Description: As any international traveler who has tried to plug in an electronic device knows, the human race is great at devising many different ways to accomplish the same task. Predictive modeling is no different. Data scientists can choose from a wide variety of languages to prepare data, fit models, and verify results. The Rosetta Stone, which showed writing composed in three languages side by side, was instrumental in translating unfamiliar Egyptian hieroglyphics into the more common Greek language. This presentation follows a similar approach by showing the same predictive model implemented in the following languages: R, Python, Spark, MatLab, and SAS. Participants will compare and contrast each of these languages and, by doing so, build a greater understanding of those languages less familiar to them.

Description: 1993 SOA Meeting, Quebec. This session is about implementing new technology, choosing between competing technology, problems to watch out for, unexpected benefits, migrating from mainframe to client/server, automating the field force and large-scale networks.

Description: Everyone loves efficiency. But what does it truly mean for something to be efficient? An actuary who has spent almost 20 years managing process improvements shares his experiences and the evolution of his views on efficiency. This parable highlights the broader aspects of efficiency that are often missed, and some of the challenges that can come from targeting a typical view of efficiency.

Description: Producing a crystal-clear balance roll-forward on time, obtaining a fresh model output the first time or providing model results for your business partners with a tight turnaround all depend on one thing: how fast the model engine runs. Authors explored various reasons behind significant computational power consumption, and discussed different ways to speed the computational process such as in-force population compression, model use case consolidation, code simplification and streamlining, and infrastructure automation, etc.

Description: In this article, we talk to Lei Rao-Knight, FSA, Vice President and Actuary at Prudential Financial. Lei discusses her model development experience in the Modeling Center of Excellence.

Description: This chapter introduces a few probability distributions that play an important role in understanding and analyzing healthcare data. While theoretical in nature, these distributions model real life if appropriately chosen. Probability distributions are mathematical representations of how statistical data behave.

Description: Artificial Intelligence (AI) is expected to change the insurance industry more in the next 10 years than it has changed in the past 100 years. Rapid advancements in computing power and the exponential growth of Big Data are creating more opportunities to incorporate AI techniques and methodologies into actuarial work. This session is for the practicing actuary who would like to learn more about AI and how to use it to analyze insurance data. At the conclusion of the session, attendees will be able to understand the fundamentals of AI, to build an AI model and to identify AI opportunities in their work.

Description: Profile of an actuary in a non-traditional actuarial role: focusing on medical cost and analysis, both retrospective and prospective.

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: 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.