Making the case for capital expenditure for risk improvement
Although it might sound strange coming from a commercial property insurer, insurance cannot always shield large organisations from every economic impact of a significant property loss. Swiss Re Institute’s Sigma No1/2018 research[i] demonstrates this, with the most current ten-year rolling average for man-made and natural disasters showing an estimated 68% of losses in the US not covered by insurance. Much of this gap – between insured loss and uninsured loss – stems from the significant damage to uninsurable assets that can follow a major property loss. Factors like reputation damage, lost market share, missed growth opportunities and negative investor sentiment can have a huge financial impact on a business. Given these assets can’t be insured within a property policy, the best way for a business to try to reduce this damage is by building resilience – preventing losses where possible; mitigating loss severity should an event happen; and being in the best position possible to recover.
The lack of clarity on the full financial impact of a property loss can be especially challenging for risk managers, when looking to prevent losses through risk improvement. In the past, an inability to use financial data and language to support requests for capital from the CFO and other members of the C-Suite, meant that risk improvement was often seen as a low priority and lost out when capital expenditure was apportioned.
FM Global’s Total Financial Loss (TFL) Modelling helps solve this issue by allowing risk managers to determine the value of risk mitigation actions and communicate that to senior management using the language of the CEO or CFO. The model quantifies potential uninsurable losses using a range of financial inputs to then estimate the impact of a large disruption on a company’s enterprise value.
If a business were to suffer a business disruption due to a fire, for example, the model includes the loss of business from existing customers that can occur beyond the time when traditional insurance risk transfer products end. It also includes figures for lost growth, acknowledging that with business disruption, companies are not only losing out on the existing established revenue stream, but also potentially on the expected growth of those streams. Finally, should a business lose customers and have reduced growth prospects, investors may not feel as confident about the organisation and perceive it to have more risk and be less valuable, a risk that is also factored in.
As with much successful business decision-making, collaboration is central to the effectiveness of this process. FM Global utilises trusted third-party financial data, in line with well-accepted approaches to financial valuation, working directly with clients to fill in the gaps where subjective values are needed. This collaboration and these discussions are often hugely important, as they often allow clients to change the way they are thinking about risk generally.
At FM Global, we have seen first-hand how this tool has changed clients’ approach to risk, moving the conversation to be less about insurance and more about business strategy and how risk can be reduced. The TFL Modelling responds to the long-standing need for a more robust financial justification for investments to be made in resilience. By helping risk managers speak the language of the C-Suite and help them justify the need for capital expenditure on risk management, we believe that organisational resilience can be significantly improved.
Resilience is the greatest asset that an organisation can have, by helping risk managers improve the resilience of their business, TFL Modelling can support risk managers in speaking the language of the CEO and CFO and therefore making it more likely that a company will be more successful over the long-term.
More information on Total Financial Loss Modelling can be found in this webinar: https://www.youtube.com/watch?v=NXihrShwQ6w
[i] Swiss Re Institute sigma No1/2018, https://www.swissre.com/institute/research/sigma-research/sigma-2018-01.html