Today's Analytics—Changing Our Understanding of the Past and the Future of Risk

  • Standard
  • 25 Aug 2015
  • admin

Willis Group, Risk Management August 2015 Feature

The future is not what it used to be. Neither is the past.

As we build and analyze our stores of data we are discovering correlations and trends that had not been readily apparent in the past. These correlations are challenging, if not upending, the way we look at the future. Still, as the proverbial black swans become increasingly grey, we know that data cannot entirely predict future events.

So how do we get the most from this ‘new past?’ That is, how can the study of data and the building of analytical models powered by insightful algorithms change our understanding of the past in a way that gives us a view into the future?

At its most basic, predictive analytics allows us to align current risk management priorities with spend. It gives us the ability to develop increasingly sophisticated solutions to managing financial exposures within the real estate industry.


The rise in analytic tools can make a significant difference in the way risks are understood, measured, mitigated and transferred. Take business interruption values for example. These values are often developed by filling out a generic template. If more information can be incorporated and additional analyses applied, we can focus in on the risks that can destroy value in the organization. When the biggest risks and failure points are identified before a major event happens, the effectiveness of a risk management program can be greatly increased.

By calculating business interruption values based on a detailed review of historical and/or projected occupancy statistics along with an analysis of saved operating expenses, more informed decisions can be made pertaining to appropriate levels of coverage and identifying where an organization should apply its resources in disaster recovery and business continuity planning. In the event of a loss, having accurate business interruption values will allow for loss exposures to be estimated for the period of indemnity which will help expedite advance payments.


The improvement in data capture and analytical capability is timely. With a significant and increasing percentage of the world’s urban population living in coastal zones, rising sea levels will demand far greater resilience to flooding and severe weather in the near future. Well-delivered and balanced forecasts of extreme events, with limitations and uncertainties explained, can be invaluable in mitigating and adapting to the effects of low-frequency but high-severity events. We can now identify the properties most vulnerable to natural hazards through the quantification of the overall risk by using insurance market recognized and/or propriety catastrophe models as well as actuarial techniques.

Awareness and understanding of the hazards a property is exposed to can help organizations become far more resilient, prepared and efficient in strategically managing and adapting to the increased risk posed by natural catastrophes such as coastal flooding compounded by climate change.

Using risk transfer to reduce the volatility of an organization’s financial results moves risk transfer spend from being merely transactional to being a strategic lever that can aid organizations in achieving their corporate financial objectives. It empowers us to embed risk management decisions into C-suite strategies.

For instance, an organization may determine that protecting EBITDA from a certain level and frequency of reversal is paramount to the company’s success. By modeling the performance of the organization’s current risk transfer program against thousands of alternative programs, we can see how they would perform in protecting EBITDA in the case of a severe financial loss. Customizing risk transfer programs to act as a hedge to protect corporate financial objectives enables risk managers to deliver programs that are superior in value and efficiency.


Equipped with improved data and analytics, organizations are able to tailor their risk transfer options more keenly, attract new kinds of capital, and better customize their risk-transfer and risk-financing strategies. From captives to financial-markets-backed catastrophe bonds for property exposures, analytics are empowering solutions that enable businesses to transfer traditionally uninsurable risks to a third party.

Precise data — both historical and exposure-related — is enabling a high level of risk transparency and opening new vehicles for mitigating and financing specific risks or portfolios of risks, including property exposures, business interruption consequences, project risks, and in some cases liability risks.


With more to learn, this future is coming into focus for all of us in the risk world. At the edges, it is beginning to allow us to think about risk finance in new ways, supporting the development of new and often customized risk transfer solutions for risks historically considered ‘uninsurable.’

The combination of diverse forms of data, from an individual entity’s own experience to broader industry data classes, provides a much more comprehensive view of risk. This allows for a look around the proverbial corner with company-specific, forward-looking loss scenarios. That gives organizations the chance to constantly adapt their risk management approach.

For any questions regarding this article, or with risk management and insurance related queries, please contact Thomas Price at 804-527-2304 (, or John Wyatt at 804-527-2324 ( You can visit Willis’ website at

Leave a Comments