We created a test instance and defined the Scope (WEF) and the five risk categories as they are defined in the WEF report. To make it look closer to the report, we adapted the style on how objects are to be represented. Also the beautiful colors on the heat map template had to be replaced with a pure and bright white.
Now all that had to be done was to enter the data. Probably the aspect that took the longest, we went into the WEF reports of 2018, 2017 and 2014 and collected the high level information such as risk name, risk category, description, impact and likelihood level and top relations to other risks. Risks that had similar names in the past, we considered as ancestors of the current risk. A few new risks emerged as well.
As the reports changed the dimension ranges over the years (7 to 5), we normalized the scales to a 5 field matrix. That did fit pretty good with the standard matrix template in the system, so we did not take the effort to change the labels and only swapped the impact and likelihood dimensions.
That was all we needed for this little experiment.
Great! Just one thing: We had to use a little trick. For audit reasons, the system does not allow you to change risks in the past or pretend that they were entered in the past. It would not have been possible to enter the risk situation for 2014 and 2017. So the trick is, we used three consecutive days, each representing a year. No change in the result, but the audit trail is safe.
The Risk Heat Map
No surprise, the heat map looks pretty similar to the one in the WEF report.
The WEF report only shows the quadrant where the risks are located. To put things better into relation, it is good to look at the full scale.
In our opinion more important though is to see the change over time. Since we entered historic data, we are able to see how risk levels have changed. We will leave it to the reader to evaluate the results, but we consider this as valuable information.
For this we compared the risk situation of 2018 with the one from 2014 and 2017. Wilst there is a major increase in the perceived risk between 2014 and 2018, the risk level has even mostly decreased after 2017. (Large points are the risks in 2018, small points are the risks in 2014, respective 2017.)
Here the details with labeled risks per risk category.
2014 - 2018
2017 - 2018
To map the risk interrelations we used the “Most connected global risks” information for each risk. Since the VirtueSpark engine uses directed graphs, we enhanced the information with directions. E.g. the risk of “Cyberattacks” could impact the risk of “Data fraud or theft”. The other way around makes less sense, although there are some bi-directional relations.
Having adapted the style we came to a similar result as in the WEF Risk interrelations map. But with the directed relations we now are able to see knock-on effects. And since the VirtueSpark Platform considers all knock-on effects, we see them across multiple nodes.
When selecting “Biodiversity loss and ecosystem collapse”, the blue edges are feeding risks and the red edges are risks that are impacted by the selection.
Another example is the risk of “Failure of critical infrastructure”.
Beyond helping us to indicate relations between risks, this information also helps us to better identify real sources of risks. This in return gives a wider range for the allocation of resources for mitigation actions and creates results that are more effective.
This can even be better identified when we rearrange the risks. Who would have thought that cyberattacks could have an indirect impact on unemployment.
This was a little internal experiment, not for public use. Nevertheless, we thought it is worth sharing some insights. We only used the data that is freely available in the WEF reports.