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: Learn about business Analytics in Insurance - Practical case studies and implementation.

Description: Accelerated underwriting is changing the world of consumer engagement. The newness and overhaul of these programs creates challenges for development, coordination and management. This seminar will take attendees through how the market is developing, the major pricing, underwriting, and marketing considerations, and what regulators are considering with regard to these programs. At the conclusion of this seminar, attendees will: • Discuss trends in accelerated underwriting • Understand the issues that arise when developing an accelerated underwriting program • Understand best practices in developing an accelerated underwriting program • Explain the impact of accelerated underwriting on underwriting, pricing, marketing and compliance

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: 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.