I’m often looking at distributions, and wanting to communicate something about how those distributions change over time, or how distributions compare. Often, I have to simply pick out key percentiles in those distributions, or key aspects, such as mean and standard deviation.
But why not graph all the points in one’s sample directly, if one has them?
In a groundbreaking TED-style talk, Dominic Lee, ACAS takes the audience on a multisensory journey beyond the boundaries of traditional insurance. He presents a framework for the actuarial profession to step into the future and claim its rightful place as a dominant force in the world of risk: Reimagine, Embrace and Explore.
The insurance industry is unique in that the cost of its products—insurance policies—is unknown at the time of sale. Insurers calculate the price of their policies with “risk-based rating,” wherein risk factors known to be correlated with the probability of future loss are incorporated into premium calculations. One of these risk factors employed in the rating process for personal automobile and homeowner’s insurance is a credit-based insurance score.
Credit-based insurance scores draw on some elements of the insurance buyer’s credit history. Actuaries have found this score to be strongly correlated with the potential for an insurance claim. The use of credit-based insurance scores by insurers has generated controversy, as some consumer organizations claim incorporating such scores into rating models is inherently discriminatory. R Street’s webinar explores the facts and the history of this issue with two of the most knowledgeable experts on the topic.
[Moderator] Jerry Theodorou, Director, Finance, Insurance & Trade Program, R Street Institute Roosevelt Mosley, Principal and Consulting Actuary, Pinnacle Actuarial Services Mory Katz, Legacy Practice Leader, BMS Group
R Street Institute is a nonprofit, nonpartisan, public policy research organization. Our mission is to engage in policy research and outreach to promote free markets and limited, effective government.
We believe free markets work better than the alternatives. We also recognize that the legislative process calls for practical responses to current problems. To that end, our motto is “Free markets. Real solutions.”
We offer research and analysis that advance the goals of a more market-oriented society and an effective, efficient government, with the full realization that progress on the ground tends to be made one inch at a time. In other words, we look for free-market victories on the margin.
In simple words, PCA is a method of extracting important variables (in the form of components) from a large set of variables available in a data set. PCA is a type of unsupervised linear transformation where we take a dataset with too many variables and untangle the original variables into a smaller set of variables, which we called “principal components.” It is especially useful when dealing with three or higher dimensional data. It enables the analysts to explain the variability of that dataset using fewer variables.
A look at the pattern of weekly deaths, all causes, for the entire United States through the beginning of September 2021, as well as: California Texas New York (minus NYC) New York City Pennsylvania Illinois CDC excess mortality dashboard: https://www.cdc.gov/nchs/nvss/vsrr/co…
Have you ever built a perfect financial model without any errors? Thought not! And for that reason, all good modellers know they need to include some error checks. But what is not as clear is how many error checks you should have, when you should include them and what form they should take. Excel “helpfully” provided us with functions like ISERR, ISERROR and IFERROR but as you progress your modelling journey you should learn to avoid these functions. Plus, you also learn the sad truth that Excel can’t even do basic maths sometimes! Join us to hear from financial modelling specialist Andrew Berg, who has spent years building models, and so happily admits he has probably already made most of the mistakes you haven’t yet had a chance to! The good news is that he is willing to share the tips he has learned about the right types of error checks to add to your models so you don’t have to learn the hard way. ★Download the resources here ► https://plumsolutions.com.au/virtual-… ★Register for more meetups like this ► https://plumsolutions.com.au/meetup/ ★Connect with Andrew on Linkedin ► https://www.linkedin.com/in/andrew-be…
Looking at the NYT article “Which Groups Are Still Dying of Covid in the U.S.?” — online interactive data visualization related to COVID deaths and demographic groups in the U.S. I thought one key graph was misleading
Welcome to another episode of Positivity with Paul, where I find Fellow Actuaries – pun intended – for a conversational Q&A on their life. The focus is on their journey along the actuarial exam path and beyond, some of the challenges they faced, and how those challenges helped shape them to become who they are today.
To give some brief context on becoming an Actuary, there’s a number of actuarial exams that one has to go through. These exams are very rigorous and typically, only the top 40% pass at each sitting, They cover complex mathematical topics like statistics and financial modelling but also insurance, investments, regulatory and accounting. Candidates can study up to 5 months per sitting and they will take 7 to 10 years on average to earn their Fellowship degree. To that end, I launched this series of podcasts because I was curious about what drove my guests to surmount trials and tribulations to get to the end goal of becoming an Actuary.
My guest in this interview is Mary Pat Campbell. Mary Pat is an actuary working in Connecticut, investigating life insurance and annuity industry trends. She has been interested in exploring mortality trends, public finance and public pensions as an avocation. Some of these explorations can be found at her blog: stump.marypat.org. Mary Pat is a fellow of the Society of Actuaries and a member of the American Academy of Actuaries. She has been working in the life/annuity industry since 2003. She holds a master’s degree in math from New York University and undergraduate degrees in math and physics from North Carolina State University. In this podcast, Mary Pat discusses similarities in concepts between physics and actuarial science, the current low interest rate environment and lessons learnt in the insurance sector from the financial crisis in 2008-2009. Hope you enjoy this all-inclusive interview! Paul Kandola