Description: This session is a primer on how to apply Gestalt psychology to display quantitative information to improve neural receptiveness. While much of brain functioning is still a mystery to psychologists and neuroscientists, we do understand a great deal about which parts of the brain engage or disengage given visual cues. Actuaries are often tasked with presenting very technical information and are prone to focus more on the substance of numerical data rather than a pictorial presentation based on neural preferences. This presentation will discuss how to display quantitative information more effectively tailored to these preferences.

Description: This presentation explores the applicability of multiple population versions of the Lee-Carter model in the modelling of mortality by cause of death and socio-economic stratification.

Description: The presenters will discuss predictive analytics as applied to recent group life and disability experience studies and other applications.

Description: The optional phase-in period for life principle-based reserves (PBR) has come to a close, and companies have put in the work to bring their PBR implementations across the finish line. With the mandatory PBR implementation deadline behind them, companies can turn their attention from compliance to model and process improvements. The presenters will outline lessons learned from PBR implementations and their applications to broader use cases, as well as resources that are available for PBR modelers. Discussion topics will include items such as: Model simplifications and validation Model run-time techniques Model scalability (e.g. adding new products / future assumption updates) Sensitivity testing Model projections Available PBR & other modeling resources (e.g. PBR practice note, ASOP 56)

Description: In the first part of this series, the presenters will provide the results of a recent SOA multi-country survey on mortality improvement practices of life insurance companies. In the second part of this session, they will discuss the issues and solutions related to the development of the most recent RPEC mortality improvement assumptions.

Description: Spreadsheet of sample calculations for extreme value theory. Extreme value analysis seeks to assess, from a given ordered sample of a given random variable, the probability of events that are more extreme than any observed prior.

Description: The discount rate is one of the most critical topics in IFRS 17 and may have important repercussions on product profitability. The presenters will cover practical implementation considerations, including alternative approaches to illiquidity premium calculation for business supported by fixed assets with credit risks. Other implementation considerations will also be covered, including granularity of setting the discount rate, application to new business, and implications on asset and liability modeling. The presenters will then discuss how discount rates could be utilized in an Economic Scenario Generator (ESG) to assist IFRS17 calculations, particularly for risk-neutral valuation of the time value of options and guarantees.

Description: The U.S. health care industry has gone through a dramatic change during the past several years. The business model has moved from a risk selection model to a risk management model. People can shop online for their health care coverage. Everyone is welcome! Annual and lifetime dollar limits, underwriting and the pre-existing conditions exclusion are all in the history book now. Facing the challenges of the post-reform era, many health insurance carriers began using big data analytics on all aspects of their decision-making processes. The goal is to be able to compete on analytics in this quickly changing environment. However, becoming an analytics competitor is not easy. It requires the belief from the leadership and a group of dedicated analytical professionals to solve emergent business problems that existing strategies cannot address in an evolving market. These problems include marketing for acquisition and retention, coding accuracy, revenue growth, health care disparity, payment integrity, quality of care, health care cost reduction and, ultimately, personalized care management. In this section, audience will learn a basic understanding of the role of predictive analytics in US Healthcare marketplace.

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.