Description: Driver-based forecasting is a powerful tool to predict and estimate future outcomes. In recent years, driver-based forecasting has gained significant attention as a technique for actuaries to forecast the financial performance of insurance companies. Often there can be frustration around the ability to use forecasts to manage the business. However the objective of a driver-based forecast is to break down historic and projected movement based on the key drivers which impact the results. The key is to understand what the key drivers are on forecasts and what is the relative level of impact and influence we have on the drivers. Listen in on a discussion on the core principles of driver-based forecasting, including how to identify key drivers, collect accurate data, and leverage the latest technology to generate forecasts that traditional models can't match. Explore with us the various driver-based models and how actuaries can leverage them to enhance their decision-making capabilities. Then join the conversation about the challenges actuaries face when implementing this technique and how to mitigate these problems.

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: Pandemic modeling and its impact on the insurance and reinsurance business have been the subject of scrutiny during the COVID-19 pandemic. As the World Health Organization (WHO) has declared its end as a global health emergency, the focus of insurers and reinsurers turns to the possibility of future pandemics. Key to considering possible future pandemics is assessing what is needed to model pandemic risks. Our presentation will focus on providing you with the following: - The main principles around creating a well-established pandemic risk modeling framework, what they are and how you should build them into your model (As a reinsurer, this is core to our business, and we have unique global insights into performing it.) - A review of pandemic risk modeling strategies as well as an understanding of the inherent uncertainties of pandemic events - Lessons learned from COVID-19 and how we can apply them in assessing the possibility of future pandemics - An understanding of the link between future pandemic risk and the capital requirements

Description: The COVID-19 pandemic has been an acute reminder of the material impacts that pandemics can have on Life (re)insurance and society at large. Learn to describe the journey of a Life reinsurance practitioner to model and operationalize pandemic and infectious disease risk, why this is of importance to actuaries, and their risk management. It will consider how such models have been used, developments over time, and challenges in using and calibrating these models. The session will also provide perspectives from a pandemic model provider who will describe the fundamental metrics for pandemic loss distributions and how learnings from the COVID-19 pandemic can be used to quantify the potential impact of the next pandemic. Finally, dive into an outline of how biological principles can be applied to anticipating plausible pathogens causing the next pandemic.

Description: Recent and potential near future extreme experiences are leading us to a new normal rather than back to the prior environment. Learn more about several examples of past Regime Changes along with a detailed discussion of how recent disruptions lead toward a persistent change in the business, social and political environment. Come develop an understanding of these drivers of change as well as the likely impacts on the Financial Services industry.

Description: Health care has changed rapidly over the past couple of years. The pandemic has shifted consumer behavior in how and where they receive health care as well as consumer attitudes toward health care. Payers continue to design innovative solutions in responses to their patient’s needs and expectations. These changes in consumer behaviors and the responses by payers are expected to impact both health care cost and utilization. Discuss changes in health care that have emerged due to the pandemic and how they are expected to influence actuarial assumptions. Explore epidemiological models commonly used in the industry, the considerations when using these models, as well as the challenges with regards to gathering and interpreting relevant experience data. By attending this session, you will be able to: Identify shifts in consumer behavior that have emerged from the pandemic and describe how payers are responding to new patient expectations. Discuss how these post-pandemic changes by consumers and payers influence medical spending. Determine which actuarial assumptions are impacted and how and if the impact is expected to be temporary or permanent. Discuss considerations taken by actuaries and challenges faced when modelling actuarial assumptions post pandemic.

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: Insurers have long relied on scenario analysis to understand the long-term impacts of potential business decisions, but the advent of rapidly changing market environments and a growing desire to optimize capital has spurred business leaders to leverage new tools in order to explore potential options. Companies are beginning to explore new analytical solutions that trade-off accuracy for speed for a more robust approach to impact quantification. In particular, they are looking at the use of additional what-if scenario models, built from the top down, to support decision-making processes. Actuaries can help take charge in the development of these top-down analyses by leveraging data and actuarial techniques to supplement their existing models. In this way, business leaders can simultaneously rely on the actuarial rigor of typical standalone cash flow projection models, while also receiving support for real-time business decision-making. The potential of what-if scenario modeling can help actuaries unlock a powerful new approach to scenario analysis to enable more rapid decision-making. Learning objectives: In this session we will discuss the fundamental concepts of what-if scenario modeling in an actuarial context, including: - Core principles and components of what-if scenario modeling - Common challenges and considerations around the implementation of what-if scenario modeling - Potential solutions to challenges and their implications

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: 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