Multi-risk optimisation:

an approach to hardening insurance markets

Multi-risk optimisation: an approach to hardening insurance markets

Introduction: positioning to grab opportunities
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 market?

Was the status quo too narrow and unnecessarily complex?
For several decades, power companies considered each class of insurance in isolation when assessing historic losses to establish ongoing insurance arrangements. Premium, market capacity, deductible and insurable limit were the main drivers, with only limited analytical decision support undertaken to assess placement outcome and pricing. Additionally, insurance lines are often bought with different renewal dates, with some local policies stretching across different geographies as well as varying levels of deductibles and limits; this adds complexity, alongside the narrow focus on individual classes. However, embracing a portfolio view using modern analytical capabilities and computing power has led to better understanding of dependencies between and within risks and exposures, together with more optimal decision making.

What if risk managers adopted their FD’s perspective?
Historically, basic terms for individual classes where tweaked in response to changing rates in hard and soft markets, often to maintain budgeted spend. But this 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 and the value of insurance as a hedge is therefore not revealed. However; power companies easily perceive the value from transferring risk in a layered arrangement by purchasing hedges in commodity markets, interest rate and currency markets and seeing a portfolio of risks interacting 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 align with the world of Finance.

In Figure 1 above:

The horizontal axis shows the expected annual cost of the insurance strategy, which is made up of the premium spend and the cost of the retained losses.

The vertical axis shows the amount of retained risk in a ‘bad year’.

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:

They will spend only what they need to on insurance - and not a penny more.

They will effectively and efficiently protect the company against the insurable risks that matter most to them. In our experience, optimization leads to a 10-30% reduction in risk and/or insurance cost savings.

There will be better understanding and visibility of new or non-insurable elements in the overall risk landscape when they are added.

They have positioned themselves to broadly enhance decision-making capability for the future.

Catching the current moment
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 in flux concurrently. 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:

Set the key metrics for the insurable risk

Define the cost and risk profile of the current insurance programme

Identify alternatives to optimise the cost/risk profile and then trim to most realistic option

Define the company’s insurable risk tolerance

Identify optimal insurances to stay within risk

Adjust the programme as the risk profile changes and insurers respond to the new market conditions

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.
Transferring volatility: preparing for the future
Tailored cover and alternative solutions
A hardening market for insurance always encourages the search for creative alternatives within the market. Currently, large power 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 outside of the traditional insurance space. They favour speed, simplicity and may generate the ability to trade power company risks into a liquid market.

Enhancing governance
A useful by-product of taking 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 accounts for the interdependencies of risk while also considering the merits of different strategies.

In the governance realm, power 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 fuel supplies due to carbon-taxes.

Benefits of this approach
More generally, companies that use this approach
find that they:

Change the nature of conversation about risk

Increase the focus on the portfolio of risks rather than individual types of risk

Recognise the value of transferring risk above their risk tolerance, often using novel approaches

Enable an enhanced understanding of the interaction of a wide range of risks

Improve their corporate governance and highlight that improvement to investors and regulators

Conclusion: a practical example
To conclude, a recent example will help to show the breadth of questions that can be answered by this approach.
Major renewable energy generating company
This client carried out a risk optimisation exercise covering differing types of generating assets and focusing on Physical Damage and Business Interruption risks to better understand their overall risk exposures and to identify the key drivers of risk, both by asset-type and class of risk. The range of assets covered wind, solar, hydro and biomass, together with an element of traditional fossil-fuel generation. The risk profile of the company was quantified, including a detailed assessment of financial risk tolerance.

Various options for increasing retentions were quantified, using a combination of client and market loss data held by Willis Towers Watson; the client increased retentions on key classes to reduce their total cost of risk. 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 to minimise increases to their total cost of risk. They are also starting to develop their capability for using parametric solutions for the non-insurable impact of weather events for wind and solar generation and integrate these risk models with their insurable risk.

Andy Smyth is Senior Partner in Willis Towers Watson’s Strategic Risk Solutions division in London.
Andy.Smyth@WillisTowersWatson.com

Douglas Stevenson is an Associate Director in Willis Towers Watson’s Strategic Risk Solutions division in London.
Douglas.Stevenson@willistowerswatson.com

However, embracing a portfolio view using modern analytical capabilities and computing power has led to better understanding of dependencies between and within risks and exposures, together with more optimal decision making.”