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For insurance companies who wish
to understand existing markets and enter new and emerging markets, being able
to accurately predict insurance demand is critical. Currently, demand for insurance is
predicted predominately by analyzing economic factors (e.g., gross domestic
product [GDP], inflation rate). However, we often see gaps in insurance demand
between two different countries, despite the similarity of those countries’ economic
conditions. This difference indicates that insurance market dynamics cannot
always be captured through economic conditions, or through an economic analysis. Clearly, some additional new and
improved rating variables would be useful to more effectively predict
insurance demand in different markets.
Most current (or recently graduated) college students are familiar with several of the methods that can be used to finance a college education. The most preferred options are college savings programs (529 plans), grants and scholarships, because they do not have to be paid back. Secondly most-preferred are government loans, which have lower interest rates compared to other loans. Once those avenues are exhausted, however, it is up to the students and their families to find other alternatives. Traditionally, this would mean that students (or their families) might resort to taking out private loans with higher rates than those offered by the government. But what if there was another funding method to consider – one that relied more directly on the quantifiable expected return of the education being pursued?
A decade ago, autonomous vehicles (AVs) seemed
like a futuristic gimmick, out of reach.
Today, however – although the word “autonomous” might
suggest otherwise – most people drive some sort of AV. Various rate filings, both for commercial and
private passenger auto, give an idea of how autonomous vehicles are being
priced with regard to insurance. Although rate filings do not exist for fully
autonomous vehicles, many filings offer discounts for having an
autonomous feature attached to the vehicle.
This raises the question – would fully
autonomous vehicles be even more cost-effective with regard to insurance?
What makes data “bad” or difficult
to work with? For actuarial consultants, data
from clients is crucial to a comprehensive analysis. Although that data may
arrive with any number of issues and challenges, solving those puzzles gives
actuaries the opportunity to demonstrate our ability to adapt, innovate and
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