Tag Archives: risk management

Solvency II presents challenges on management actions

Milliman’s Elliot Varnell, Jeremy Kent, Russell Ward, Russell Osman, and Andrew Gilchrist published a new paper on InsuranceERM.com assessing the implementation of management actions (subscription) to reduce technical provisions and capital charges by firms as well as their desire to carry over credit previously taken for these actions into Solvency II.

Here is an excerpt from the paper discussing the modelling of management actions:

In order to take credit for management actions, the actions need to be reflected in the model used to calculate the best estimate. This can be challenging where actions depend on the solvency of the insurer, creating a circular logic in the calculations which need sophisticated techniques to solve or model simplifications to remove the circularity.

There may be a number of actions available and the action, or actions, taken will depend upon the circumstances applying at the time. Some modelled management actions may not be undertaken in circumstances where the model assumes they will be taken. In this case, the board will need to consider whether it remains appropriate to take credit for that action and regulators may well seek justification from firms wishing to continue to take credit for such actions.

There may also be some non-modelled actions which will be available to the insurer in adverse circumstances. For example, a mutual insurer will need to reduce, or remove, discretionary policyholder benefits in significantly adverse circumstances to meet minimum benefits guaranteed to all policyholders. If the mutual is currently well capitalised, then it is unlikely to be modelling such an action, because the consequential reduction in liabilities and capital would be small. That is, the implicit margin included in the reserves by not modelling that action would be small. However, if capital becomes constrained, then the implicit margin would increase and it is likely the model would be enhanced to include that action.

For more insight about management actions and how they provide a crucial link between Pillar I and Pillar II of Solvency II, read this research paper on dynamic management actions.

New ideas on stress testing are 300 years old

Milliman’s Neil Cantle and Martin Neil (of Agenda LTD) assess the implementation of Bayes’ Theorem as a solution in performing stress tests for capital allocation under Solvency II in this article published in InsuranceERM.com.

Here is an excerpt from the article:

The Bayesian recipe for successful stress testing involves blending expert insight, narrative history and data. But how does one go about cooking these ingredients? We know that traditional “frequentist” risk concepts get us only so far, so we need a different way of cooking our stress test model.

Compared with the frequentist approach, modern Bayesian approaches to stress testing are significantly more flexible and powerful in situations where there is little data but where we have some explanatory insight into the mechanisms underlying the risk process and could use this as the basis of the model.

You might think that this approach is inherently subjective. It is. But that doesn’t mean that it is “simply making up the numbers”, because it is a highly pragmatic realisation that all models are just that — models.

As such, there is no single “correct”, unassailable, objective model out there awaiting discovery. It should therefore be obvious that differing enterprises facing different risks, with diverse histories will develop different constituencies with dissimilar views on the risks they face. After all, if we all had the same model of risk there would be no marketplace.

Comparing the Bayesian approach with statistical approaches shows that purely statistical approaches are merely phenomenological, in that they do not offer any way to explain why events occur and so offer no means to influence these events in future. In essence, they can be summarised as offering nothing more than a “stuff happens” explanation of risk.

Managing operational risks

As companies implement Solvency II programs, operational risk, often seen as a catch-all for ‘other’ risks, is being recognized as having greater impact than was previously realized.

Modeling and management of operational risks—and preparing companies to be more robust to these risks—are now seen as a key aspects of sound insurance management.

Operational risk is also moving up companies’ agendas because the capital charge under the Solvency II Pillar I standard-formula calculation is a rather crude measure—it is essentially based on business volumes. While this has the benefit of simplicity, it may lead to what could be considered excessive capital requirements and falls short of the principles underlying the Own Risk and Solvency Assessment (ORSA).

A new white paper by Milliman consultants provides a brief summary of how companies are currently approaching operational risk under Solvency II, and offers some suggestions for improvements using innovative techniques.

Here is an excerpt from the paper:

The modeling and management is rapidly moving up companies’ priority lists as recognition is growing of the potentially lethal nature of these risks, their often inherent unknowability and, if nothing else, the significant capital charges that can emerge from the standard-formula approach.

More sophisticated approaches are becoming available that not only integrate the modeling and management of operational risk but also generate insights into the complex risk stream running unseen through the bedrock of a company. This approach allows appropriate risk mitigation and increasingly robust measures to be developed and embedded into business processes.

Download and read the white paper here.

Model limitations for risk management

While model building will always be a core element of insurance, being aware of the limitations of models and techniques to manage those limitations is crucial to successful risk management.

A new article authored by Pat Renzi and Elliot Varnell for the Actuarial Post focuses on the challenges risk managers face given their responsibility for an internal or Own Risk and Solvency Assessment (ORSA) model. The article highlights three issues that risk managers need to make allowance for in managing their models: model scope and historical data; associative dependency; and the problems that arise when elegant models confound transparency and understanding.

Here is an excerpt:

…Model building is at the core of the insurance sector and we will never stop building models. But awareness of their limitations and the techniques to manage those limitations is crucial to successful risk management.

Simplifications and expert judgment are a fundamental part of building models too and should be recognized as such. However they should be applied with full transparency and in full knowledge of their limitations rather than couched in mathematical derivations where they cannot be readily challenged.

The article was published in the July 2012 issue of the Actuarial Post. Read it here.

Risk management in a global downturn

Risk management in a global downturn proved an excellent and thought provoking session.  It began by examining the effect of Greece and Spain defaulting from the Eurozone and the resulting impact on other Eurozone countries, the US, China, their currencies and a generous sprinkling of global political and social unrest added in for good measure to emphasise that “thinking about the unthinkable is an important part of risk management” and that sometimes it pays not to be too optimistic in the world of risk management……

Going forward, scenario testing will play an increasingly important role in the assessment of potential risks with the associated caveat that stress tests are only useful if the evaluation is realistic. It also remains imperative that management information emphasises the fact that the prediction of uncertain events is highly ambiguous.  Add to that reverse stress testing for the FSA and you can see why actuaries need to engage with scenario testing on a variety of fronts.