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: An overview of the objectives of outbreak modeling, and an examination of the various types of models used both to forecast an uncontrolled outbreak, and to estimate the effects of interventions intended to alter the outbreak’s trajectory

Description: Emerging Applications of ESG Methods offers ground-up insights on cutting edge applications of economic scenario generation. The course will focus on the incorporation of climate change risk in scenario modeling and investment applications of ESGs. The following topics will be covered: Approaches to climate risk modeling. Climate risk is very different than classical financial economic risk. Radical uncertainty is amenable to scenario analysis and lack of data. Building special deterministic scenarios that go beyond NGFS requirements as well as bespoke scenarios. Holistic Approach to Climate Risk Modeling. Overview of different Climate Risk Modeling Applications. Use of ESG scenarios for Strategic Asset Allocation (SAA). Liquidity risk management.

Description: This report outlines a methodology for estimating mortality improvement factors based on data from insured lives. The report details challenges, methodology, results, comparisons with population data, limitations, and potential improvements.

Description: This report introduces quantum computing to actuaries. In addition, this research will assess and identify areas in the insurance field where quantum computing can make a difference, accompanied with suggested algorithms.

Description: This paper introduces practitioners to the selection and calibration of stochastic interest rate models. Six continuous time interest rate models and their calibration are presented. Actual code and data examples are added to help the practitioner implement them. The emphasis is on hands-on calibration with RStudio. The reader is assumed to have a working knowledge of RStudio, including writing R scripts.

Description: Hear AM Best rating analysts discuss the rating process and the best capital adequacy ratio (BCAR), a quantitative foundation for AM Best’s assessment of carriers’ financial strength. The benchmarking of BCAR scores for various company types (national MCOs, BCBS plans, smaller regional plans) will be discussed. Look at how changes in investment and underwriting environment impacted the results over the past several years. Further, AM Best’s view of the quality of capital and various factors impacting the overall assessment of the balance sheet strength will be discussed.

Description: Modeling Approaches and Challenges focuses on approaches to real world simulation and the challenges and considerations in real world simulation. Topics include: A frequency domain approach to build realistic multi-horizon scenarios. (Ortec approach) Cascade models structure linking models for major asset classes and economic variables. (Moody’s approach) Overview of models, calibrations & approaches within the context of different use cases. Deterministic & stochastic approaches. Importance of model re-calibration flexibility. We will also discuss specific insights into considerations and challenges in real world simulation, which include topics like: Setting capital market assumptions and/or calibration targets. Assumptions about correlation and causality. Calibrating the model to capital markets assumptions. Validating the model against capital market assumptions. Role of history, market data and stylized facts. Importance of back testing. Calibrating the model to multiple investment horizons. Inclusion of alterative asset classes such as private equity, infrastructure, and direct real estate. Associated issues of limited data, illiquidity and greater uncertainty about calibration targets.

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: Life and annuity markets around the world have seen increasingly complex investment-combined products, including universal life, fixed indexed annuities, variable annuities with a wide variety of guaranteed benefits and indexed variable annuities. Insurance companies are facing unprecedented exposure to financial risks in addition to traditional mortality and longevity risks. The risk assessment and modeling for such complex designs by conventional methods often focus heavily on their market competitiveness and profitability. However, there seems to be a lack of quantifiable and systematic approaches to assess investment risks associated with investment combined products. The objective of the proposed work is to develop a framework for quantifying and analyzing various forms of contractual designs and showcase different techniques to assess the trade-off between competitive contractual designs and their practical implications regarding the difficulty of risk management.