Description: This article is the second in a four-part series about eliminating the confusion with using data terminology. Part one discussed data governance. This second part will discuss data analysis and database management.

Description: 1992 SOA Annual Meeting, Washington, D.C. This session discussion is about technological advances. Topics discussed: 1. The exponential growth in power of the microchip. 2. The role of fiber optics in the revolution. 3. The role of the airwaves in the revolution. 4. The risks of over regulation.

Description: The panelists address Health Insurance Portability and Accountabuility Act [HIPAA] related concerns for health insurers, such as: implementation issues, privacy, transactions and code sets, employer identification numbers, and current developments relative to the pending rules. From the Record of the Society of Actuaries, Volume 29, No. 2, Session 65

Description: Sponsored by the Society of Actuaries Research Expanding Boundaries Pool, this report examines an encompassing method to allocating economic capital in insurance conglomerates. Authored by Edward Furman, Yisub Kye, and Jianxi Su, the plan put forth unifies the centralized top-down and the detail-oriented bottom-up approaches

Description: Actuaries today have unique opportunities to analyze, understand and use data for strategic decision making. With nearly unlimited computing power and a broad spectrum of data sources at our fingertips, we are enabled to support our actuarial judgment with comprehensive findings backed up by deep data analyses.

Description: This paper outlines how derivation of many balance sheet items can be improved both at the year end and at interim shipment dates by a company which utilizes data processing equipment. From Transactions of Society of Actuaries 1958, Vol. 10, No. 28.

Description: This abstract describes a presentation that gives an in-depth look at genetic algorithms - what they are, why they are important for actuaries, when to use them, and how to create them.

Description: This third session in the Munich Re series continues with an exploration of responses to the changing workplace landscape from those inside the industry. The presenters will focus on practical approaches to identifying new opportunities, and on ways in which traditional products and sales channels are changing to meet emerging needs. Are voluntary benefits still relevant? Yes, but they are changing. While the first session provided an overview of the future workplace, this session will provide an overview of the current and evolving voluntary benefits market. Sales processes and products will be challenged to change to keep up with the new workplace dynamic. The presenters share strategies for developing products and sales channels that are robust and flexible enough to address evolving customer needs.

Description: Natural Language Process (NLP) techniques can be used to find insights in large unstructured data sets and Word Embeddings are pervasive on a wide spectrum of NLP applications. These word representations improve downstream tasks such as Sentiment Analysis, Recommender System, and Topic Modeling. Learn how NLP transforms the Insurance Industry; and how word embeddings and pre-trained models improve the effectiveness and performance of NLP applications. NLP is increasing in importance for insurance companies. Additional application examples are available in session 36.

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.