Description: In the wake of the pandemic, much of the insurance industry is now endeavoring to find sustainable, longer-term solutions among many forced innovations – and to ensure that temporary adaptations add value beyond mere change.

Description: Defines Accelerated Underwriting programs as a transformational trend, and outlines a process for evaluating mortality implications of such programs.

Description: Key findings from Milliman's 2019/2020 UL/IUL survey. Provides benchmarking for UL/IUL competitors relative to trends in the industry.

Description: This article discusses gaining a competitive advantage by reducing new-business processing costs using point-of-sale technology.

Description: This article summarizes a session at a recent SOA meeting that featured numerous short presentations on underwriting ideas. The focus was on areas such as, process improvement, improved underwriting information, middle market penetration, and organizational issues.

Description: Discusses what is known about HIV and the prospect of wider availability of life insurance coverage for infected people in Kenya.

Description: This article explains how the use of technology and data can help a company be successful in the simplified issue life insurance market. Prescription drug profiles are a valuable new source of data for this purpose.

Description: This article summarizes the findings of the 2020/2021 Milliman UL/IUL/VUL survey. The survey covers a range of specific product and actuarial issues and the results provide a good tool for benchmarking

Description: The current coronavirus pandemic has caused a number of challenges for life insurers and this article summarizes the operational challenges, namely in two areas. First, the article will discuss the challenges companies are facing to collect traditional underwriting evidence and potential short-term solutions used to overcome these limitations. Second, the actions companies are taking to alleviate the financial difficulties customers may have paying premiums will be addressed.

Description: This article shows how to use a RBF Neural network to predict if a breast tumor is malignant. The network is trained using a genetic algorithm that uses C# multi-threading.