Description: Innovation is known to be challenging. It creates discomfort for others used to the current way of doing business, and there is always the risk of failure. Innovation is a type of change and change is difficult for companies to manage. Reacting to pandemics or new regulations requires a lot of effort, but the need is clear and urgent. How do you muster the effort to tackle an optional change that holds the promise of future benefits and current sacrifice?

Description: The COVID-19 pandemic helped accelerate many changes in the Health Care system that were already taking place. The major disruptions to the way providers operated was quite evident with shifting of focus away from elective services to create capacity for treating COVID-19. However, even before the pandemic, providers were adapting to changes in their environments.

Description: The landscape of climate-related and sustainability-related disclosures has been changing and evolving. The approaches that companies will use under the new ISSB standards (IFRS - IFRS S1, IFRS - IFRS S2) remains to be seen. To help prepare for these standards taking effect, the SOA Research Institute Catastrophe & Climate Strategic Research Program Steering Committee is seeking researchers to conduct a literature search to lay out methodologies and approaches being considered to implement these new standards, along with common terminology and common language determination in the approaches.

Description: The Society of Actuaries Long Term Care Insurance and Health Sections are seeking researchers to author a primer on long-term care insurance wellness programs. The underlying motivation for this effort is to educate actuaries on these programs and provide a useful resource for understanding their benefits, structure and limitations.

Description: The SOA Research Institute Health Care Cost Trends Strategic Research Program and Health Section Council Committees are seeking researchers to compare the intended effects of the ACA and its amendments since the implementation of each provision.

Description: All you wanted to know about InsurTech but were too afraid to ask! This one-day event will feature four webcasts that will unlock and enhance the world of InsurTech. By attending this webs series, you will find answers on what InsurTech is about and what it’s trying to solve. Dive deeper into the successes of InsurTech and find out how to join this established organization. Session 1 – Introduction to InsurTech Set the stage for the day, defining the main types of InsurTech companies and where these companies fit in the insurance value chain. Come discover where actuaries could play a role and the importance of the profession within this space. Technology driven innovation is multifaceted and could take place within an established carrier or a start-up.

Description: The old adage 'insurance is sold not bought' has been used to dismiss digital insurance and support the old agent model of distribution. However, modern distribution systems address this with personalization. This is not simply a customized application form based on age or gender. Personalization is much more dynamic and happens in real-time. It's about tailoring the end-to-end experience to meet the needs and expectations of the specific applicant. The core tools to achieve this are AI learning and nudges from behavioral science.

Description: Enterprise Risk Management (ERM) provides practitioners all of the tools necessary for assessing, quantifying, and managing risk on an aggregated, holistic basis. However, in practice it is often found that there is a tendency for company managements and even seasoned risk practitioners at times to navigate their risk exposures one at a time. This habitual approach produces sub-optimal results due to the oversimplification of risk events and understating the complexity of the world in which decision makers must navigate. At the heart of this sub-optimal behavior is the human predilection towards mental accounting. As with financial accounting, mental accounting employs a ledger function that mentally registers debts and balances. However, unlike a traditional ledger, the mental ledger is not precise, does not always sum debits to equal credits, and creates arbitrary categories for resources that are treated as independent, unrelated pools. These differences in function profoundly impact financial judgments and decision-making. Learn more about the long history of mental accounting, including examples from daily life to illustrate the principles involved. Discuss how mental accounting can arise during the ERM process cycle, with a special emphasis on its impacts on effectively quantifying risk and how this results in sub-optimal decision making. Discover behavioral tools specifically designed to mitigate the risk of mental accounting creeping into a company's ERM activities.

Description: Models are meant to represent reality, and they often do so quite well. However, they cannot make decisions that contradict the modeling algorithm or underlying assumptions and data. Neither do they possess emotional intelligence or the ability to adjust for social equality. As complexity of models evolved to predictive analytics and machine learning, reliance on model results has increased as well. Over the last decade, as actuaries, modelers and data scientists looked closer into their models, they often identified certain social biases in model assumptions and algorithms. From the use of indicators like credit scores to geo-social segmentation, insurance models have often skewed results in favor of certain socio-economic groups. Underlying this segmentation, one can identify bias around access to health care and education, socioeconomic groupings and financial literacy.

Description: Predictive analytics projects in the life insurance industry are oftentimes set up as research ideas leading to intangible or unusable results for the practicing actuary. What if you could change that? What if the research turned into development and practical use through leveraging valuable resources properly? Learn how to structure predictive analytics projects, and about the project team, so there will be actionable takeaways for the business unit. Specific areas of value for you include underwriting, experience studies, assumption-setting, and product development. Watch as presenters talk through real case studies on turning a predictive analytics project into workable solutions rather than a resource drain, an unclear goal, or a misunderstood business need, which are some of the pitfalls of a poorly planned project.