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: The insurance industry is in a constant state of transformation. As organizations adapt to the effects of COVID-19, undergo modernization efforts and implement new technologies, soft or “human” skills are becoming key differentiators for success. Skills such as communication, empathy, storytelling, leadership, problem-solving and more are separating the good from the great actuaries. Those who possess these skillsets can better translate data and most efficiently leverage systems and teams. At the same time, by taking a human approach to modernization efforts, leaders will be able to best engage and motivate their workforces, while building teams that will excel in the future of work.

Description: Machine learning models have many practical applications in the world of insurance. These applications are well documented and most actuaries, at least at a high level, are familiar with these concepts. The presenters will take this one step further and provide an introduction to the theoretical formulas and real-world considerations that make machine learning models possible.