The obvious response to the issues emerging risks provide is to make sure reserves and capital position are more than robust enough for any eventuality - however remote - and then release them when the risks fail to materialize. But, there are many arguments against this as a practical strategy:
Posts Tagged ‘loss reserves’
The chart below attempts to illustrate the solvency calculation issue. Suppose the best estimate is 20 and the assessment from modeling is that the 1-in-200-year ultimate loss is 100. If all else stays the same and with the simplifying assumption that the yield curve stays flat, one can say that the sum of the 1-year solvency capital requirements (SCRs) approximated the difference between 100 and 20 (i.e. 80). Yet, because of the discounting, when in time the change in own funds is recognized, is important. The black line represents a linear recognition pattern so the 1-year SCRs are all equal with increments of 10. The blue line represents a Binary Fast recognition so the first year SCR is 80 and the remaining years’ SCR are zero. This means that the deterioration is recognized quickly. The red line again shows binary recognition but with a slow pattern as the movement is only occurring toward the end of the liabilities’ life. The two curves in light blue and light red represent less severe versions of the binary forms.
As discussed in the Executive Summary of this report, the term “crystalization of risk” refers to the timescale over which we realize that the risk is manifesting itself and how this view changes until ultimate understanding of quantum is reached and all liabilities are discharged. The “Reserving Risks” section in last year’s report, Ahead of the Curve: Understanding Emerging Risks looked at how information emerges in the presence of reserving cycles. The profit or loss in any particular financial year is made up of not only the profit or loss from the same accident year but also any recognized changes in the reserves on prior years.
Once the risks have been identified and ranked, the next step is how to quantify the likely impact on the financial results of the firm. The first and most obvious question is what available quantification techniques are available for each risk on the list. This will depend on the availability of relevant data and commercially produced models.
Loss reserves are arguably one of the most difficult risks to estimate and monitor. In fact, inadequate pricing and deficient loss reserves have been the leading cause of property/casualty company impairments. According to A.M. Best, from 1969 to 2009 they triggered approximately 40 percent of all impairments - four times more than those emanating from natural catastrophes (1). There are many uncertainties in managing long-tailed, heavily legislated lines of business that can be triggered from emerging risks. Unforeseen inflation and anticipated legislative changes over a 10 to 30 year period present many demands. In order to prepare for emerging risk scenarios, future trends and related uncertainties need to be explicitly identified, contemplated and estimated.
Guy Carpenter & Company announced the release of the 2015 Insurance Risk Benchmarks Report titled, Risk and Opportunity In the year of ORSA: Annual Statistical Review. The report is produced annually through Guy Carpenter’s ongoing Insurance Risk Benchmarks research project, which focuses robust analytics on risk and performance in the U.S. property/casualty (P&C) insurance industry.
Crystalizing risks, as defined in Guy Carpenter’s 2014’s emerging risk report, are highly interrelated with the technology risks discussed in this year’s report. When we refer to crystalizing risk, the term refers to the timescale over which underwriters realize that the technology risk is manifesting itself — and how this view changes and intensifies until ultimate understanding of quantum is reached and liabilities are discharged. The risks associated with new technologies, implemented rapidly on such a global scale, by their nature operate to a large extent somewhat outside the bounds of our current knowledge. A viable response is therefore to establish business practices that aim to detect “weak signals” and monitor them in case they become “clear tendencies with a high potential for danger” (1). Most (re)insurers have groups of experts assigned to the task of building early warning systems that attempt to identify such lead indicators. Once such indicators are identified it is important that their financial and reserving implications are recognized promptly and accounted for correctly. In this respect a key task of regulators is to enforce prudent risk management and reserving methodologies that preserve a sustainable and level playing-field for responsible competition.