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How Can Data Analytics Support Risk Managers?

Risk managers deal with varying degrees of risk on a daily basis, but few could have predicted the scale and severity of the coronavirus pandemic. Whilst there have been winners and many have adjusted to a new style of ‘business as usual’, the impact of the virus on their own resources, business results, supply chain, and local economy has been massive.

 

Risk Management Lessons Learned from COVID-19

Risk managers firstly faced the difficulty of keeping on top of a situation that was changing rapidly, and they needed to understand three key elements:

  1. Where the hotspots of the pandemic were
  2. How close their properties were to these hotspots
  3. The likelihood of their staff and customers being impacted by the disease

Businesses are now looking to the future as lockdown restrictions begin to ease around the world. Risk managers must also start reflecting on the lessons learned from this pandemic and prepare for the next large-scale crisis. Data analytics can guide risk managers through the next challenge, whether that’s a pandemic or another catastrophic event.

 

Mapping Risks with Geospatial Analytics

Many risk managers have started to realise the benefits of geospatial analytics and how this technology can shape their risk management strategy. Put simply, it’s a way of gathering and displaying data with a geographical component to make trends easy to spot and understand.

For instance, we recently used geospatial analytics with integrated data from the WHO, CDC, multiple Asian and European governments, and disease control organisations worldwide to create simple visualisations of recorded cases of coronavirus.

 

Covid cases

 

These maps allow risk managers to identify where the hotspots are and how close these zones are to their offices, enabling them to prepare for possible reduction in staffing levels or customer footfall. Maps can also include data on local hospitals and their capacity, the availability of testing kits in certain areas, and details on closures for schools and public transport.

The ability to integrate this type of geographical data into a risk management system can give risk managers a wealth of insight into local vulnerabilities and enable them to see at a glance how their businesses may be impacted by the spread of a pandemic or a natural disaster.

 

Projecting Future Risks with Predictive Analytics

Whilst it’s impossible to tell when the next crisis will occur, predictive analytics can help risk managers understand the projected impact of an evolving situation, giving them the insights to make critical decisions on how to mitigate risk.

One area in which predictive analytics has been particularly useful during the current pandemic is developing projections for the spread of the virus in different areas and predicting future peaks. The integration of data on hospital capacity, confirmed cases, and testing rates has enabled us to develop accurate projections for businesses who want to understand how they could be impacted, and how they can adjust their operational procedures and staff resourcing appropriately.

 

CovidEU

 

Another interesting application of predictive analytics is the projected impact of the virus on employment figures in a region based on the mix of industries in that area – the Economic Vulnerability Index. The Economic Vulnerability Index relies on eight interlinked factors including population size, remoteness, merchandise export concentration, and instability of exports of goods and services. By combining these factors in a risk management platform, risk managers can predict the vulnerability of certain industries and understand whether their business is particularly at risk.

 

Applying Advanced Analytics to Enterprise Risk Management

The integration of numerous sources of data into a risk profile can be enormously beneficial in helping risk managers to build a comprehensive picture of their risk, particularly from an enterprise risk management perspective. However, a vast array of information can be difficult to manage and complicated to cut through in order see the relationships between different variables.

This is where advanced analytics techniques such as geospatial mapping or predictive analysis can be so helpful. These techniques enable risk managers to organise this data in ways that allow them to easily identify trends and patterns that will in turn inform their response to the risk.

 

Next Steps

Now is the time for risk managers to explore the range of data-driven solutions available to help them guide their business through whatever obstacles come their way, as safely and efficiently as possible.

 

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Sep 1, 2020

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