In the past couple of years, many leading companies have combined data with focused analytics and deep industry knowledge to view risk in a different manner, enabling superior risk financing decisions and positioning themselves strongly relative to others in the industry. How are these leaders positioning themselves to capitalise on their efforts-to-date and generate relative premium savings in a hardening insurance market?
Was the previous status quo too narrow and unnecessarily complex? For several decades, energy companies have considered each class of insurance in isolation when assessing historic losses to establish ongoing insurance arrangements. Premium, market capacity, deductible and insurable limit are often the main drivers, with only limited analytical decision support undertaken to assess placement outcome and pricing. Additionally, insurance lines are often purchased with differing renewal dates, with many local policies stretching across different geographies as well as varying levels of deductibles and limits; this adds complexity, alongside a narrow focus on individual classes. However, embracing a portfolio view, using modern analytical capabilities and computing power, has now led to better understanding by some energy companies of dependencies between and within risks and exposures, leading to more optimal decision making.
What if risk managers adopted their FD’s perspective? Historically, basic terms for individual classes were tweaked in response to changing rates in hard and soft markets, often to maintain budgeted spend. But this strategy does not fit with the preferred decision-making framework of Treasurers and CFOs, as the complex structures are not transparent regarding protection from a series of losses. This lack of transparency means that the value of insurance as a hedge is hidden from view. However, energy companies can easily perceive the value from transferring risk in a layered arrangement by purchasing hedges in the commodity, interest rate and currency markets, thereby seeing how a portfolio of risks interacts with extreme scenarios. The trade-off between risk and return is a familiar approach for most CFOs and Finance teams and is integral to their decision-making framework. For our purposes, we will amend the framework slightly to show the trade-off between retained risk and expected cost and how this approach aligns with the Finance Director’s world.
In Figure 1 left:
The objective is to reduce the amount of retained risk and at the same time reduce the expected annual cost and move to a more efficient programme, closer to the edge of the cloud in the above diagram. We call this edge the efficient frontier; it represents structures with an annual cost saving to the company, as well as significantly de-risking the balance sheet at the same time. There can be many paths to the efficient frontier, depending on potential insurance structure scenarios; furthermore, new and known non-insurable risks can be easily added to the portfolio.
Advantages of multi-risk optimization The proposition for companies here is clear:
What happens in a rapidly hardening market? A potential consequence of the old-fashioned approach to viewing insurable risk in siloes is purchasing more insurance than necessary across the portfolio of risks - and at elevated prices, given current market conditions. In a prolonged soft market, awareness of this unfavourable pricing will be low, as each successive year may yield the company a small decrease in premium for the same terms and conditions, with relatively little effort expended. The hardening market therefore often comes as a somewhat rude shock; plain sailing has quickly become a storm. However, those that have invested in robust navigational instruments can use the storm to their advantage to win the race, at least in relative terms.
How can energy companies react? When insurance rates are rising rapidly there is sudden pressure for transparency and better understanding on costs of risk-transfer and sharing, so opportunities for savings are more easily realised and communicated. Clearly in a rapidly hardening market the positioning of the edge of the cloud can evolve, as all elements of premium and coverage structure are concurrently in flux. Insurers may simultaneously change their view on deductibles, limits, sub-limits and committed capacity. The range of potential optimal scenarios has widened and can easily be captured by a good multi-risk optimisation approach as described above. It is preferable if the underlying models have already been constructed before the market hardens, but an experienced analytics team can construct a model relatively quickly.
Methodology In practice, the response to a changing market is carried out in six distinct steps:
Having kept the ship steady, it may also be desirable for key new or non-insurable risks to be given visibility in the decision-making framework, yielding a more complete picture.
Tailored cover and alternative solutions A hardening insurance market always encourages the search for creative alternatives within the market itself. Currently, large energy companies may wish to understand the impact of using insurance-linked securities as a vehicle for tapping alternative markets for risk-transfer of extreme scenarios. Parametric solutions, which can transfer financial volatility arising from weather related events or natural catastrophes away from company balance sheets, are an excellent example. By understanding the variability inherent in risk exposures that are not necessarily insurable, it is possible to use analytics to develop tailored cover based on measurable factors such as volume of rainfall, wind speed, footfall and temperature. These may offer good long-term value for certain segments of the risk landscape as the risk partners are often from outside the traditional insurance space. They favour speed and simplicity and may additionally generate the ability to trade energy company risks into a liquid market.
Enhancing governance A useful by-product of adopting this systematic approach to establishing the most efficient structure for transferring risk is the creation of an audit trail of decision-making for risk financing. It can be shown that an objective and robust approach has been followed that both accounts for the interdependencies of risk while also considering the merits of different strategies. In the governance realm, energy companies will be particularly interested in the Task Force for Climate Related Financial Disclosures (TCFD) introduced by the Bank of England in 2017. The use of cross-class modelling, including interdependence and non-insurable elements, will allow companies to demonstrate awareness of the longer-term impacts of climate change on their business. Some good illustrative examples include the cost of additional flood defences on low-lying infrastructure or higher cost of power supplies due to carbon-taxes.
Benefits of this approach More generally, companies that use this approach find that they:
To conclude, a couple of recent examples will help to show readers the breadth of issues that can be answered by this approach.
1) Bespoke oil-spill model An integrated oil company wanted help to quantify the risks of a significant oil spill in both its drilling and production wells. Working closely with the client’s risk and engineering teams, we developed a model to forecast both the likelihood and volume of an oil spill at their sites, as well as the cost of clean up of any spill. The tailored model is based on industry data for large oil spills as well as key risk factors particular to the client, including well location, type of drilling, well depth, water depth and hydrocarbon type. The client has used the model to make decisions for:
Figure 3 above shows the cost of potential oil spills, expressed as Value at Risk, for the portfolio of drilling and production wells for the client and is one of the outputs of the analysis.
2) Global energy company This client carried out a comprehensive risk optimisation exercise to better understand their total risk exposures and to identify the key drivers of risk, both by geography and by class of risk. The risk profile of the company was quantified, which demonstrated significant inherent risk in a single business unit. As a result, the company decided to sell off the highest risk business unit and optimized its insurance program for its remaining business units. As a well-structured portfolio model had been developed, the company was well positioned at the next renewal to understand the impact of a hardening market and adjust their insurance structure appropriately. As a result, they were able to minimise increases to their total cost of risk across a wide programme of coverage.
Andy Smyth is Senior Partner in Willis Towers Watson’s Strategic Risk Solutions division in London.
Douglas Stevenson is an Associate Director in Willis Towers Watson’s Strategic Risk Solutions division in London.
