Description: A new wave of life insurance buyers is finally here - but are we ready? In this article, Richard De Sousa shares proprietary research about the Millenial generation, its beliefs related to life insurance, and its preferences when it comes to learning about, purchasing, and engaging with life insurance products, advisors and companies. Also explored is the foundational role that technology and wellness play in driving the kind of experience that will attract millenial buyers. After reading this article, you will better understand the characteristics of this new generation as well as the innovative companies that are targeting them, and learn about different and novel ways to better engage with customers and enhance the value of your own products and services.

Description: Artificial intelligence (“AI”) adoption in the insurance industry is increasing. One known risk as adoption of AI increases is the potential for unfair bias. Central to understanding where and how unfair bias may occur in AI systems is defining what unfair bias means and what constitutes fairness.

Description: The Society of Actuaries (SOA) Research Institute received a grant from the U.S. National Science Foundation (NSF) to educate insurance actuaries on the use of wildfire risk datasets and models to be developed by researchers from academia and the U.S. National Aeronautics and Space Administration (NASA).

Description: Society of Actuaries (SOA) Research Institute’s Committee on Finance Research (CFR) and SOA Investment Section are interested in an exploration of the impact of Artificial Intelligence on investment professionals from a variety of perspectives. The result of this effort is intended to provide a useful resource for readers to become well-grounded on the issues surrounding the impact of these tools in an investment context and set the stage for future research.

Description: Emerging technologies like GenAI will be more disruptive than past technological developments, such as spreadsheets, low-code data and dashboarding tools, and cloud computing. This article will explore how how we can lead ourselves, our teams, and our organizations to thrive within a defined modern value proposition, using one of two archetype roles: actuaries as business partners or actuaries as developers.

Description: Regular update provided by the Chair of the Joint Risk Management Section on initiatives put forth by the section council for the current year.

Description: PartnerRe Life and Health is rapidly growing its presence in global markets. This growth means that our medical underwriting team has to handle 25 percent more cases every year. To manage these additional cases without increasing headcount at the same pace, the team has to operate more and more efficiently. In a market where response time is a key differentiator, we improve efficiency by using machine learning to triage and prioritize the underwriting team's case work. In this talk, we describe the model pipeline end to end. That is, we cover the entire implementation chain from accessing data to the eventual evaluation of the added value. In particular, we discuss often neglected topics such as model risk mitigation, technical deployment of the model and integration into business processes.

Description: Natural Language Process (NLP) techniques can be used to find insights in large unstructured data sets and Word Embeddings are pervasive on a wide spectrum of NLP applications. These word representations improve downstream tasks such as Sentiment Analysis, Recommender System, and Topic Modeling. Learn how NLP transforms the Insurance Industry; and how word embeddings and pre-trained models improve the effectiveness and performance of NLP applications. NLP is increasing in importance for insurance companies. Additional application examples are available in session 36.

Description: The article explores how integrating AI into insurer apps can address critical challenges in healthcare, such as patient no-shows, medication adherence, and cost management. These non-disruptive approaches aim to increase the effectiveness of patient interventions and improve medication adherence.

Description: Principal component analysis is an unsupervised linear transformation mainly used for dimension reduction. Determine key numerical variables with maximum variances in a dataset, identify the correlation and removal of redundant variables are the key aspects of this exploratory data analysis.