Wars and terrorist attacks will never fit traditional frameworks for assessing risk. But that does not mean they can’t be covered. Kai-Uwe Schanz and Doron Zimmermann outline a new intelligence-based model for underwriters.

Conventional wisdom dictates that political risks such as war, terrorism and civil unrest largely defy risk modelling, so are impossible to insure against.

There is also a significant accumulation risk – many individual risks being affected by a single event – which makes commercial insurers tend to shy away from offering cover.

Public or public-private substitutes only partially meet risk transfer and management needs, particularly as globalisation and international economic integration increase exposure to political risk. How can this “market failure” be addressed?

This article outlines a new information, knowledge and intelligence-based model for writing political risk insurance (PRI) commercially. We explain how your company can use it – and why it has the potential to generate underwriting profits.

Only lower-risk and lower-frequency threats such as riots and strikes are insured against at present. The industry avoids terrorism and war-like risks because of the potential for huge loss and accumulation. Nonetheless, the capacity of the commercial terrorism insurance market has grown from $50m (£33.8m) in 2000 to $1.4bn in 2008, according to Willis.

A niche market has developed for smaller risks such as expropriation and contract frustration. It mainly serves investors and banks exposed to emerging market risks. According to Swiss Re, this market’s standalone volume is estimated at $600m to $700m. Combined with commercial credit risk, it generates about $5.5bn of premium income.

The main players in this market include state-owned export credit agencies, private sector companies such as ACE, AIG and Chubb, Lloyd’s syndicates such as Catlin, Hiscox and Liberty, and credit insurers such as Atradius, Coface and Euler Hermes.

It’s hard to gauge the potential of an enlarged market that would include higher risks. These are generally excluded from property insurance policies, but are partially covered in marine, aviation and other speciality insurance policies, usually for an extra premium.

No reliable market figures are available but we can make assumptions. Assuming a 2% share of political risk premiums in total property and marine, aviation and transport (MAT) premiums, there is a global market potential of close to $10bn. Swiss Re says the world’s non-life insurance markets were worth $1.6 trillion in 2007. We further assume that the property and MAT lines account for 20% and 5%, respectively, of total non-life premiums.

It is impossible to quantify the specific economic and social costs associated with a lack of PRI cover. It is obvious, however, that it hurts trade flows, foreign direct investments, travel, construction, the utilities sector, hotels/leisure and many other activities vital to society.

We all remember the aftermath of September 11, when air travel came to a virtual standstill and needed massive government intervention. In general, appropriate PRI cover can alleviate the paralysis caused by massive political risks by at least mitigating its financial consequences.

The credit crisis has highlighted the shortcomings of a primarily quantitative and backward-looking approach to risk management. Respective risk models spectacularly fail to address the possibility of market dislocations and disruptions: modelling the future by extrapolating the past leads to impasse, irrespective of the degree of quantitative sophistication.

According to the traditional model, risk around high-severity events cannot be measured satisfactorily, accumulation risks are incalculable as multiple lines of insurance are affected, and maximum losses are simply not manageable. We challenge some aspects of this thinking.

Scenario-based thinking, sound information and intelligence-based managerial judgment will play a larger role in anticipating future risk. We believe this fundamental shift favours a new PRI model.

In the insurance industry, analysts tend to design highly complex risk models, inputting arcane sets of numbers, intelligible to a secretive guild of actuaries. This quantitative set is then submitted to underwriters, who, in turn, derive their pricing on the basis of the output provided.

But there is considerable doubt about the usability of quantitative risk modelling for political risks – war, war-like events, political violence and internal unrest due to war. Political risk and, more particularly, security political risk are largely subjective.

So the question is, if the nature of the risk is understood by insurance pundits as incalculable, why does the industry want to apply quantitative methods to an entirely qualitative risk?

One answer is that there is simply no room for a revolution in insurance: if it is not calculable, it cannot be insured. Such thinking may be ingrained in the history of insurance. But the nature of risk should always be assessed without being restricted by any one method.

Let’s start with the three key attributes of our inquiry. First, the behaviour of political risks is shaped by individuals or groups deviating from social norms.

Second, political risks tend to be counter-intuitive: they usually are beyond what can “reasonably” be fathomed and they follow their own logic that cannot easily be defined by applying our own values.

Third, the behaviour of political risks can be characterised as asystemic: political risks do not behave consistently within a bounded structure. So the nature of political risk is context-specific, dynamic and entirely unpredictable – if observed through the lens of a static, quantitative model.

We suggest applying a method that takes into account unpredictability and other factors including “black swan” worst cases. We believe it is the nature of the risk we need to assess – not our need to make the political risk fit traditional quantitative frameworks.

To put these ideas into action, we suggest a “security intelligent enterprise” (as introduced by Doron Zimmermann in The Swiss Export Journal, Q1 2007). Government security agencies have been operating with the idea of prospective, intelligence-driven assessment for decades. The challenge is not only to collect the data, but to transform this information into knowledge.

Knowledge by itself is not applicable to a specific problem. The ability to condense knowledge into relevant and timely intelligence is what determines how successful any political risk assessment is.

The key success factor for an intelligence-based assessment of political risk is the quality of connections between collection, analysis, dissemination and application – for example, decision-making support, awareness and education. As the underlying data is processed, the security intelligent enterprise must be concerned with the verification of sources, quality control and ensuring the results are interpreted to reflect the three key attributes of political risk we’ve already talked about.

In addition to thoroughly reviewing any given threat, opportunities to ensure resiliency and reduce vulnerability should be identified and implemented constantly.

The security intelligent enterprise can be understood as a qualitative system. It starts with risk assessment, followed by a peer review of that assessment. Experts such as police, intelligence analysts and practitioners can discuss potential risks rather than just relying on historical and numerical data. Once the consolidated qualitative, intelligence-driven risk assessment is considered fit for use, it is discussed with the specialist underwriter, who will assess client exposure and come up with a price based on expected event frequency and severity.

The relationship between risk assessment and underwriting is shaped by subject-matter expertise instead of assumptive modelling. Experts share knowledge with specialised underwriting personnel, for example, in marine insurance, where a keen sense of assessing political risk has been preserved.

A security intelligent enterprise produces a client-driven, bespoke solution for the PRI market. It is important to abandon more traditional product design elements, such as annual flat fees and quantitative risk modelling, and introduce forward-looking, more adaptive and dynamic risk sensitivity.

Cumulative limits are no longer capped arbitrarily but are solely determined by a 24/7 monitoring of highly specific client exposures. This would allow highly specific, yet contextualised, risk assessment to drive underwriting and pricing for an object-specific policy. Companies that master this approach are likely to provide pinpoint accurate and client-driven risk transfer solutions that their competitors will find hard to match.