Description: Overview of evolving industry trends and examples of how life insurers in Canada are innovating.

Description: Explores the open vs. closed systems debate with regards to actuarial modeling and exposes the over-simplification. Valuable for anyone who uses models.

Description: Can we improve upon traditional methods of risk measurement and risk selection? Panelists discuss potential applications of predictive modeling, including individual underwriting, group rating and provider reimbursement. They focus on ways in which predictive modeling can be used to improve risk selection, experience rating and provider reimbursement. A session at the Health/Pension Spring Meeting held in New Orleans, Louisiana June 15-17, 2005. From the Record of the Society of Actuaries, Vol. 31, No. 2.

Description: This research report addresses two purposes: [1] to incorporate phase transitions into a copula framework, and [2] to calculate the probability of defaulting in the context of a portfolio of correlated mortgages when a contagion effect exists. Understanding and measuring the effects that contagion has on the probability of defaulting, for both single assets and portfolios of assets, are crucial to collateralized debt obligation [CDO] markets.

Description: The article provides several examples of projects led by actuaries who are applying advanced data analytics and big data to solve industry-specific business problems.

Description: Using Healthcare Claims Data to Create Actuarial Models.

Description: 2016 marks the 60th anniversary of the Canadian Reinsurance Conference. This one day industry meeting hosting delegates from North America and beyond will be held in Toronto on April 13, 2016 under the theme "ReDefining Leadership".

Description: This actually is a copy of an appendix of a research paper sponsored by the SOA Committee on Finance Research. You can read the entire research paper: “Lapse Modeling for the Post-Level Period—a Practical Application of Predictive Modeling” at the SOA site https://www.soa.org/Research/Research-Projects/Finance-Investment/lapse-2015-modeling-post-level/#sthash.W9lERSls.dpbs, but you can read this valuable appendix copied here in this issue to see how to build a PM step-by-step using your data and the R programming language. We thank Richard Xu, Dihui Lai, Minyu Cao, Scott Rushing, and Tim Rozar for their excellent paper and the Society of Actuaries, for the permission to reprint this portion of the paper.

Description: Bayesian networks are one of many models that are useful for predictive modeling. This article introduces the concept of Bayesian network and shows how time-series data can be modeled for a Bayesian network.

Description: Here we break down deep learning into its various conceptual sets so as to have a better intuitive understanding of deep learning.