Description: You really can have it all: readily available and relevant information derived from consistent and understood data. Getting at the Truth requires a good datawarehouse design and an appropriate business data model. Add a powerful and flexible Web–based analytical interface to get at the data and success in the pursuit of Truth will happen.

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

Description: Read how the world’s largest provider of specialty insurance for classic vehicles uses its media department’s YouTube videos to its full advantage.

Description: This article looks at how the underwriting process has transformed from a paper process to become fully online. Not only has the process been digitized, there has been various innovations incorporating technology thus enabling large volume process automation, better customer experience as well as higher closing ratio. Other than improvement on speed and efficiency, various smart solutions are being explored to refine risk management through data mining and analysis of underwriting data.

Description: This article is a discussion of social media tools such as Facebook, LinkedIn, Twitter, YouTube, blogs, MySpace, Friendwise, Yahoo! 360, Orkut, and Classmates and how they relate to entrepreneurial practitioners and the way they do their jobs.

Description: Global megatrends in technology are disrupting the life insurance industry.

Description: Big data, predictive analytics, machine learning and AI will likely transform the insurance market and the actuarial profession. The actuarial profession is seeing changes many markets, including health and property and casualty. This article describes these trends and provides an introduction into what companies and the Society of Actuaries (SOA) are doing to adapt and capitalize on these trends.

Description: This article provides an overview of activities that should be considered by an actuary when validating a predictive model. It describes several components to consider when validating a predictive model and provides an examination of various methodologies that can be implemented in the validation process.