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Reimagining History Can Benefit Insurance Risk Analysis

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November 03, 2017

A Lloyd's and RMS report titled Reimagining History: Counterfactual Risk Analysis states that "whenever an event occurs that takes the insurance market by surprise, questions are asked how the loss might have been averted, or what additional risk mitigation measures might have reduced the loss. It is also useful for questions to be asked how the loss might have been worse. To analyze what could have happened if events had turned for the worse is called a downward counterfactual. By contrast, an upward counterfactual considers what could have happened if events turned out better."

According to Lloyd's website, the "report aims to encourage insurers to think about risk in a different way by highlighting counterfactual analysis' potential to mitigate data bias, test model results, analyze tail risk and identify potential high impact events. It also details how counterfactual analysis can be carried out in practice and acts as a starting point for future research."

Trevor Maynard, head of innovation at Lloyd's, said, "The fact that downward counterfactual events are anchored to actual historical experience helps facilitate complex explanation, deeper understanding and more coherent communication of future risks and modelling uncertainty to board members, policyholders, policymakers, risk managers and others.'

The report states that "for catastrophe risk quantification, counterfactual risk analysis can be applied in all three core catastrophe modelling activities of a [property and casualty] P&C (re)insurer, namely pricing, capacity management and capital calibration."

According to Gordon Woo, catastrophist at RMS, "Downward counterfactual risk analysis helps address the bias that can be inherent in some models that are based on the same historical data sets. By expanding the data available based on what could have happened, these models can be built with less reliance on single-source data, which might improve their accuracy. It also provides a useful tool for regulators to stress-test catastrophe risk models."

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