Description: Claims are insurers deepest and richest transactional data – yet we rarely unleash the full potential of this data. In this session, we will discuss potential sources and attributes of claims data, traditional approaches alongside new analytic techniques and how to integrate models into existing claims management processes. During this session, the presenters will discuss different areas of how machine learning can drive significant value unleashing the potential in claims data, these include data capture, claims management and workflow design as well as behaviour prediction. In addition, the audiences will also have opportunity to learn different use cases of how claims models taking advantage of machine learning, such as foreign death fraud detection and critical illness triage.

Description: This paper presents a statistical and mathematical method to calculate Stop-Loss Premiums. It also includes a discussion of the paper. From Transactions of Society of Actuaries 1976, Vol. 28.

Description: This paper develops a theory for the valuation of some kinds of interest sensitive cash flows. A process is derived which takes as input, assumptions about the capital markets and policy holder behaviour, and produces as output interest and decrement assumptions which can be used for conventional actuarial discounting. The methodology is illustrated using some idealized, but tractable, models of conventional non-par whole life insurance. The method is also applied to a simple Universal Life plan. The paper concludes by considering some of the problems encountered when applying the ideas to more practical problems.

Description: This report explores the behavioral factors influencing insurance purchase among Gen Z and Millennials for forward-looking purposes, leveraging a rich dataset spanning 22 markets across six continents.

Description: It has been a little over a year since the release of the Society of Actuaries (SOA) Research Institute's Mortality Improvement Model, MIM-2021. Since then, waves of COVID-19 have continued to occur in the U.S. The uncertainty around the COVID-19 pandemic and the slowing of U.S. general population mortality improvement prior to the pandemic are just a few of the reasons that make developing MI projection scales difficult. Uncoverthe results from intercompany surveys on mortality improvement, including one that addresses COVID-19, discover insights from a mortality expert panel on COVID-19 and future mortality, and discuss enhancements to MIM-2021. By attending the session, you will: - Understand MIM-2021 and 2022 enhancements. - Understand SOA Research Institute activities around COVID-19 and impact on mortality improvement. - Apply research results and insights presented to daily work.

Description: The following topics were discussed at concurrent sessions: 1. Personal Security in the 1970's- United States and Canada, 2. Adjusted earnings for life companies, 3. Pension funding, 4. What about these younger actuaries?, 5. Sources of statistical information for actuaries, 6. Variable life insurance, 7. Models and decision techniques, and 8. Role of the actuary in expense control. From Transactions of Society of Actuaries 1970, Vol 22, Pt. 2, No. 63.

Description: This paper explores the flexibility of kernel estimation as a means of nonparametric graduation and relates it to moving-weighted-average graduation. From Transactions of Society of Actuaries 1995, Vol. 47.

Description: In the light of growing adoption of AI in life insurance and with it a growing emphasis on the need to safeguard the data used against reidentification,traditional approaches to de-identifying data mitigate these risks but often fail to protect against more sophisticated attacks. Find out more about data anonymization methods, geographic differences in regulation (PIPEDA, HIPAA) and special considerations when dealing with particular data types By attending the session, you will: Gain knowledge of various data anonymization techniques and understand the differences in how data anonymity is defined across different regulatory schemes.