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: 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: The Society of Actuaries are once again offering a seminar that investigates all aspects of life insurance, annuity and long term care product tax compliance. Developed by the Taxation Section, participants will be brought up-to-date on recent Internal Revenue Service (IRS) guidance and learn about the newest tax issues related to product design and administration. Legal, IRS and product development experts will provide vital insights into the challenges of developing and administering products within the requirements of the Internal Revenue Code (IRC), and allow ample opportunities for networking, questions and information exchange.

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: "Visual storytelling is frequently used to effectively convey complex actuarial concepts to our clients and other audiences. When talking about data visualization, we often focus on the explanatory aspect. But data visualization is also a crucial tool in developing insights from large and complex data sets at every stage of data analytics spectrum: • descriptive (what happened?), • diagnostic (why it happened?), • predictive (what will happen?), • and prescriptive (how do I influence what will happen?). In exploratory and modeling stages of data analytics’ project, we let the data tell the story or explore our intuition and hypothesis through data pictures. We demonstrate the power of visual display in healthcare data analytics through vivid examples from the healthcare case studies. These illustrations of data visualization exploratory power range from assessing the quality of data or examining outliers, to exploring variables’ relationships and trends, to hypothesis testing, to guiding in feature selection and models’ comparison and evaluation, to model validation. Throughout these illustrations, we will discuss principles of graphical effectiveness, excellence and integrity."

Description: While medical and pharmacy claims data is a well-established source for healthcare analytics, significant data can be found elsewhere in electronic medical records (EMRs) and in hospital admit, discharge, and transfer (ADT) messages. Increasing availability of bulk EMR data to analyze coupled with more powerful and specialized tools for analyzing it presents an opportunity for actuaries to incorporate these sources in their work. This session will take a look at some of the data available beyond claims in EMRs and HL7 ADT messages, and will discuss case studies, with consideration around structure, ease of use, cleanliness, and tools available for analyzing these data types including natural language processing.

Description: Critical Illness Insurance is one of the fastest growing insurance products available today. Carriers continue to introduce innovative approaches in second, third, and fourth generation products while refining their pricing, underwriting, and claims adjudication processes. This session will look back at historical trends using Gen Re's longstanding, comprehensive Critical Illness Market Survey, and look forward to potential market changes to occur in the next 18 months.

Description: Legislative changes and the economic environment have created a need for plan sponsors to understand and manage pension risk and volatility. Dynamic asset allocation glidepaths reduce risk as plans become better funded. But what about the risk that plan sponsors face when their plans are underfunded? The presenters will discuss the challenges of managing pension risk when assets don't keep pace with liabilities.

Description: The Individual Disability Experience Committee (IDEC) of the SOA has gathered industry data covering 2006-2015. The presenters will highlight findings on the recent industry experience compared to the 2013 Individual Disability Income Valuation Table (2013 IDIVT), including the impact of the recession on incidence and terminations. They will cover claim incidence and claim termination Actual to Expected (A/E) using 2013 IDIVT as the expected basis. The adequacy of the modifiers will also be discussed.

Description: The presenters will cover the variation in risk adjustment approaches used across state Medicaid programs and compare them to other Federal programs. Additionally, recent developments, such as the use of OCR to extract data from medical charts for risk adjustment, will be discussed.