Description: This report describes a simple way to construct a bivariate copula with specified marginals and partially specified dependence. For example, the method allows one to model tail behavior scenarios while holding the core of the joint distribution fixed. An Excel workbook named CopulaExample.xls is provided to illustrate the method, including its use in Monte Carlo simulation.

Description: Life insurance industry use experience studies to manage and monitor their blocks of business. The purpose of this paper is to provide an overview of the data cleansing and validation role specific to the life insurance experience study process.

Description: This research report is a summary of an online survey and follow-up discussions with survey respondents on the current state of actuarial modeling controls within U. S. and Canadian life insurance and annuity companies. The current state was then compared to the results from the 2012 survey on this topic to examine the evolution in actuarial model controls and governance. The report also provides an industry assessment and proposes considerations for enhancing the current state towards a well-controlled governance framework.

Description: Most actuaries deal with model development at some point in their career. However, very few of us have proper training in programming, software engineering or software shop operations. Explore topics, techniques, and practices from the FinTech and IT industries that can be applied to building robust, high-quality actuarial models. This session will be of particular interest to actuaries engaged in 'in-house' model development, managers of modeling teams, and actuaries interested in software development.

Description: Claims are insurers deepest and richest transactional data – yet we rarely unleash the full potential of this data. In this session, we will discuss potential sources and attributes of claims data, traditional approaches alongside new analytic techniques and how to integrate models into existing claims management processes. During this session, the presenters will discuss different areas of how machine learning can drive significant value unleashing the potential in claims data, these include data capture, claims management and workflow design as well as behaviour prediction. In addition, the audiences will also have opportunity to learn different use cases of how claims models taking advantage of machine learning, such as foreign death fraud detection and critical illness triage.

Description: This article will clarify valuation issues for the waiver of premium benefit in Accident and Health products.

Description: This paper proposes using a classic actuarial framework of frequency and severity, modified to address the unique challenge of XPL.

Description: Results of survey, conducted recently by the Taxation Section of the Society of Actuaries, on how federal income tax is reflected in the projection work being performed.

Description: Overview of the issue.

Description: This article explores strategies for optimizing actuarial models built on open system platforms. It begins by contrasting open and closed system modeling environments, emphasizing that users of open systems must take responsibility for in-house runtime optimization. The authors underscore the critical role that efficient, well-optimized models play in supporting a high-performing modeling team. To reinforce the techniques discussed, the article includes a case study focused on disability insurance modeling to offer practical insights.