April 28th, 2016

Emerging Risks: Modeling Considerations Moving Forward

Posted at 1:00 AM ET

william-garland-sm3Will Garland, Managing Director

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Technological progress is accelerating at a rapid pace and with it are the risks and opportunities that accompany those changes in many different segments of our economy:

  • *Exposures to cyber-attacks are increasing due to proliferation of technology, cloud computing and electronic commerce.
  • *Medicinal advances, pharmaceutical breakthroughs and a continued focus on healthy life styles are prolonging our lives, increasing exposure for annuity or pension plan providers.
  • *The cost of medical services continues to increase, putting pressure on health (re)insurers to price their products correctly as well as adequately quantifying potential claims volatility over multiple years.
  • *New chemicals are being used every day in new products - and these products could lead to diseases that are not yet quantifiable, creating unknown implications for any number of product lines.
  • *New mechanical products and processes, such as drones, 3-D printing and self-driving automobiles are transforming the liability potential for (re)insurers in many different industries.

The emergence of these increasingly complex global risks is challenging the way the (re)insurance industry evaluates, analyzes and manages these new exposures. The industry needs to build credible models of potentially accumulating incidents so that risk appetites can be aligned with the exposures being faced. However, the problem with emerging risks is the lack of historical data with which to build these models since many of these risks continue to evolve and change.

Consider the evolving nature of cyber risk. Cyber-attacks are now considered to be above average in terms of likelihood and impact, according to the Marsh 2015 World Economic Forum Global Risks Report (1). At the same time, data breaches continue to evolve, with the potential form(s) and target(s) of the next cyber-attack being unknown. As a result, there is little information to assess the severity and frequency of possible cyber catastrophes, particularly if multiple insureds were to be implicated in a single breach.

Meanwhile, longevity risk is beginning to raise concerns. The United Nations expects the aggregate expenses of the elderly will double over the period between the years 2010 and 2050. Beyond the impact on governments, this directly challenges pension fund managers and annuity writers who are assuming whole-of-life financial liabilities. Faster than expected improvements in mortality can result from improvement in underlying population health or dramatic changes in the underlying prognoses for specific pockets of individuals. The issue for (re)insurers regarding longevity risk lies in the inability to measure it because changes in longevity are difficult to forecast.

Clearly, we are entering a new phase of technological advances that will bring new exposures that were not present in any historical database. For example, two areas where technology risk has rapidly become apparent are nanotechnology and drones. Chemical technology breakthroughs from nanotechnology involved in making stronger and enhanced materials may have unknown liability outcomes many, many years in the future. Drones and other technological advances remove human input into the machine’s operations.

The commercial applications are increasing rapidly and potential for misuse of this technology needs to be considered. With an unknown liability potential, (re)insurers will need to develop models that are based more on estimates and assumptions than on experience.

Implicit in the very idea of emerging risks is the impact on reserving and capital setting. A single product or another high-tech risk can result in a chain reaction that can produce losses over several accident years’ reserves simultaneously and even expose a company to insolvency. If these exposures are unaccounted for and appropriate reserves provisions have not been made, adverse reserve development would be a likely result.

The best historic example is the liability from exposure to asbestos. A.M. Best has stated that asbestos has cumulatively paid out over USD85 billion, and by some accounts after several decades, is just entering its third wave of emergence (2).

A casualty catastrophe model must contemplate the complexities of damage and liability that will not be contained to one geography or one industry. GC ForCasSM has been developed as a platform with model components to cover U.S. commercial lines losses resulting from casualty catastrophes. It is an experience-based model that groups historic losses into three main perils, or modules: Sudden Disasters, Financial Institutions and Cyber. An additional module covering Products is in development. GC ForCas leverages a variety of industry sources to model loss scenarios and line of business dependencies. Through the modeling process, industry portfolio concentrations will be uncovered by mapping exposures and analyzing the interrelationships among those industries.

The modeling of emerging and casualty catastrophe risks remains challenging and the models continue to vary in their experience and exposure based approaches, level of development and industry acceptance. However, there is one consistent theme across all of them: to bring a robust analytical thought process to better understand and, to the extent practical, quantify the risk. This will create a marketplace where these risks can be transferred to third parties or retained with greater confidence by (re)insurers.

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Note:

1. Global Risks 2015 (10th Ed.), World Economic Forum, Geneva, 2015

2. A.M. Best: Asbestos Losses Fueled by Rising Number of Lung Cancer Cases, October 2013

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