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: 1998 Valuation Actuary Symposium. The panelists discussed applying economic scenario generators in the life insurance business, a particular generator that has been developed at SS&C Technology Company, a way of reducing the number of scenarios to get the maximum amount of information, and what to look for in a software vendor.

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

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: Discussion on modeling Medicare Supplement insurance and the considerations when choosing an Actuarial Modeling Software (AMS). Focuses on model risk management, key modeling assumptions and inputs, and the complex interactions between modeling assumptions and why its critical to have a model that can handle that complexity. This article also touches on the incorporation the of accounting and regulatory regimes into the model, including Statutory, GAAP (Legacy and LDTI), and Tax regimes, and the modeling considerations for sensitivity testing and capital planning. Lastly, the article highlights the capabilities to look for when choosing an AMS for Medicare Supplement