When it comes to risk management, CFOs are in a bind: They’re expected to aggressively tackle serious business risks – while spending reluctantly. You sometimes feel damned if you do and damned if you don’t.
Addressing glaring risks is easy. Quietly simmering risks, however, are also likely to disrupt your business. Weighing their likelihood and potential severity can be challenging. And it can be next to impossible to decide where among hazy, hard-to-measure risks to invest limited resources. Often the “solution” is to simply wait.
Fortunately, users of big data and predictive analytics are just now starting to slice, dice, filter, and clarify risks to give CFOs more concrete, actionable information than ever. It’s getting easier for CFOs to take the right risk management actions with surprising precision.
Among the biggest operational risks organizations face are equipment failure, nearly data breaches or cyberattacks, and natural disasters. Yet many haven’t developed or tested formal loss-recovery plans, not to mention ones that employ big data and predictive analytics.
While cyberattacks are a relatively new animal, there are terabytes upon terabytes of data relating to actual risks of fire, flood, earthquakes, wind. and equipment failure just waiting to be leveraged. I’m specifically referring to data on actual losses generated through millions of evaluations by engineers who have visited actual commercial and industrial sites. This data is being enhanced by data on actual catastrophes and business disruptions throughout recent history.
Based on this data, CFOs are now starting to get improved information they can trust on exactly where to invest in risk reduction. Here’s a simple analogy: There’s ample data on the safety, mileage, and reliability of different car models. But let’s say you have a commercial fleet of 50 cars all of the same model (but differing years) and an annual maintenance budget of $5,000.
It sure would be nice to know which cars in the fleet are most likely to fail, how costly those projected failures would be, and what particular mechanisms within those cars are the shakiest. Then you could spend that $5,000 wisely. Without that information, your $5,000 would likely be wasted.
Insurers are now providing a similar level of detail to global businesses in the area of natural hazards, fire, and equipment failure. Let me walk you through it.
For a few years, some insurers have been able to benchmark clients’ overall property risks relative to one another and the industry. For instance, our clients’ portfolios are sorted into risk quality quartiles based on their inherent risk (for instance, are they in a flood zone?) – and deficient risk (for example, do they lack sprinklers in their warehouses?).
This benchmarking has given property owners a good basic understanding of their aggregate property risk. It’s equivalent to saying, I’m sorting your cars into four groups based on how risky they are, at first glance, relative to one another and the industry. Properties benchmarked to be in the highest-risk quartile have proven to be seven times more likely to suffer a loss than the lowest-risk category, and the losses are 30 times costlier.
Now it gets more interesting.
Based on those terabytes of historical data I mentioned, we can look across property owners’ facilities and identify a small number of their perhaps hundreds of locations that have the highest predisposition to suffer a loss. The predictive analytics now come into play. Risk calculation is based on how their current conditions align with actual historical loss experience.
With this information, CFOs can start prioritizing their planned investments to certain locations in their portfolio. They can combine this list with their own knowledge of which operations within their businesses contribute the most to their bottom line. Our data shows that locations flagged as most predisposed to suffer a loss are 15 times more likely to sustain a significant loss.
Relative Likelihood and Severity
It’s good to know which locations are most likely to suffer a loss. It’s even better to know which exposures within those locations are most likely to be associated with a loss. Analytics are now predicting the relative likelihood of different loss-producing events at a location whether it’s related to fire, wind, flood, or earthquake.
Here, the analytics concern combinations of deficiencies historically associated with losses. For example, an insurer should be able to tell a CFO that this building at this location has the highest predisposition to a loss based on the very high likelihood that, say, a storm will rip off the roof, causing an estimated loss in the neighborhood of $N thousand. Experience shows that hazards flagged as highly likely are twice as likely to produce a loss.
These insights aren’t limited to fire and natural hazards. Commercial property insurers should be able to give you similar information about valuable equipment such as generators, turbines, and chemical vessels. Such information can help you better identify which pieces are most likely to fail, how likely they are to fail, and how severe the loss would be. The seven equipment factors known to correlate with failure (or resilience) are maintenance quality, operating conditions, environment, history, operators, contingency planning, and safety devices. Equipment flagged as at risk is 10 times more likely to break down, and breakdowns are five times more severe than others.
Why This Matters
There have always been smart people to estimate your risk. What’s happening now is that decades of actual loss experience and hundreds of thousands of site visits are being applied to current conditions at property owners’ sites to make risk identification more robust than ever.
Predictive analytics are taking the guesswork out of risk management for CFOs and essentially giving them a crystal ball. They also provide underwriters a more detailed view of the risk as it exists today, and how it can be improved in the future. This allows bigger bets on providing large, stable insurance capacity at more competitive pricing.
Although it’s hard to prove a negative – that a given disaster didn’t strike because of a given preventive action – a loss-prevention dollar is more likely than ever to make a positive impact. And the risks associated with procrastination just disappear.
Gary Love is vice president of operations underwriting at FM Global, one of the world’s largest commercial and industrial property insurers.