Description: The actuarial profession is changing rapidly and the skills needed to be successful are also changing. In this session, we will debate key topics and questions, such as: What is the predominant future career path for actuaries? Specialists or generalists? Does the actuarial profession have a meaningful value prop outside our core domains e.g. insurance? What skills are needed for the actuary of the future? What’s the best approach to improve actuarial storytelling to non-actuarial audiences? What’s the role of actuaries to managing and predicting emerging trends and assumptions? What is the role of actuaries in relation to development and ownership of advanced modeling? Is the future actuarial profession smaller/larger than it is today? In either case, how does it differ from today in terms of make-up, deployment, function, purpose, value add, etc.?

Description: Creating a predictive model is one thing, but then taking that model and getting full stakeholder buy-in is another. Learn from presenters' experiences and insights on proving model effectiveness in the long-term care (LTC), life and annuity spaces. Explore various topics such as feasibility, model transparency, fairness and equity, ROI measurement, data assets and compliance, infrastructure and more. By the end of the session, you will gain an understanding of how to navigate the challenges of proving the effectiveness of predictive modeling and obtaining management buy-in.

Description: Session 9C: Adventures in AI - Group Insurance Perspectives

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 session will discuss essential theories and practical applications of deep learning for actuaries. Participants will gain insights on leveraging these cutting-edge techniques to enhance actuarial models and drive predictive accuracy.

Description: As insurers search for ways to optimize the value of their actuaries, cloud-based data architecture has emerged as a leading solution to deliver quality data that enables the unlocking of actuarial potential. Hear an introduction to cloud based data architecture, explore cloud-based data strategies and view a live demo. Learn about different data “zones”, piping data between the zones, transforming data, and a collection of cloud-based tools that could be used for each one of these steps.

Description: Virtual - Session 3A: But We have ChatGPT at home... Or Operationalizing Generative AI

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: En el seminario hablaremos sobre los nuevos flujos de tarificación, y nos enfocaremos en la inclusión del análisis geográfico en el proceso de modelado para optimizar la extracción del riesgo en modelos de Machine Learning.

Description: Session 2F: Artificial Intelligence and Actuarial Modeling - Ask an Expert!