Description: Large claims have always been a major source of volatility in health care claims experience. In this session, the presenters will discuss techniques for analyzing large claims, predicting large claims and measuring the associated risk. The presenters will also discuss the early results of the SOA 5/50 research project which will examine the 5% of the population that accounts for 50% of the health care costs.

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: This session will explain how, by applying his or her educational background and business experience, an actuary can quickly learn the basics of data science and perform the important modeling work of insurance.

Description: We will determine the role of risk function in evaluating operational risks associated with the implementation of LDTI and any system conversions. Are all models validated by the risk function before they are productionalized and results used by management to make business decisions? Does the risk function play a part in to assist finance, actuarial and investor relations in communicating changes and volatility resulting from LDTI? If not, should it? We will articulate the role that risk functions play in being part of large transformation from the start as a stakeholder/input provider to subsequently being an independent reviewer (i.e., second Line of defense).

Description: The arrival of ASU 2018-12 (a.k.a Targeted Improvements to the Accounting for Long-Duration Contracts or GAAP Targeted Improvements) presents insurers with augmented financial reporting risk across all areas of the valuation process. Naturally, the internal control frameworks that serve to mitigate these risks must evolve and expand. Valuation actuaries should view this transformative event as an opportunity to take an active role in developing new and improved controls that are both effective and sustainable.

Description: The SOA Research Institute and LIMRA have completed the most up-to-date study of variable annuity contract owner behavior. Learn more about this vital resource for actuaries working with annuities.

Description: In the context of reinsurance, this paper addresses finding the quota share retention level and the retention limit, with a diffusion process being added to the claim process, that maximizes the adjustment coefficient in the Sparre Anderson model. From Actuarial Research Clearing House, 2006.

Description: This paper provides a new insight to the previous work of Briys and de Varenne [1994], Grosen and Jørgensen [2002] and Chen and Suchanecki [2007]. It shows that if an insurance company follows a risk management strategy, it can significantly change the risk exposure of the company, and that it should thus be taken into account by the regulators.

Description: The article explores how integrating AI into insurer apps can address critical challenges in healthcare, such as patient no-shows, medication adherence, and cost management. These non-disruptive approaches aim to increase the effectiveness of patient interventions and improve medication adherence.

Description: Commentary on practical observations resulting from constructing and running Economic Capital models. Possible standardization of assumptions and result interpretation are morphing even though use of E.C. models has now been around for years.