Description: This report summarizes the results of a Delphi study to identify and debate possible new approaches or Blue Ocean Strategies to acquiring business by life insurers.

Description: Panelelists at this session at the SOA 1998 Maui I Spring Meeting present their thoughts on how life and disability risk assessment and underwriting might be performed in the 21st century. Focus is on how technology can change and speed up the process.

Description: Big data and predictive models have disrupted the life insurance new business and underwriting processes over the past few years. How did we get here? What does the future hold? This session will review the lessons learned from the evolutionary process and discuss what the potential next steps are in this journey. Are real time fully underwritten decisions an achievable goal?

Description: This is a discussion from the June 2000 meeting of the Society of Actuaries in San Diego, looking at the evolution of e-commerce for the insurance business.

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

Description: This is an interview with Stephen F. Siegel, Director of Knowledge Engineering at Applied Intelligence Systems, Inc. Mr. Siegel defines what an expert system is, how they are built and their applicability in the life insurance industry.

Description: Discussion of the selling of life insurance to the middle income market through banks and with the support of new technologies and underwriting processes.

Description: The article provides several examples of projects led by actuaries who are applying advanced data analytics and big data to solve industry-specific business problems.

Description: PartnerRe Life and Health is rapidly growing its presence in global markets. This growth means that our medical underwriting team has to handle 25 percent more cases every year. To manage these additional cases without increasing headcount at the same pace, the team has to operate more and more efficiently. In a market where response time is a key differentiator, we improve efficiency by using machine learning to triage and prioritize the underwriting team's case work. In this talk, we describe the model pipeline end to end. That is, we cover the entire implementation chain from accessing data to the eventual evaluation of the added value. In particular, we discuss often neglected topics such as model risk mitigation, technical deployment of the model and integration into business processes.

Description: These are the appendices to a report summarizing the results of a Delphi study to identify and debate possible new approaches or Blue Ocean Strategies to acquiring business by life insurers.