One sure-fire way of selling insurance policies is to maximize your coverage while keeping premium rates reasonably low. Had we now got this down to a science?
By: Vanessa Uy
Back in the good old days – the previous 25 or more years to be exact – insurance company actuarial mathematicians used to statistically assess risk using a figure called the expected loss. They got it by multiplying the probability of an accident occurring times the damage done by the accident.
Henceforth, policymakers and statisticians of almost every insurance company around the world grown content in using the concept of expected loss as the sole measure of risk. But since insurance companies are always in a perpetual search of ways to “streamline” their economic “bottom line”, the quest is on to create policies that are more ambitious than the one that precedes it. An insurance policy that not only provides coverage for “catastrophes” other insurance providers won’t touch with the proverbial ten-foot pole but also can keep the client’s premium rates down to the absolute reasonable minimum (from the insurance providers perspective at least).
That fateful day came around in 1986, when a mathematician from the University of Virginia named Yacov Haimes and his team developed the partitioned multi-objective risk method or PMRM. Haimes and his team argue that insurance company actuarial mathematicians need to account for catastrophes separately from ordinary accidents in order to provide a better-structured insurance policy, one that maximizes coverage while minimizing premium rates. Rare but expensive (in monetary terms) accidents, the team pointed out could have a small-expected loss given their improbability of occurring.
In his book “Risk Modeling, Assessment and Management”, Yacov Haimes discusses the art of risk management after years of being acquainted and gaining expertise on the subject. Especially it’s important applications in such areas as engineering, science, and even the politically tinged vagaries of public policy. Haimes’ writing style equally covers the quantitative and qualitative aspects risk management by emphasizing how to quantify risk via construct probability together with real-world decision-making problems without ignoring the host of institutional, organizational, political and cultural considerations which these days often accompany such challenges.
Since developing his PMRM, Haimes has co-developed an even newer method of risk assessment called risk filtering, ranking and management or RFRM. The usefulness of RFRM in risk assessment is supported by several case studies cited in Haimes’ book. Given that Yacov Haimes has provided a new focus on minimizing the high cost associated with today’s more extensive risk management, how can all of this benefit us, the lowly policy holder, or for that matter, the whole global economy as a whole?
Ever since our on-going global economic downturn slowly – but inexorably – continues to drive all of us into an uncomfortable sense of fiscal austerity. Whoever can provide products that provide the maximum performance for the least amount of money will not only survive, but can even prosper during these times of economic hardship. If insurance companies can manage to provide us with insurance policies that offer more for less, then both – the insurance provider and the “mere” policy holder – can weather out the on-going global “financial storm” with a comfortable margin of confidence.