Description: This report proposes a methodology to isolate crop yield improvement due to technological change and favorable and unfavorable weather.

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: Artificial Intelligence (AI) has become a key component of the insurance landscape and getting full value from an AI depends on much more than simply building the model. Please join us to hear the challenges and potential solutions around successfully deploying an AI-driven initiative within an organization. Hear from panelists as they describe the human impacts of model implementation, how to assess a model for effectiveness and (lack of) bias, and detail the journey of implementing AI-driven underwriting models at a large insurance company.

Description: Further explore the pipeline of large claims approaching in the future in this webcast. Some of these upcoming claims include Cell and Gene Therapies, as well as high medical costs. In order to prepare for the multitude of claims, this session provides the insurance industry with an overview of what these large claims consist of. You can also expect important recommendations on how to properly prepare for them to succeed in this market.

Description: This is a list of links to research papers on predictive modeling.

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: View 2017 Kaggle Involvement Program Results.

Description: Early in his career, Ehren Nagel, FSA, MAAA, scoured through podcasts and articles about predictive analytics, long suspecting this was a field with potential for him.

Description: The Predictive Analytics Seminar is a unique event that allows experts in the practice areas of life, health, and P&C to come together and explore the vast world of predictive analytics.

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.