Description: One of the most effective ways to convey quantitative results is via data visualization. However, many times actuaries have been ineffective in doing this. In this session, you will see a demonstration of certain dataviz choices that make communicating results more effective in terms of graph type choice, graph element styles, colors and more. All examples will be in Excel-and doesn't require Visual Basic for Applications (VBA)! Nothing too fancy! I want to distribute working files to attendees before the session so they can follow along with how to build the graphs, make changes, etc. Graph choices will include line graph, tile grid map and jitter graph.

Description: With the unprecedented proliferation in the size and complexity of data over the past decade, actuaries have invested considerable time and energy in the development of new skills and expertise in order to be able to understand and use “big data” to its fullest potential in the development of actuarial work product.

Description: As data continues to become more robust and readily available, there has been a push in the actuarial field to transition away from Excel to SQL and Python native cloud-based platforms. While these platforms are widely used and powerful, they present challenges for the actuarial industry: Requiring processes to be production-ready from the onset, technical expertise for integration and robust documentation. Low-code tools provide a unique alternative solution, which works when the organization's operating model allows for actuarial and IT to work together, giving actuaries the flexibility they need while maintaining a level of control followed by the organization In this session, you will watch demonstrations that showcase low-code tools and their use case in the actuarial space. See how these low-barrier-to-entry tools can be leveraged to create robust processes. We will discuss solutions such as Alteryx and Dataiku and how low-code tools can integrate with coding languages such as Python to create more robust processes.

Description: This session will discuss basic and advanced hedge modeling concepts. It will explain common hedge strategies used to support guarantees and options embedded within life and annuity products and the materiality of these products within the US. Attendees will learn how Machine Learning acts to reduce the computer processing needed to simulate the dynamic hedging of product liabilities projected in the future across many stochastic scenarios. Actuaries working in consulting, software development, and a large life insurance company will provide a wide range of hedging viewpoints ranging from a broad industry perspective to a hedging practitioner. Attendees will obtain a comprehensive understanding of a novel ML approach to modeling dynamic hedges and the hedge optimization opportunity it presents.

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: In this session, actuaries and data scientists will describe how the Cleveland Clinic has utilized large language models to support caregivers with difficult end of life conversations with patients and their loved ones. While artificial intelligence is often prefaced with words of caution, it can also be harnessed for good.

Description: Attend this webcast to learn more on how to navigate managing ESG principles in the new normal of a post-COVID world. Enhance your ESG insights in a post-pandemic landscape.

Description: Model Risk Management at insurers has been an established practice for well over ten years now. Until recently, a primary focus of model governance was on actuarial models. The Gen AI evolution we’ve experienced in the last two years, has shone a spotlight on “all things AI”. Along with the increased capabilities AI brings, there is increased concern for the proper use of these models, protection of IP, safeguarding of PII, and overall security. This session will bring together a diverse panel to discuss how we can adapt our established practices for these new models to minimize the risks that come with Gen AI and help our organizations optimize the benefits of AI.

Description: The frequent use and complexity of today's actuarial projection models has increased the importance of having efficient models. Model optimization can reduce both cloud costs and overall end-to-end clock times, thereby allowing companies to do more with less such as reducing close times, increasing controls, enhancing analysis, doing more complicated calculations, and/or improving employee workloads during the close. From design and data to execution and audit, come explore techniques that can be applied throughout the actuarial modeling process to optimize actuarial models for improved run-time, accuracy, and reliability. There will be a focus on techniques that don't rely on model compression. Topics covered include model design pitfalls, data handling, improving convergence on asset-liability projections, strategies for interacting with the cloud, and other tips for optimizing actuarial models.

Description: Health Insurers commonly face challenges regarding data including: Limited knowledge of how to handle large datasets Lack of a common language to discuss data challenge Limited understanding of how to use data and analytics to inform and facilitate decision-making We are seeing a turn in the market to use data and analytics to cultivate an insights-driven culture. But we are noticing a disconnect between the actuaries that are able to glean these insights and the new technologies required to handle the amount of data available to them. During this session you will look at moving from MS Excel to more advanced data handling and modeling tools (e.g., Alteryx), visualization tools (e.g., Power BI & Tableau) and expanding utilization of these tools and analytics. By attending this session, you will: • Understand data and analytics (D&A) concepts, methods, and fundamentals to help drive value. • Gain an appreciation for data privacy, protection protocols, and related ethical considerations when working with data. • Be able to identify of how to use data and analytics to inform and facilitate decision-making.