Jason Mitchell
Actuary, Vice President, Marsh Advisory
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United Kingdom
From airlines to airports and manufacturers — there is huge potential for companies across the aviation sector to use modelling and risk analytics to maximise the value of their insurance programs. These techniques can improve organisations’ understanding of risk exposure and, crucially, loss volatility, plus also provide a cost-benefit assessment of different insurance programs with the data to design optimal risk financing strategies.
These were among the insights shared at the analytics and modelling workshop at Marsh’s inaugural aviation summit. Experts discussed how analytics enable companies to gain greater control of their insurance program, make more informed decisions on which risks to retain and those to insure, and identify risk correlations and opportunities for diversification in advanced program structures.
Putting the insured in charge of transferring risk
Analytics help a company to evolve from being a buyer of insurance to a transferor of risk. Rather than starting engagement with the insurance market from an existing, expiring insurance structure as a base, a transferor of risk can take greater control of their insurance program by internally pre-qualifying a range of insurance strategies through analytical work before approaching insurers. They can then seek to transfer a more consciously defined amount of risk with a view of the associated volatility and potential financial impact, if that risk materialises. The analytical findings and data give the insureds a better grasp of the level of risk they could benefit from retaining and where insurance can add most value.
Companies across the aviation sector, for example, typically expect losses such as trips, spills, and baggage claims that are usually smaller in value, occur more frequently, and are hence more predictable. Company-specific, “ground-up” loss models use an insured’s own historic loss data for these types of losses, to generate projections that best reflect the company’s own risk/loss profile. Some companies will decide to retain these more expected losses and use insurance capacity for more costly, less frequent, and less predictable events (such as a total hull loss) that fall outside their insurable risk appetite. However, it is worth noting that (re)insurers usually model the global aircraft fleet and then allocate that in some way to particular insureds. Such models are, therefore, typically skewed more towards catastrophic exposures and scenarios and, as such, these “top-down” models are more likely to positively adapt to reflect a specific fleet’s loss experience, exposures, and deductible structures, if the potential dollar swap of the attritional losses is managed more effectively.
A “guide rail” approach to volatility
Companies with a comprehensive understanding of their potential loss volatility can design a risk financing strategy better aligned to their risk tolerance and risk appetite. One approach to transferring risk entails companies deciding on the level of volatility they wish to transfer and the range of price they are willing to pay to transfer that exposure. Operating within these “guide rails,” and combined with other non-analytical placement methods, it is possible to create a bespoke program that can assist to withstand shock losses and help to somewhat offset market cycles.
Predictive analytics allow companies to set a longer-term risk financing strategy, and multi-year plans with clear guide rails also tend to avoid the chasing of short-term benefits at the sacrifice of long-term objectives.
Comparing and enhancing insurance programs
“Economic cost of risk” (ECOR) builds on the more widely recognised total cost of risk metric by further taking into account fluctuations in losses from year to year. Once organisations have quantified their economic cost of risk, they can see the cost of both more known risk and the cost of absorbing volatility over a specified period. Average annual retained losses can be calculated by some companies internally, but volatility can be difficult to quantify without more advanced statistical methods. Armed with an economic cost of risk value for the company’s current insurance program and a set of alternative strategies, risk managers can directly compare the relative economic value of different insurance strategies to inform and demonstrate their decision-making.
With regard to advanced risk financing structures, such as captive self-insurance and potentially captive reinsurance, analytics and modelling can help companies set retention/transfer stop-loss levels according to their risk appetite, and evidence risk diversification across insurance classes or across years. These tools can also enable a company to better determine the probability of a loss being paid by a given reinsurance layer and explore profit-sharing opportunities with (re)insurers under certain structures. For parametric insurance, models can identify correlations between external and client data sets and assist in assessing appropriate indemnification amounts.
Why analytics and modelling matter
There are substantial financial and risk management benefits to having the right insurance policy in place for your company. Risk analytics and modelling provide an enhanced understanding of your risk exposure and potential loss volatility and ultimately can help you to develop your insurance program into a valuable, strategic asset.
It is also important to note that price is only one measure of the cost of an insurance program. Without analytics, an insured cannot always see how different insurance program structures might impact their exposure to volatility in retained loss costs. Working with a qualified insurance consulting partner to help you quantify your economic cost of risk, can better position you to compare the suitability of various insurance programs for your company. Deploying a high level of analytics and modelling can also allow you to develop strategic insurer engagement plans designed to help you get the best out of your insurance purchase and financing strategy.
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Bradley Saunders
Analytics Development Leader, Senior Vice President
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United Kingdom
Joseph Tunstall
Vice President, Risk Management Practice
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United Kingdom
