Description: This article will highlight new model visualization approaches we use to shed light on the “black-box” nature of advanced modeling techniques. These visualizations help us better highlight and understand the key drivers of predictions so that they can be understood and validated by all stakeholders

Description: this article addresses the need for rate increases in LTC insurance and some common misconceptions.

Description: The article looks at the impact of voluntarily offering nonforfeiture benefits at the time of a rate increase on ALR and future profits.

Description: LTC insurers are beginning to implement population health programs to improve the lives of their policyholders before and during a claim. Measuring the financial impact of these programs can be difficult for a variety of reasons and multiple methodologies are used to evaluate treatments or programs. This article proposes two different methods of randomized control trials to evaluate the impact of these programs through a case study approach, along with additional potential considerations when evaluating effectiveness.

Description: LTC insurers can proactively identify policyholders more likely to go on claim in the near future, and methods to potentially delay or prevent claims, benefiting company and customer.

Description: This article provides a summary of the 2016 Milliman LTC rate increase survey and additional commentary from our years of experience with rate increase filings.

Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: First article in a series that walks through the progression from developing LTC assumptions using traditional methods to doing so using predictive analytics.

Description: Third article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: Today, a plethora of tools and approaches exist to develop lifetime projections of long-term care (LTC) business. Within these tools lie two distinct approaches to project mortality; namely, by using assumptions that are applied: (1) to an all-lives exposure base, or (2) separately for disabled- versus active-lives exposure bases. Using disabled versus active mortality allows for more granular modeling of the two different cohorts that exhibit dramatically different mortality. And so, when using an all-lives mortality assumption, is the projection missing important details about the appropriate mortality level? This article seeks to not only answer this question, but also answer how a “traditional” all-lives mortality assumption can be impacted when you introduce a combination of the two modeling techniques into the development of a mortality assumption. To answer these questions, we examined the experience and results developed from one c ompany as an illustrative case study.