Influencing the c-suite: using analytics to quantify loss exposure

In an ever changing, increasingly globalised and technology-driven world, the nature of risk has continued to evolve at a rapid pace. As the parameters of risk are continually changing, prioritising the most pressing and severe business risks has become a complex task for risk managers. With environmental risks on the rise and the growth in digital cyber threats increasingly impacting incidences of physical risk, the potential for complex losses to occur is growing.

For risk managers trying to manage this complex risk landscape, the question of where to prioritise risk improvement efforts remains, particularly in a world where the ability to obtain funds for risk management may be constrained by a c-suite focused on efficiency.

One advance that is becoming ever more useful in answering this question, is the use of "big data" and analytics solutions. By embracing tools that can help quantify the effect of losses on uninsurable assets such as reputation, missed growth opportunities, negative investor sentiment etc., risk managers can highlight to the c-suite the potential uninsurable damage they could sustain.

Creating a strong argument has always been a major factor in the c-suite's decision to invest in risk management, and the ability to highlight many of the negative, irrecoverable impacts of a major loss needs to become another useful tool that risk managers can draw upon.

Understanding physical risks through realistic models

Whilst the ability to quantify the impact of losses on uninsurable losses is an important step for risk managers, it is still vital that traditional, physical risks are not forgotten. Risk managers have access to many tools that can help evaluate the likelihood and impact of major equipment breakdowns, fires, explosions and natural hazards, such as floods, and should embrace these where possible. However, it is vital that the datasets that underpin these tools are accurate and sufficiently large to be statistically valid.

Many tools use actuarial models, based on historical data, which don't always account for changing physical conditions. When looking at risks such as flood, tools that fail to account for physical changes caused by rising sea levels and trends altering population density may be providing risk managers with an inaccurate assessment of the risks their business faces. Using tools based on physical data, collected at regular intervals, can vastly improve the reliability of the tools that risk managers rely on to help assess these physical risks.

For risk managers in the modern world, having access to clean and reliable data is a vital tool in understanding where risk management strategies should be prioritised and presenting this to senior management. Risk managers that can clearly highlight why investment in risk management is critical for achieving broader business goals are more likely to be successful in obtaining the necessary capital expenditure from the c-suite.

FM Global will be hosting a 20 minute talk on predictive analytics at the Learning Hub at Airmic's annual conference in Harrogate. Click here for more details.

Allan Macpherson is operations vice president, London operations chief engineer at FM Global.