Description: Most actuaries deal with model development at some point in their career. However, very few of us have proper training in programming, software engineering or software shop operations. Explore topics, techniques, and practices from the FinTech and IT industries that can be applied to building robust, high-quality actuarial models. This session will be of particular interest to actuaries engaged in 'in-house' model development, managers of modeling teams, and actuaries interested in software development.

Description: As the portion of inforce business valued under PBR grows, it becomes increasingly important to not only calculate reserves for point-in-time valuation but to also project reserves. This is especially critical for actuarial functions such as pricing, business strategy/forecasting, and asset adequacy testing. In this session, we will review PBR projection techniques based on work completed by the American Academy of Actuaries when developing the PBA Projection Practice Note. In addition, this session will examine specific challenges that can arise when actuaries use models to project reserves at future dates. Topics covered include mortality improvement, credibility, scenario generation, starting asset projections, and various other technical issues related to nested modeling requiring both inner and outer loop projections.

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: 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: 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: As the use of predictive models and novel datasets becomes more prevalent, a focus on consumer protection is being highlighted by regulators, ethicists, and industry insiders. Over time, increased rigor should and will be applied in model development and implementation. These issues are not new. Hundreds of individuals have worked on these issues in the laboratory, the board room, and the court room. The Civil Rights Act of 1964, Uniform Guidelines of the US EEOC, the Principles for the Validation and Use of Personnel Selection and other sources are intended to protect the rights of citizens and consumers and provide guidance to professionals in the development and use of decision-making tools. Drawing heavily on examples and parallels from psychology and employment practices as well as other disciplines, concepts such as model bias, fairness, adverse impact, disparate treatment and the like will be presented. Suggestions to ensure model validity, demonstrate model and data relevancy and minimize the potential for adverse impact to protected classes will also be offered. Each of these considerations will be linked to recent dialogue and concerns in the Life Insurance sector.