Cristiano Ronaldo is a Portuguese professional football/soccer player, widely regarded as one of the greatest players of all time. Ronaldo holds the records for most goals and most assists in the history of the UEFA Champions League, and has scored over 700 senior career goals for club and country.
Ronaldo possesses many fine attributes as a footballer, but pressed to identify the single thing most critical to his goal scoring ability, most would agree, it would be his legs. Recognizing this, the astute management team at Real Madrid upon signing Ronaldo swiftly secured a $100 million insurance policy to cover for injury to said legs. How does one go about assessing the risk, and pricing the risk premium, on something as unusual as this?
Risk Scoring (Not Goal Scoring)
Risk scoring is the practice of creating a calculated number (a score) that reflects the level of risk in the presence of some number of risk factors. It is applied in many disciplines, including statistics, econometrics and in Ronaldo’s case, insurance. In a case like Ronaldo’s, the calculation includes risk factors such as:
- Previous injuries
- Position on the pitch
- Games played
In a more mundane example of scoring, such as your credit score, it will includes such inputs as your payment history, and current credit utilization.
In short, risk scoring is a widely used, and proven approach to handling complicated risk decisions, and has an important application in predicting the next Covid-19 Hot Spot.
Future Risk Events – Covid Hot Spots
An important risk question for many businesses in our new Covid-19 world is where will the next hotspot occur:
There are many reasons to ask this question. Are our workforce or customers at higher risk? Do we run a higher risk of a regulatory infraction, or negative news media? In a hotspot location the risk of one of these outcomes significantly increases. Identifying hotspots in advance creates an opportunity to adjust operations and processes in readiness.
Many websites and news outlets have developed tools which can be used to view the state of Covid-19 in numerous ways for a given country, region or county, including total cases, daily new cases, 7-day moving average, acceleration in new cases..
Borrowing from insurance
Similar to the approach taken by insurance companies in assessing future risk, predicting the next Covid-19 hot spot requires a multi-factor approach. By combining and risk-weighting factors such as the rate of case acceleration, near term caseload and longer term caseload, we were able to create a real time, blended risk-weighted score at the county level, making it possible to better identify areas more likely to become the next Covid-19 hotspot.
Below is a screen-shot visualization of our county level risk score, taken from one of our visualization tools, as of 30 days ago:
By using this approach we were able to identify – more than 30 days in advance – that the upper Midwest was shaping up as a high risk area, and as of today we know:
A major hotspot.
Learning from the pros.
Real Madrid made an investment to protect their business, and their insurance company took multiple factors into consideration when assessing the risk of injury to Ronaldo’s legs. Covid-19 is a deadly important topic. The identification of Covid-19 hotspots in advance presents companies with a window of time in which to adjust operations to mitigate negative outcomes. Importantly, as is the case with any prudent risk assessment, improving the accuracy of prediction calls for a multi-factor approach.
We are delighted to partner with the thought leaders at some of the top companies in the US working on the front lines of Covid-19 risk management, and we welcome new sign ups to trial our approach, as we work together to help reduce risk and improve health and safety outcomes.