Jay Vadiveloo, PhD, FSA, MAAA, CFA, is a Professor at the University of Connecticut and Director of the recently endowed Janet & Mark L Goldenson Center for Actuarial Research at the University of Connecticut. Jay has a doctorate in statistics from the University of California, Berkeley, is a Fellow of the Society of Actuaries, a Member of the American Academy of Actuaries, and a Chartered Financial Analyst. Jay has over 25 years of experience with the life insurance industry which includes senior level appointments at Connecticut Mutual, Mass Mutual, Aetna Financial Services, ING, Deloitte Consulting and Towers Watson.
As Professor and Director of the UConn Goldenson Center, Jay works on applied actuarial research projects using teams of academicians, students and industry professionals. Jay’s research work has also enabled him to supervise several PhD students in a variety of topics including integrated retirement financial planning, measuring and analyzing the volatility risk for individual disability income (DI), and analysis of efficient financial modelling techniques. The Enterprise Risk Management for Small Businesses (ERMSB) initiative is one example of the research projects undertaken by the Goldenson Center.
Jay has published several articles in the actuarial literature and is a frequent speaker at actuarial conferences and seminars. One of Jay’s important contributions to the financial services industry is the invention of a patented new algorithm (Replicated Stratified Sampling or RSS) for risk modelling which exponentially reduces processing time at a pre-determined accuracy level for any complex actuarial modelling. More recently, Dr. Vadiveloo has obtained a provisional patent on a claims tracking and monitoring process which allows a company to easily detect significant deviations in claims experience and recognize whether it is only a one-time occurrence or shows a historical trend as well. Dr. Vadiveloo is also editor and co-author of a new text by the Society of Actuaries on Enterprise Risk Management for Small and Medium-Sized Enterprises.
Areas of Expertise (4)
University of California, Berkeley: Ph.D.
University of California, Berkeley: M.S.
University of Malaya: Econ.
- Society of Actuaries, Fellow
- American Academy of Actuaries, Member
Media Appearances (5)
Visualizing The Impact of Social Distancing and Wearing Masks
NBC Connecticut tv
"We are trying to educate the public on how certain actions can impact the rate of infections and deaths," said Jeyaraj Vadiveloo, Director of UConn's Goldenson Center for Actuarial Research. Vadiveloo's team spent the last two months developing an educational model for people to visualize how safety measures can affect the spread. They used a hypothetical community of 1,000 and created a formula that allows people to input different levels of social distancing to see how the infection and death rates change over a three-month period.
Opinion: Here’s how long you’ll live — and how much of that will be healthy years
As the old saying goes, the only things certain in life are death and taxes. While death is inevitable, the quality of life you experience until death is often within an individual’s control. This is what our team at the Goldenson Center for Actuarial Research chose to focus on by developing a rigorous measure of quality of life. How many healthy years of life do you have ahead before you become unhealthy?
This calculator will guess how many healthy years of life you have left
Associated Press online
We’re living longer than ever. But how many of those years will we be healthy? As the old saying goes, the only things certain in life are death and taxes. While death is inevitable, the quality of life you experience until death is often within an individual’s control.
Our calculator will guess how many healthy years of life you have left
U.S. News & World Report online
"This is what our team at the Goldenson Center for Actuarial Research chose to focus on by developing a rigorous measure of quality of life. How many healthy years of life do you have ahead before you become unhealthy?" (...)
We’re living longer than ever. But how many of those years will we be healthy?
National Post online
Want to know your own estimate of healthy years ahead? We developed a free online tool that lets you calculate healthy, unhealthy and total life expectancy.
This simple model shows the importance of wearing masks and social distancingThe Conversation
With the advent of an infectious disease outbreak, epidemiologists and public health officials quickly try to forecast deaths and infections using complex computer models. But with a brand new virus like the one that causes COVID-19, these estimates are complicated by a dearth of credible information on symptoms, contagion and those who are most at risk.
Unlocking Reserve Assumptions Based on the Retrospective Loss Random VariableSSRN
Vadiveloo, Jeyaraj and Niu, Gao and Valdez, Emiliano A. and Gan, Guojun, Unlocking Reserve Assumptions Based on the Retrospective Loss Random Variable (May 14, 2016). Available at SSRN: https://ssrn.com/abstract=2779947
2016 In this paper, we define a retrospective loss random variable and mathematically demonstrate that its expectation is the retrospective reserve which in turn is equivalent to the prospective reserve. By defining an associated random variable for the retrospective reserve, similar to the prospective loss random variable for the prospective reserve, we can further explore and understand various properties of this retrospective loss random variable. In particular, we find and demonstrate that this retrospective random variable can be a powerful tool for providing us valuable historical information on the pattern and significance of deviation of actual experience from that assumed for reserving purposes. This valuable information can subsequently guide us as to whether it becomes necessary to adjust prospective reserves and the procedure to do so. The paper concludes with a model of a block of in force policies with actual experience different from reserving assumptions, and a rigorous and consistent methodology on how prospective reserves could be adjusted based on the realized retrospective loss random variable.
Life insurance policy termination and survivorship☆Insurance: Mathematics and Economics
Emiliano A. Valdez, Jeyaraj Vadiveloo, Ushani Dias
2014 There has been some work, e.g. Carriere (1998), Valdez (2000b), and Valdez (2001), leading to the development of statistical models in understanding the mortality pattern of terminated policies. However, there is a scant literature on the empirical evidence of the true nature of the relationship between survivorship and persistency in life insurance. When a life insurance contract terminates due to voluntary non-payment of premiums, there is a possible hidden cost resulting from mortality antiselection. This refers to the tendency of policyholders who are generally healthy to select against the insurance company by voluntarily terminating their policies. In this article, we explore the empirical results of the survival pattern of terminated policies, using a follow-up study of the mortality of those policies that terminated from a portfolio of life insurance contracts. The data has been obtained from a major insurer which traced the mortality of their policies withdrawn, for purposes of understanding the mortality antiselection, by obtaining their dates of death from the Social Security Administration office. Using a representative sample of this follow-up data, we modeled the time until a policy lapses and its subsequent mortality pattern. We find some evidence of mortality selection and we consequentially examined the financial cost of policy termination.
Multivariate Analysis of Pension Plan Mortality DataNorth American Actuarial Journal
Charles Vinsonhaler , Nalini Ravishanker , Jeyaraj Vadiveloo & Guy Rasoanaivo
2013 This paper uses the logistic regression model to examine private pension plan data for 1989–95 collected by the Retirement Plans Experience Committee of the Society of Actuaries. When only one explanatory variable, such as annuity class size, is used in modeling mortality rates, the model provides a reasonable fit to the data. Multiple explanatory variables give less satisfactory results.