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: At this session, experts will provide an overview of current statutory valuation issues pertaining to life and annuity products. The focus is on activities of the American Academy of Actuaries relative to principle-based approaches, but other matters will be covered, including the status of the NAIC valuation manual, life risk-based capital, developments at the NAIC affecting life insurance companies, and practice issues for appointed actuaries.

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.

Description: This article is the first installment of a three-part series that aims to provide an overview of ESGs and will focus on the general motivation for stochastic modeling, its advantages and its limitations. Future articles will further break down the key factors of ESGs and the application of these factors to better understand the Academy Interest Rate Generator (AIRG).

Description: In this article, the interviewers will discuss the pros and cons of centralized modeling function vs a business unit based modeling function. Their experience will help us in identifying factors in contributing to a successful centralized model function and understand how to set expectation with modeling stakeholders.

Description: This paper gives an overview of fuzzy optimization, presents applications of fuzzy optimization and investigates ways to extend the methodology to risk assessment.

Description: Four appendices are attached to the monograph: a letter requesting information, two letters requesting participation, and a set of general database specifications. From Group Medical Insurance Large Claims Database Collections and Analysis, M-HB97-1.

Description: Different mortality projection methodologies are utilized by actuaries across applications and practice areas. As a result, the MIM Advisory Group developed a single framework to serve as a consistent base for practitioners in projecting mortality improvement.

Description: This is an Excel workbook that can be used as a tool for health insurance companies to model their potential risk exposure from an influenza pandemic. It is based on a model developed for a Society of Actuaries study examining the potential implications of a pandemic on the U.S. health insurance industry. The spreadsheet is saved with many of the same assumptions used in this research and reproduces the results of the exhibits contained in the June 2010 report, 'Potential Impact of Pandemic Influenza on the U.S. Health Insurance Industry.'