A report has highlighted the potential for risk models in the insurance and reinsurance industry to cause systemic risk, signalling a need for underwriting discipline alongside advanced and robust modelling capabilities, an area the insurance-linked securities (ILS) space is now embracing.
A lack of adequate catastrophe models in certain regions of the world and limitations with better-developed models in parts of the U.S. and Europe, for example, can create systemic risks for insurers and reinsurers, underlining the need for sound underwriting practice coupled with improved analytics and greater modelling capabilities.
“As we move into the 21st century the world is becoming more connected, more complex and more uncertain. The latest wave of globalization has integrated markets and finance while the information revolution has compressed time and space. In order to reduce systemic risk, risk governance needs to be strengthened,” said Professor Ian Goldin, Director of the Oxford Martin School at the University of Oxford.
A recently published whitepaper from Amlin, the result of an industry-working group, explains; “A risk model intends to make probability estimates of a risk, which will then be used to make decisions.
“If it underestimates the probability of a risk, actions will be taken in false confidence. If it overestimates risk, resources will be misallocated. If it causes correlated actions across an organisation or market, systematical or systemic risk emerges.”
The use of models in the insurance, reinsurance, ILS and wider risk transfer markets are essential and necessary, providing an excellent view of risk and exposure, but as highlighted by Amlin in its report they aren’t always as accurate or advanced as required to adequately assess the complete risk.
However, as discussed recently by several reinsurance industry executives at a recent conference in Canada, covered at the time by Artemis, models should be viewed as the “skeleton on which you hang your underwriting thinking,” and in no way be used alone, absent common sense, knowledge and underwriting discipline.
“Expanding the investigation to the insurance linked security market and other uses of risk models may also prove useful: such instruments have begun to connect previously uncorrelated markets (insurance and capital) in ways that may pose systemic risks,” explains the report.
As the ILS market has continued to grow at an impressive rate, highlighted in part by another busy year for cat bond issuance and the rise of collateralized reinsurance agreements, the sophistication, understanding and willingness of investors and sponsors alike has matured also.
ILS market participants have become increasingly eager to understand the scope and workings of their catastrophe (and other) exposures, regardless how small of an investment it might be to their portfolio.
While ILS players, as reinsurers and insurers, utilise the benefits of catastrophe models, the increased sophistication of participants and the solutions provided points to a market that is increasingly understanding the need for underwriting expertise alongside the use of models.
Hence ILS fund managers have been staffing up, adding expertise in modelling, underwriting and actuarial analysis, in order to ensure that they have the expertise alongside the risk models, to avoid any systemic risk.
Catastrophe modelling capabilities are expanding all the time, and starting to reach areas where underinsurance and high severity/frequency catastrophe events come together, but a lack of historical data in underdeveloped regions of the globe limits advancements.
That being said, should robust catastrophe exposure models exist throughout the world for all perils and locations, the need for knowledge, common sense, and underwriting expertise will always be essential, alongside the model, enabling a more holistic view of the risk.
In an effort to highlight and mitigate systemic risks posed by models for the insurance and reinsurance sector, the report proposes a Systemic Risk of Modelling scorecard (SRoM), “a practical way to measure the amount of systemic risk introduced from modelling leveraging the Amlin – OMS collaborative research and findings to date.”
“The SRoM scorecard has been designed less towards an exact risk measure and more towards providing an indication of whether certain actions and practices are aligned with reducing systemic risk of modelling.”
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