Description: This article was written to increase the knowledge and insights of both the direct writer and reinsurer in optimizing the reinsurance negotiation process. It should help the reinsurer offer a more competitive quote and hence win a share in a reinsurance pool while simultaneously helping the direct writer obtain lower reinsurance costs helping their product pricing and bottom line.

Description: Summary of pending legislation in response to looming crisis in the Long Term Vare market.

Description: While there are many approaches currently being taken to transform health care, to be successful, we know that leading and planning for quality improvement is a science. Data is the common denominator as healthcare organizations establish best-practices, and ultimately overcome unintended variation through strong analytic systems. Health care leaders require specialized data analysis education and training to hone their skills in understanding and managing clinical variation. The problem with unexplained variation in healthcare is that it negatively impacts clinical outcomes, increases the cost of care, and diminishes value for patients. In this session, Dr. Ted James (BIDMC and Harvard Medical School Faculty) will explain how actuaries can offer practical solutions that provide insights into variations in clinical practice that cannot be explained by differences in medical conditions or patient preferences.

Description: CMS provides a significant, and growing amount of information that can be leveraged by actuaries. Between the public use and research files CMS makes publicly available large amounts of information that can be assimilated to begin to understand the who, what, when, where, and how of Medicare. This session will focus on the depth and breadth of data available, summarize any restrictions on the use of the data, and identify areas where access to the new data can provide more meaningful insights than CMS's historical data sets.

Description: 1) Vision of the applications and development of data analytics in life insurance industry in Asia; 2) How big is the pie and what are the current obstacles insurers are facing; 3) Utilization of data and analytics target operating models as a competitive advantage, with focus on future readiness, controls, efficiency and effectiveness; 4) Potential roadmap ahead: a) Assessment of the current state of maturity levels of data and analytics initiatives; b) Identification of technology and competency gaps; c) Establishment of data integration and management process within BUs, alignment of advanced analytics tools across departments, with centralized data governance, technology structure and decision making framework; d) Definition of criteria and metrics to measure key outcomes and business value added. 5) Case study on agency behavior management: a practical example on how data analytics is being used in practice: (a) key business objectives: improve agent behaviors and maximize productivity with the lowest possible compensation costs; (b) data collection and current state assessment; (c) detailed predictive modeling process, modeling outcomes, and business implications; (d) implementation and monitoring process.

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: 1) Vision of the applications and development of data analytics in life insurance industry in Asia; 2) How big is the pie and what are the current obstacles insurers are facing; 3) Utilization of data and analytics target operating models as a competitive advantage, with focus on future readiness, controls, efficiency and effectiveness; 4) Potential roadmap ahead: a) Assessment of the current state of maturity levels of data and analytics initiatives; b) Identification of technology and competency gaps; c) Establishment of data integration and management process within BUs, alignment of advanced analytics tools across departments, with centralized data governance, technology structure and decision making framework; d) Definition of criteria and metrics to measure key outcomes and business value added. 5) Case study on agency behavior management: a practical example on how data analytics is being used in practice: (a) key business objectives: improve agent behaviors and maximize productivity with the lowest possible compensation costs; (b) data collection and current state assessment; (c) detailed predictive modeling process, modeling outcomes, and business implications; (d) implementation and monitoring process.

Description: While it is now almost universally accepted that data can bring value to the insurance industry, there is little guidance on exactly how to make that value tangible. This session goes beyond advances in computing and storage technologies and is aimed directly at business leaders. This session will define what a data strategy is, discuss the main components, describe how to get started creating one, and will outline how to translate a strategy into execution.

Description: There is a lot of focus on GTI and its impact on modeling, methodology and transition. However, given the dependencies that exist on the data infrastructure, the implementation of the guidelines is an ideal opportunity to also streamline and improve data storage, data transformation, and reporting processes to create an efficient, effective and scalable data solution. This session will focus on the specific impacts and possible solutions to the following data processes: In force data Experience studies and assumption setting post model data feeds and subledger processes Data warehouse and reporting systems

Description: The speaker of this session will discuss some common difficulties one encounters when dealing with exotic equity linked derivatives. In particular, a study of a Monthly Sum Cap (MSC) will be presented. Pricing issues, along with dynamic hedging, as well as projections of MSC into the future will also be discussed. Time permitting, some aspects of historical performance will also be addressed