Analytics are now being used to predict the relative likelihood of different loss-producing events at a location.
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.
