Description: As commodity markets evolve in tandem with advents in communications technology, the criticality of data management becomes a lynchpin of corporate success or failure. Advanced analysis on market movement can lead to substantive market gains, while an inability to assess risk in a timely fashion can hinder corporate responsiveness, stagnate growth or result in unmitigated risk. In essence, the old paradigm of data flow and management was severely limited by 1 the amount of readily available data, 2 the transmission capabilities for data, and 3 the costs of data storage and maintenance. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

Description: Using Healthcare Claims Data to Create Actuarial Models.

Description: Interest in blockchain is high among insurers and reinsurers. While it’s not universally useful the technology does merit continued closer investigation.

Description: Research report sponsored by SOA to model and compare projected costs of different proposals for a Medicare prescription drug coverage plan. 3 types of costs are studied: net benefit cost estimates per member per month, continuance table data, and projected annual costs to the government over 10 years for two selected plan designs.

Description: Read the March 2018 issue of Small Talk published by Smaller Insurance Company Section. Practical information on trends that impact sections and activities.

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: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.