Description: There has been a shift in finance and actuarial modernization in the insurance industry over the past 2 to 3 years. Discuss the latest trends in the marketplace and the key changes we see actuaries and actuarial employers needing to make in the future of work. Learn how actuarial work is evolving and about the changing manner in which work is performed. Brainstorm specific ways actuaries can thrive in the future, including performing more strategic work.

Description: Highlight the importance of assigning granular policyholder behavior assumptions to cohorts of a block that behave differently for universal life products. The case study assumption testing will focus on potential cash flow impacts of surrender, lapse and premium persistency behavior— individually and combined. The case study will be based on testing data related to typical demographics for a ULSG product and not on any specific company data.

Description: Predictive analytics techniques help actuaries and their companies make better decisions in managing their business.

Description: Our panel will discuss techniques for efficiently modeling assets and liabilities while minimizing the computational expense of full-blown nested Monte Carlo simulation. These techniques include: • Neural networks • Data clustering and selection of representative contracts • Scenario reduction • Proxy functions for estimating the value of complex assets and exotic derivatives embedded in liabilities. Attendees will learn about the theory behind these techniques and implementation practicalities, and we will present case studies illustrating their benefits.

Description: A three-minute neuroscience-based image exercise is able to accurately unlock the emotional state of individual employees, permitting prediction of a host of specific needs. Actuaries can use this quantitative information at scale to target resources, benefits, and supports to increase the objective wellness and subjective well-being of workforces.

Description: In this video Jing Lang emphasizes the importance of ethical considerations in AI utilization, particularly focusing on privacy, bias, transparency, and creativity. It highlights the need for collaboration among various stakeholders to address these concerns through regulations, diversity promotion, and ongoing discussions. Sources of bias in AI, such as underrepresented datasets and historical biases, are identified along with strategies to mitigate bias, including careful assessment of inputs and retaining algorithms. Contributors: Jing Lang, FSA, FCIA, MAAA FLMI; Scott Allen, FSA, MAAAA, Henry Chen FSA, FCIA, MAAA; Jon Forster ASA, MAAA

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: Data science is being used to create more algorithms used across a variety of applications, including underwriting and pricing. As data science gains traction, it is imperative that we as actuaries consider the ethical implications of use of these algorithms. This session will discuss techniques for evaluating whether your algorithms are inadvertently making biased decisions and considerations for ensuring that your algorithms minimize risk of making biased decisions. Learn to identify potential ethical issues with their algorithms and techniques to address them.

Description: Dana M. Peterson, chief economist for the Conference Board, has established herself as a visionary, and the go to expert providing insightful analysis and predictions to corporations, associations, and government organizations looking for clarity and foresight into how economic drivers might impact their businesses in the short- and long-term. With over two decades of experience in the field, she serves as the globally recognized think tank’s public face and primary spokesperson on all things related to the economy.

Description: "Discuss research conducted at the Cleveland Clinic to understand levels of anxiety and depression across Northeast Ohio. Specifically, learn how advanced analytical techniques were utilized with social media data from Twitter and Google to estimate the need for psychological support by geography and overtime. The panel will go further to demonstrate how natural language processing (NLP) and machine learning could be used to make operational decisions at a leading health care provider."