Description: This article explores the disruptive applications of blockchain, an emerging technology, and its impact on the pharmacy benefit space, and pharmacy benefit managers (PBM).

Description: This article summarizes five key lessons we have learned in implementing LDTI and shares our insights on practical ways to address them. This is not an exhaustive list of implementation challenges, but companies that can successfully tackle the issues we describe should have a smoother data and systems implementation transition.

Description: Blockchain technology has the potential to significantly improve the quality of life and overall health of the members of a health system, while also adding to administrative efficiencies. For the first time, we will have access to members’ entire diagnoses and prescription history, consumer behavioral attributes, and other socio-economic determinants which that are valuable inputs to designing appropriate benefits and proper care management plans. Through the blockchain, we will be able to access data not just of the existing members of the health system but also of potential enrollees and new entrants. The data can be accessed by querying the blockchain through a permissioned granted by the member. This information can be very useful in performing feasibility studies for evaluating entry into newer markets and/or newer products, developing customized care plans, and designing efficient value-based contracts. By executing smart contracts on the blockchain, we will be able to achieve administrative efficiencies like never before. The applications of blockchain technology are numerous and ground-breaking. Organizations that do not pay heed to the benefits that blockchain can bring may find themselves left behind as the rest of the industry marches forward in its quest for efficiency and member satisfaction.

Description: We explore four plausible scenarios for how AI might reshape the future, focusing on employment as the main driver. At one extreme, AI boosts productivity, sparks entrepreneurship, and leads to near-full employment through human-AI collaboration and reformed education. At the other end of the spectrum AI-induced mass layoffs trigger an economic spiral, with soaring unemployment, creative stagnation, and societal resentment. The middle path presents a fragmented transition with a two-tier workforce, periodic recessions, and variable adaptation success. Lastly AI fizzles due to poor returns and rising costs, causing economic stagnation and investor losses without widespread disruption. Each scenario also includes potential impacts on the insurance industry, ranging from innovation and growth to regulatory strain and investment losses in the others. Proactive scenario planning, by preparing for multiple futures by identifying early warning signals, adapting plans, and strengthening institutional resilience is encouraged. The article closes with four additional speculative scenarios, including government-controlled AI, AI dominance, militarized AI, and mass surveillance. Ultimately, the piece argues that while AI’s future is uncertain, the choices made today will shape whether it becomes a partner, a threat, or a disappointment.

Description: This article explores the evolving role of assumption management in the life insurance industry. The article highlights how behavioral, economic, and demographic assumptions are critical to pricing, forecasting, and explaining financial results, yet many insurers still struggle with fragmented practices and outdated processes. Drawing on survey insights from 15 U.S. insurers, the authors illustrate persistent challenges in governance, data integration, and automation. They argue for a more holistic, technology-enabled operating model centered on centralized assumption repositories. Such repositories provide a single source of truth, enable automated ingestion into models, support sensitivity testing, and enhance governance and transparency across actuarial, finance, and risk functions. The article offers guiding principles and design considerations for building scalable repositories that improve efficiency, consistency, and decision-making. Ultimately, it positions assumption management modernization not as a technical upgrade but as a strategic imperative to unlock clarity, agility, and competitive advantage in a complex market.

Description: LDTI’s introduction has forced many insurers to examine their existing operations and technologies. They must determine how to optimize compliance efforts while balancing data, analytics and system modernization considerations to achieve maximum value. This article will examine the four key facets—the data, technology, processes, and people—essential to any successful LDTI exercise. It will also touch on the various approaches for tackling LDTI and how a modernization mindset can help insurers realize much greater transformation for the business.

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 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: This article is a brief overview of what digital health is, how it is regulated, and who some of the big unicorns are.

Description: Innovative solutions are necessary for insurers to: • Meet customer needs, • Better collect and process data, • Improve risk assessment and underwriting .