Description: Predictive analytics projects in the life insurance industry are oftentimes set up as research ideas leading to intangible or unusable results for the practicing actuary. What if you could change that? What if the research turned into development and practical use through leveraging valuable resources properly? Learn how to structure predictive analytics projects, and about the project team, so there will be actionable takeaways for the business unit. Specific areas of value for you include underwriting, experience studies, assumption-setting, and product development. Watch as presenters talk through real case studies on turning a predictive analytics project into workable solutions rather than a resource drain, an unclear goal, or a misunderstood business need, which are some of the pitfalls of a poorly planned project.

Description: The old adage 'insurance is sold not bought' has been used to dismiss digital insurance and support the old agent model of distribution. However, modern distribution systems address this with personalization. This is not simply a customized application form based on age or gender. Personalization is much more dynamic and happens in real-time. It's about tailoring the end-to-end experience to meet the needs and expectations of the specific applicant. The core tools to achieve this are AI learning and nudges from behavioral science.

Description: Come discuss inforce management techniques that go beyond the traditional methods of adjusting non-guaranteed elements within contractual limits. Methods such as retention, cross-selling/up-selling, wellness, exchange programs and other customer behavior initiatives will be presented with relevant considerations.

Description: Agility, responsiveness, and real-time access to data are critical today, and they become even more critical tomorrow. As business leaders, we need to drive a more responsive decision-making culture, and we can do this by utilizing the concept of continuous improvement. Discover how an agile company can increase its financial IQ to improve the speed, openness, confidence, and frequency of finance-led decision-making - and ultimately position finance as the strategic core of the organization.

Description: Models are meant to represent reality, and they often do so quite well. However, they cannot make decisions that contradict the modeling algorithm or underlying assumptions and data. Neither do they possess emotional intelligence or the ability to adjust for social equality. As complexity of models evolved to predictive analytics and machine learning, reliance on model results has increased as well. Over the last decade, as actuaries, modelers and data scientists looked closer into their models, they often identified certain social biases in model assumptions and algorithms. From the use of indicators like credit scores to geo-social segmentation, insurance models have often skewed results in favor of certain socio-economic groups. Underlying this segmentation, one can identify bias around access to health care and education, socioeconomic groupings and financial literacy.

Description: This session will discuss what is meant by 'bias' and 'fairness', the regulatory environment, how to identify and mitigate bias in models, and proper oversight and governance of model building. The session will include a case study.

Description: The presenters will discuss market impacts of Affordable Care Act (ACA) risk adjustment results with a focus on recently released 2024 results. Their discussion will include the challenges of a single risk adjustment model and the growing state variations that impact risk dynamics.

Description: This session will explore governance considerations for insurance carriers leveraging machine learning models. During the session, panelists will share insights on the evolving predictive model and AI regulatory landscape, as well as techniques for developing or enhancing AI governance frameworks that comply with emerging regulatory guidance.

Description: Many insurance companies have started Diversity, Equity, and Inclusion (DEI) initiatives over the past few years to better engage with their employees and communities in new ways. Explore another important audience of DEI programs: the customers who purchase insurance products. Review research on the unique needs of specific groups that may have been underserved by insurance companies in the past. You will also touch on ways to make the insurance buying process more accessible to everyone.

Description: In this AUW-inspiring session, we'll dive into a decade-worth of accelerated underwriting (AUW) random holdout and post-issue audit data collected across 30+ industry programs and learn monitoring best practices directly from two life insurance underwriting experts on the 'front lines' of monitoring. From the industry study, we'll explore misclassification trends, including the characteristics of applicants most likely to be misclassified in a random audit and how we use this monitoring data to estimate excess mortality 'slippage' in AUW programs. From first-hand experience, underwriters will highlight the challenges and benefits of various monitoring methods, including resource constraints and how targeted auditing can be leveraged to guide future program enhancements. Lastly, we'll discuss when year-over-year trends and claims experience become credible, and in the meantime, how to look for value beyond the slippage estimate to improve fluidless underwriting programs moving forward into the next decade and beyond! Learning outcomes: 1) Gain a solid understanding of common monitoring practices in accelerated underwriting programs 2) Explore an in-depth monitoring study of 30,000+ random holdout and post-issue audit cases across 30+ AUW programs spanning over a decade of issue years 3) Learn how to use monitoring data to estimate excess mortality 'slippage' in AUW programs 4) Discover how monitoring insights and techniques can be leveraged to improve fluidless underwriting programs moving forward