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: As long-term care enters into its fifth decade, the product continues to evolve. Current trends as well as innovative product designs pose many challenges as carriers seeks to model long-term care.

Description: Artificial intelligence (AI) at the heart of new digital tools allows actuaries to extract structured data useful for creating more precise risk assessment models. This technology sits best alongside the expertise of insurance professionals who have years of experience in the industry. RGA’s partnership with DigitalOwl is an example of how AI combines with human knowledge for the best outcomes.

Description: There are many ways to actively utilize BE approaches – and implementing these important tools can help insurers in many ways.

Description: With coinsurance of an existing block of long-duration insurance contracts, there may be existing reinsurance that remains in force. The new coinsurance is typically net of such “inuring reinsurance.” In this article, we look at approaches that the new reinsurer might apply to account for the effects of inuring reinsurance.

Description: This article will highlight the issue that the sum of key-rate metrics does not add to metrics derived by parallel shift to the entire curve, even at very low shift size. Additionally, in the article I will outline how to define a single rate change to be applied to a key rate (or the entire curve) since the rate changes are generally not level or parallel.

Description: James Dunseth's MBA journey at Yale’s School of Management and his startup internship taught him invaluable entrepreneurial lessons. Key takeaways include the importance of customer feedback, flexibility, technical skills, effective metrics, and achieving product-market fit. These insights emphasize customer-centric approaches, agile methodologies, technical understanding, and strategic metric use to guide business decisions and foster innovation.

Description: This paper uses the ideal quasi-natural experiment of government-subsidized agricultural insurance program phased out in different regions of China since 2007 to study the effects and transmission mechanisms of the program on alleviating regional poverty using a multi-period Difference-in-Differences (DID) approach. Specifically, this paper identifies the causal effects on the incidence, depth and intensity of poverty among rural residents. The research has important policy implications. First, this paper uses the program as a case to provide a research paradigm to estimate effects of implementing policy instruments that combine markets and government mandates. Second, as common prosperity is now a government imperative, the research also provides insights into ways insurance can be used to hedge income risks, alleviate poverty and help achieve more equitable distribution. The paper has been published on WeChat account of Chinese Association of Actuaries on February 9, 2022. The full version has been published in CSSCI journal Insurance Research, issue 11, 2021.

Description: LTCi rate increases are large and becoming larger. As more and more policyholders cannot afford the cumulative impact, they need some relief - like access to the large contract reserve they have funded for the benefits they can no longer afford.

Description: Regression to the mean is a phenomenon that arises when comparing sequential data points. It states that when an initial observation is extreme or an outlier, future observations will be closer to the average. Health care actuaries need to understand and be able to mitigate the effects of regression to the mean to avoid misapplying predictive models or misstating measurements of historical effects.