasing outcomes for both policyholder and insurance providers. Changing an entire industry group’s position in any marketplace setting requires a dedicated and collective industry group initiative, and that can be a challenge.
Imagine for a moment, if you will, a collective federation of like-minded crane owner/operators, association trade groups, OEM’s, OSHA, ASME, NIOSH, ANSI, insurance providers, unions, academia and data science/AI expert firms that all come together as a singular force to reduce fatality exposure in crane operations as their collective and (singular) purpose. On the surface, this may appear to be a tall order, so let’s focus on three pertinent questions in this regard:
1. Has this type of collective industry business initiative ever been attempted before?
2. Can it really make a difference in changing and/or improving crane industry outcomes?
3. Are there any examples of other collective industry initiatives having success in reducing fatality risk?
Commercial Aviation Safety Team (CAST)
As an example of another high hazard industry segment that established long-term solutions to reduce fatality risk, the commercial aviation industry has designed, built and is currently administering a successful data-driven approach to reduce fatality risk in the global aviation industry called CAST.
CAST’s initial goal was to reduce the commercial aviation fatality risk in the U.S. by 80% from 1998 to 2008. By 2008, CAST was able to report that, by implementing the most promising safety enhancements developed by their industry stakeholders from their data-driven approach, the fatality risk results were reduced by 83% in that 10-year period. These astounding results were achieved through a collaborative effort of multiple aviation industry stakeholders, including major airlines, OEM’s, FAA, NASA, ALPA/ (Union), U.S. Dept of Defense, National Air Traffic Controllers Association (NATCA), Aerospace Industry Association (AIA).
Introducing the Federation for Crane Risk Improvement (FCRI)
FCRI is synonymous with the CAST structure and their purpose/intent and objective is to measurably reduce fatality risk.
A shared mission exists between FCRI and CAST, as both have a mission to enable a continuous improvement framework built on the proactive identification of current and future risks, developing mitigations and monitoring the effectiveness of implemented actions through the progressive use of advanced analytics and AI resources to continually improve crane risk factors to support insurance transformation objectives.
There is an equally shared vision between FCRI and CAST in that key stakeholders working cooperatively to lead the worldwide (aviation/crane) communities to the highest levels of global safety by focusing on the right target areas based on advanced analytics. The utilization of AI risk resources to enhance enterprise applications and use cases for generative AI are consistent for improved outcomes with insurance policy holders and insurance providers alike.
Conclusion
Crane owner utilization of advanced analytics with generative AI resource output in negotiating commercial insurance renewals can spark innovative and potentially changed risk perspectives by underwriters to visualize the real risk picture in specific crane operations. Although generative AI is in its infancy, the technology is already leaving an indelible mark on how heavy industrial products and services are being conceived, innovated and utilized to improve operations in multiple market segments. The insurance industry is not exempt from these changing industry dynamics, especially considering the historically common utilization of basic/old school analytics by insurance underwriters year over year in their existing methodology.
Therefore, generative AI can help usher in a new era of creativity, with a focus to specifically reduce crane fatality exposure. This action can favorably impact insurance underwriting for crane operations. The McKinsey & Company photograph illustrates some basic tenants for crane owners to consider in utilizing generative AI with advanced analytics via the new Federation for Crane Risk Improvement.
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Kevin Cunningham is president and CEO of Crane Risk Logic Inc., and has 27 years of experience in crane risk management. He can be reached at kcunningham@cranerisklogic.com.
