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A quantitative framework for reputational risk insurance

In our introductory blog outlining the concept of reputational risk insurance, and how it could be employed to mitigate the effects of reputational damage, we concluded that, at the centre of any reputational risk policy must sit a real-time reputational risk quantification. Only through the use of such a framework, in concert with the efforts of insurance specialists and communications teams, can realistic policies be created to indemnify organisations against reputational damage.

Quantifying the known risks

How, then, could such a framework be constructed? The foundations would be laid on known risks, starting with an audit of an organisation’s reputation; examining the existing risk register, and analysing and quantifying what a ‘normal’ state looks like for these risks. By tracking how much exposure an organisation has to a given issue, the level of negativity surrounding it, which stakeholders are talking about it, and in what context, the base level reputational risk can be established.

Once the norm is established for known risks, organisations can plot in real-time any deviation from that standard: increasing levels of negative exposure; new and more vocal antagonists entering the fray; greater strength of criticism from usually benign stakeholders. Real-time reporting can then generate automatic alerts, enabling intervention before reputational damage occurs.

Understanding the sector context

Another strut in the ‘known risk’ sector of the framework is composed of red flag issues to which an organisation is exposed due to the nature of its activity. Tracking the tenor of negativity around these issues gives an understanding of likely reputational damage.

For example, the use of palm oil has become a rallying point for environmental NGOs lobbying big business: any organisation in the food or pharma sectors using palm oil should, therefore, understand not only their own reputation, but the shifting reputation of the product itself (is it getting more negative, are viable alternatives being championed?), and make a risk/reward calculation on its use. If the reputation of a product becomes increasingly negative, and NGO pressure and media coverage over its use escalate, there comes a point where reputational damage outweighs the financial reward of using it.

In addition, organisations need to track whether they are ‘decoupled’ from the rest of the sector regarding a specific issue, and therefore at greater risk because they stand out. Being out of step with the sector on an issue exposes a company to potential targeting by NGOs and therefore to greater risk of reputational damage.

How to address future risks

Monitoring and managing the known risks is only one dimension of the framework needed to build reputational risk insurance. The ‘unknown unknowns’ as posited by Donald Rumsfeld, could derail any attempt to structure reputational risk insurance – you can’t insure against the unknown events which you don’t know will affect you. Two decades on from Mr Rumsfeld’s speech, however, we have the tools to expose the unknown.

Technology, including machine learning and topic modelling, enables analysts to surface new risks – identifying issues that were previously unconsidered –and so protect against them. By looking at the organic growth of negative reporting around certain topics, organisations can determine what they need to change their position on.

It is now, therefore, possible to create a quantitative framework upon which to build a reputational risk insurance policy and which can in turn be used to objectively assess when such a policy is triggered and for how long. The challenge for the insurance companies remains how to put a price on it.

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