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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
learning is a branch of artificial intelligence (AI) that teaches a computer
how to analyze and find hidden patterns in data through the use of algorithms. It’s been called a “revolution,” and from self-driving cars to health care, it has begun
to change the way we live our lives. Our Pinnacle University
group explored the emerging world of machine learning and how it fits into the
verdicts (verdicts over $10 million) are becoming more common and contribute to
significant angst experienced by commercial auto executives, underwriters and
claims handlers across the country. A common theme in these matters is how
plaintiff attorneys have been successfully employing reptile theory.
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