Description: 2021 Life Meeting - Session 8B: Proxy Discrimination in Life Insurance 9.1.21 Recording

Description: Pandemics are a main driver of losses for both life and non-life (re-)insurance. Until COVID-19, risk assessment mostly relied on a few historic examples, most importantly the Spanish flu. The COVID-19 pandemic has shown that other pathogens (namely the coronaviruses) need to be taken into account. Also, modern societies and governments drastically respond to novel threats and implement measures such as social distancing, mask mandates, and lockdowns. Besides the properties of the pathogen itself, these reactions and the medical progression (namely the development of vaccines) are key influences on the course of a pandemic, and thus also the resulting outcomes. To account for the new insights, join us as we take a look at a stochastic, age-structured compartmental model that is calibrated on recent pandemics with several parameters sets to model different levels of reaction speed and stringency between groups of countries. Other important factors also must be defined in close collaboration with experts. Get ready to explore the development of a model and framework that can provide potential outcomes to be used as the basis to derive both summary and tail statistics to assess pandemic risk and help price complex deals.

Description: Regression to the mean (RTTM) is inflating your health care intervention evaluations if you are not carefully managing it's omnipresent and significant impact RTTM is the statistical phenomenon which arises when comparing sequential data points when an initial observation is extreme or an outlier, and states that with no other intervention, future observations will be closer to the average. Members who had high costs last year will, on average, have lower costs this year, even in the absence of any intervention or clinical program, simply by 'regressing' to the mean. This means that if your clinical programs are simply targeting members who had adverse medical experience last year, you’re targeting the wrong people. And if your savings methodologies are simply last year’s costs minus this year’s costs, the savings are inflated. By attending this session, you will be able to identify situations where RTTM will be a significant factor, how to eliminate it's impact when performing retrospective evaluations, how to identify prospects for clinical intervention with RTTM in mind, and more.

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

Description: Attend if you are an organization interested in utilizing various kinds of client data to improve the whole insurance sales process, and to develop new applications. Interested parties may include distribution sales force, underwriting, claims, actuarial and finance departments within a typical insurance company, online distribution partners, health care providers and other consulting service providers. The research looks into various use cases, which are typically main issues in modeling in the insurance industry. The research also helps benchmark performance using different languages that may serve as a good reference when best to use a certain language to enhance the development cycle.

Description: ALM and hedging can reap significant benefits from innovation in computation and operations research. Explore applications of static and dynamic optimization in two different ALM and hedging use cases. Insurers are increasingly moving offshore by establishing Bermuda captive reinsurers. In calculating the Bermuda Best Estimate Liability ('BEL'), reinsurers will need to segment assets to support liabilities in each treaty. A Linear Programming ('LP') problem can be formulated to tactically select assets in a way that minimizes the BEL and consequently the total asset requirement. Learn more about what US Variable Annuity Insurers face with the regulatory requirement to reflect 'future hedging strategy' in their statutory funding calculation. The traditional numerical method of finding the optimal hedging target and then allocating assets may encounter the curse of dimensionality with expanding product and economic environment complexity. A Reinforcement Learning algorithm may well fit this problem and provide insurers a solution for dynamic hedging.

Description: This article gives a brief overview of ways to extend clustering analysis beyond basic methods such a k-means

Description: CMS has required hospitals to publish their chargemaster rates, cash discounts and negotiated rates as of January 1, 2021. Our panel is comprised of a data strategist, an executive from a large hospital system and an actuary.

Description: Data science, predictive analytics and AI are certainly the buzzwords these days. They are heavily used and developed in underwriting and marketing but for most actuaries, they might/may as well be science fiction. The presenters will demonstrate how a normal actuary can utilize data science techniques in their daily job function without forming an official data science team within a company. It can be done in a gradual fashion so that normal business is not disrupted and improvements are made over time. The usage of data science techniques will include demonstrations of the following areas: - Regression analysis - Economic scenario generator - Experience study - Data quality checks - Inforce compression for explicit hedging

Description: This session is a primer on how to apply Gestalt psychology to display quantitative information to improve neural receptiveness. While much of brain functioning is still a mystery to psychologists and neuroscientists, we do understand a great deal about which parts of the brain engage or disengage given visual cues. Actuaries are often tasked with presenting very technical information and are prone to focus more on the substance of numerical data rather than a pictorial presentation based on neural preferences. This presentation will discuss how to display quantitative information more effectively tailored to these preferences.