Description: Mergers and Acquisitions (M&A) are reshaping the insurance industry landscape. Some M&A transactions even garner wide media coverage. The presenters discuss current trends in recent M&A transactions and explore considerations for buyers and sellers leading up to the transactions.

Description: Numerous behavioral finance theories have emerged in recent years, many of which apply to insurance. The presenters look at key theories and their use in explaining, predicting and inducing behavior from the perspective of the health insurance customer journey.

Description: This presentation shares experience and ideas of both AIA Group and AIA China during implementation of the IFRS17 project and will discuss potential impact and outlooks in the domestic market from IFRS 17. This presentation includes a general overview of an IFRS17 implementation project, focusing on changes and requirements, as well as the resources cost of the main challenges and relative resources.

Description: The speaker will discuss the training of neural networks to compute future Greeks of liabilities (or options) without the use of nested stochastics.

Description: The speakers will discuss the replication of long-term options, including a discussion of issues such as liquidity, transaction costs, slippage, impact of policyholder behavior, and machine learning algorithms to improve hedge effectiveness.

Description: Representing a large indexed annuity writer, the speakers will discuss the growth and evolution of the market for index annuity products. Moreover, the challenges that growth has introduced and how those challenges are being met. While much of the market consists of simple, accumulation-oriented S&P 500 based strategies with 0% return floors, a significant portion of industry sales come from more complex products with highly customized index strategies and other features, such as living benefit guarantees. Attendees will gain a broad understanding of the indexed annuity market, product and features, operational difficulties in risk management arising from product design, and how actuarial and hedge professionals are managing these issues.

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.