For each life catastrophe peril, models are developed from a detailed understanding of the fundamental causes, and combined into probabilistic simulations of thousands of potential scenarios.
Sampling from the full range of potential attributes of the viruses that
can cause a future epidemic generates a range of pathogen scenarios
Each pathogen scenario is simulated using population spread models. Government response measures and medical treatments are simulated. Insured populations have different death rates to the general population.
Insurance losses from compensation payouts are calculated. The universe of scenarios provides an exceedance probability of loss that can be used for risk capital estimation.
Infectiousness: High (R0 of 2.25)
Virulence: Moderate (1% Case fatality rate)
Age profile of mortality shows highest impact on ages <40
40% of the population are infected in 6 months • Tamiflu stocks exhausted Vaccine is late to arrive and has low efficacy • Primary healthcare and hospital capacity is overwhelmed • Causes excess mortality of 0.6 per mille on a typical life insurance portfolio
The RMS Longevity Risk model consists of thousands of scenarios of how the future may develop. Each vision of the future is a combination of the way each cause of mortality improvement may play out. This approach blends best-of-class actuarial techniques with medical science.
Statistical projections of past mortality experience are proven and powerful tools of actuarial expertise. But extrapolating past volatility can project medically implausible futures.
RMS enhances statistical projection models by adding future projections by causes of improvement, using five 'vitagion' categories. This provides realistic medical constraints on what could happen in the future
The parameterization of vitagion models is informed by a detailed research program of best-of-class data from many other disciplines, combined with sub-models of medical outcomes
Example of an extreme trajectory
of improvements from the RMS
Smoking becomes de-normalized; Obesity trends slow dramatically.
Cancer management improves dramatically.
New monoclonal antibody drugs are effective and cheap.
Continued reduction of premature deaths from cardiovascular disease.
Insurers managing risk capital requirements for both life insurance exposure and annuity liabilities need to understand the causes of fluctuations in both. Scenarios of the causes of change in future payouts enable the understanding of how different books of business offset each other. Understanding the causes of change provides insights into correlation structures and enables efficient risk capital management.
Cash flows of life books and annuity portfolios are modeled, including contract terms and duration. Each scenario makes a difference to the present value of the two cash flows.
Analyzing the differences from the full set of possible scenarios provides correlation metrics between the two books of business for volatility of mortality and longevity trend risk.
These demonstrate a clear value in offsetting mortality risk with annuity risk.