From 1688, when the insurance began in Edward Lloyd's coffee house in London, to the present, underwriters have been looking for better ways to assess risk.
Traditionally, risk professionals have looked at historical data and extrapolated forward to determine the level of risk and how to price coverage appropriately.
But a new report from Lloyd's and modeling company RMS, "Reimagining History: Counterfactual risk analysis," suggests that there might be a better way.
The report explains that whenever an event occurs that surprises the insurance markets, the first question asked is: "How could this loss have been averted?"
That question is followed by, "What additional risk measures might have reduced the loss?"
When the interested parties focus on ways the loss might have been worse, they're using a modeling technique called downward counterfactual analysis. (Upward factual analysis looks at what would have happened if things had been better.)
Shifting the perspective
When modeling rare extreme events — the 500-year flood, for example — the report suggests that the lack of loss data may give a false picture of the actual threat level, which could have been distorted by near misses. Downward counterfactual analysis could help insurers to identify such anomalies and adjust risk models and pricing. It can also provide insurers with the ability to search for and analyze data that may not be collected by historical real-world event research, and therefore can assist with the identification of unlikely but possible events.
So why don't more insurers and risk modelers use the technique? It's rarely undertaken because of the substantial effort required and because its purpose and value are underestimated, the report says. But counterfactual risk analysis can help calibrate the tail of loss distributions with limited distortion to the expected loss, especially with respect to region perils where historical losses are limited and no major vendor models exist, for example, flooding in Thailand. Counterfactual risk analysis can assist in modeling secondary perils, such as a tsunami following an earthquake.
There may be a lack of data from such historical events as The Great Fire of London, which was depicted here in 1666 by an unknown painter. (Wikipedia Commons, Public Domain)
Unraveling history's mysteries
PC360 spoke with Dr. Keith Smith, research & development manager, Innovation, at Lloyd's about the report and what he thinks the long-term benefits of using downward counterfactual analysis are for the insurance industry.
PC360: What would you like readers to take away from this report?
Dr. Keith Smith: The past is just one realization of what might have happened. It is useful for insurers and other interested parties to ask how the loss might have been worse. The report from Lloyd's and RMS shows how counterfactual analysis can be carried out in practice and provides a framework that can be used to apply counterfactual analysis in a systematic way.
Counterfactual analysis can benefit insurers in several ways:
— Stretching the range of event possibilities in a plausible and scientific way
— Improving risk awareness and communication through better understanding of tail risk scenarios
— Helping gain confidence in the tail of catastrophe risk distribution and
— Using in the application of all three core catastrophe modeling activities of a P&C (re)insurer, namely pricing, capacity management and capital calibration.
PC360: How would a risk manager, underwriter or other insurance industry professional convince an organization to undertake this kind of analysis, given the cost?
Smith: In the first instance, it should be recognized that this is a useful technique and with the right risk, it is well worth the cost and effort. The justification, therefore, would be on a risk-by-risk basis. However, we can also see scenarios in which costs may be defrayed across a group of collaborating partners, who may each gain from the insight created by the technique.
Counterfactual analysis can be applied to data-poor scenario-based modeling. (Photo: iStock)
3 key ways counterfactual analysis can help
PC360: What are the key issues around risk modeling generally and counterfactual analysis specifically?
Smith: There are three key points to counterfactual analysis to be aware of:
— 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.
— Counterfactual analysis can be applied to traditional probabilistic natural catastrophe modeling. Downward counterfactual analysis can help test model sensitivity and insurers' understanding of systemic uncertainty.
— Counterfactual analysis can be applied to data-poor scenario-based modeling (especially for emerging risks). It could help insurance professionals by creating structured, transparent, scientific and evidence-led scenarios. These could augment existing limited historical loss event datasets and could improve insurers' assessment of probable maximum loss scenarios.
PC360: Does downward counterfactual analysis also help regulators who are weighing policy decisions, for example, the extension of the National Flood Insurance Program?
Smith: Downward counterfactual analysis also provides a useful tool for regulators to validate risk models.
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 modeling uncertainty to board members, policyholders, policymakers, risk managers and others.
A complete copy of the report, which includes examples of counterfactual scenarios and a mathematical appendix, is available from Lloyd's website.
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