Description: How the speed of change is accelerating and how we could react to this as a profession.

Description: Presented at June 1986 Spring Meeting. This session discussion is about the National Association Of Insurance Commissioners' Early Warning System, A.M. Best Company ratios, Conning and Company solvency ratios, and California Department of Insurance Empirical ratios.

Description: The barrier between humans and machines continues to blur, but curiously, this may have been prediction thousands of years ago in ancient myths. This article looks to how some of the early legends are becoming manifest in modern technology, particularly in the area of elder care. From automation, to humanoid assistants, to mind clones will the age of technology bring a new utopia?

Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

Description: The proposed session will introduce Outcome Based Risk Sharing Arrangements (“OBRSA”) between health insurance payers (“payers”, including insurance companies, health plans, sponsored employers, and government programs) and pharmaceutical manufacturers (“pharma”), and it’s intended to meet the needs of actuaries who focus on pharmacy benefits and risk sharing arrangements. The covered content will include (1) the background of OBRSAs, definitions, stakeholders, and OBRSA’s role in shifting the system from volume-based toward value-based; (2) conflicting perspectives about OBRSAs between payers and pharma (cultural and methodological); (3) identification of incentive gaps between payers and pharma, and incentive alignment between them; (4) barriers to implement OBRSAs; (5) limitations and considerations of OBRSA implementation; and (5) hypothetic OBRSA examples.

Description: This chapter introduces a few probability distributions that play an important role in understanding and analyzing healthcare data. While theoretical in nature, these distributions model real life if appropriately chosen. Probability distributions are mathematical representations of how statistical data behave.

Description: You really can have it all: readily available and relevant information derived from consistent and understood data. Getting at the Truth requires a good datawarehouse design and an appropriate business data model. Add a powerful and flexible Web–based analytical interface to get at the data and success in the pursuit of Truth will happen.

Description: Digital is the main reason over half of the companies on the Fortune 500 have disappeared since 2000." (CEO Accenture). We cannot be sure how global technology megatrends will disrupt the life insurance industry, but we can be certain that the future will not look like today. As large quantities of available data are no longer owned by insurance companies and statistical techniques become more accessible to non-statisticians, the pressure to keep up with technological advancement increases. Life actuaries must think critically about and be prepared for the coming changes and position themselves as leaders in an industry lagging behind in new technology adoption.

Description: Big data, predictive analytics, machine learning and AI will likely transform the insurance market and the actuarial profession. The actuarial profession is seeing changes many markets, including health and property and casualty. This article describes these trends and provides an introduction into what companies and the Society of Actuaries (SOA) are doing to adapt and capitalize on these trends.