Description: Reflection on three years on the PAF section council and where we're headed next.

Description: An introduction of predictive modeling techniques that are applicable to insurance industry, including generalized linear model (GLM) and classification and regression tree model(CART). Other models such as data clustering, neural network, and support vector machine (SVM), are also briefly discussed.

Description: Actuarial tools now include both open and closed source software; both need a strong model governance framework to mitigate potential sources of model risk and external threats. This presentation will discuss how to develop, install and monitor a model governance framework to safeguard company modeling assets and intellectual capital.

Description: Crop insurance plays an important role in mitigating food production disasters and keeping a robust food production system in the United States. Crop insurance increases stability in food and fiber production, smoothing variability which would otherwise occur because of weather patterns and changes in international trade regulations and markets. Stability increases the ability of farmers to make long-term investments that increase production efficiency and keep productive land in agriculture.

Description: Data used for HAT at 2020 PA 4.0 event.

Description: Toward a Predictive Analytics Framework files

Description: "Risk adjustment (RA) is a key component for CMS Medicare Advantage program and California Medicaid Managed Care program. While both RA programs follow generally accepted basic principles at a high level, their underlying methodologies and assumptions are also quite different. Due to recent events such as COVID-19 Public Health Emergency and Medicaid redetermination delay and restart, it adds complexity to Risk Adjustment. This is a hot topic in the industry and has significant impact to health plans. Keeping current with risk adjustment program changes and understanding the commonalities and differences of these two government risk adjustment programs is important to our health actuaries. Compare these two risk adjustment programs so you can learn key elements of a risk adjustment program, similarities and differences in methodology and assumptions , as well as special considerations due to serving different populations."

Description: Human longevity is one of the great success stories of the past century, and there is growing consensus that there are further gains to come. What will these future gains look like, where will they come from and how likely are they to happen. Furthermore, how do we as actuaries deal with the uncertainty when trying to predict future long-term mortality improvements more than 20 years into the future. This is one of the most important assumptions on the balance sheet that insurers and reinsurers make, so it is important to have a strong perspective on it. Our presentation will give attendees a view into some of the core components of how to assess long-term mortality improvements by looking at the cyclical nature across different countries of life expectancy. It will also deep dive into the different drivers of life expectancy over the past cycle driven largely by improved diagnosis and treatment of cardiovascular disease (CVD) and cessation of smoking, which ended in about 2010. It will then discuss how population mortality improvement has slowed and plateaued in many major countries.

Description: Internal modernization initiatives and external changes of the financial reporting regulations (e.g., LDTI, IFRS 17) increase the amount of validation activities for actuarial models. In addition, the use of predictive analytics in actuarial modeling requires a different set of validation skillsets/techniques in model validation. These internal and external forces call for a structured, streamlined and robust model validation practice.

Description: This report provides actuaries and other professionals working with risk, data and analytics or program evaluation in the health care space with considerations and frameworks related to the use of determinants of health (DOH) in data and actuarial work streams. Such frameworks can leverage new data sources to enrich health care data analysis to move beyond traditional data elements, such as age, gender, zip code or health diagnosis.