Description: The audience will learn about practical applications for actuarial and operational problems by using data science, how to collaborate effectively between actuarial, data science, data engineering, data management, legal and others and specific questions they need to ask the data science team to make effective data science solutions.

Description: The life insurance industry has a lot going on. Come discover some key highlights of the life insurance market mixing quick hits with some in-depth conversations about the headlines and hard lines happening in the life insurance sector. Topics include underwriting, regulation, product changes and distribution to name a few. If you enjoy the PTI show for sports, you will enjoy hearing about the life insurance industry in this format and hosts will run down the headlines of the insurance industry in PTI fashion.

Description: Model runtime is a common challenge for actuaries in the life and annuity space. In particular, applications such as pricing and forecasting may require the use of nested-stochastic calculations, which may require extensive computing resources or use of rough approximations. There are also other areas where runtime may be a challenge, such as projecting certain reserves or layering solving algorithms within actuarial calculations. Recently, runtime challenges have been exacerbated by the development of increasingly complex insurance products and changes in reporting frameworks, such as IFRS 17, LDTI, PBR, and Solvency II. Many approaches have been explored by actuaries in the past to address model runtime challenges. To further this research, the SOA has recently published the paper Predictive Analytics and Machine Learning - Practical Applications for Actuarial Modeling. The authors of the paper will share their insights on innovative techniques using the implementation of artificial intelligence and machine learning (AIML). Come examine an introduction into AIML, review how AIML can be used to circumvent nested stochastic and other runtime challenges, explore case studies, review how costs and benefits can be measured and discuss model risk management considerations. Key learning objectives include: - Understanding the fundamental concepts of AIM - Understanding applications of AIML for actuarial modeling - Learning about the costs and benefits - Learning how AIML can be applied in practice to improve model runtime

Description: This report is a technical paper on a Predictive Analytics Framework and Reusable Workflow for Experience Analysis in actuarial studies. It covers data analysis, machine learning models, methodologies, and model interpretation techniques.

Description: This report provides a toolkit for actuaries and other interested parties to model and forecast mortality by cause and socioeconomic factors.

Description: This report provides a toolkit for actuaries and other interested parties to model and forecast mortality by cause and socioeconomic factors.

Description: For actuarial projections to serve as the management tools that they are intended to be, the persons charged with making those projections need to consider whether the projection is sufficiently sophisticated so that it does not miss major items.It is important to consider the tax effect. This article covers several of the major tax issues that should be considered in any actuarial projection.

Description: The document is a report on U.S. Population Mortality Observations updated with 2022 experience by R. Jerome Holman, FSA, MAAA. It covers mortality trends, analyses by factors like sex, age group, and cause of death, and includes methodological details for the data presented.

Description: This article will be focused on maximizing the reuse of code using features available within Moody’s Axis specifically targeted at using Formula Link.

Description: Market consistent valuations require path-dependent discounting for scenarios. IFRS 17's requirement to discount best-estimate cashflows using locked-in rates could give non-sensical results in many instances. This article describes a technique for calibrating each of the scenario's cashflows so that they can be discounted with other discount factors or curves without distorting the value of underlying options and guarantees.