Description: The actuarial profession is at a tipping point and a combination of disruptive technologies and external pressures are creating a unique opportunity to move from results producer to insightful adviser. For a prudent but forward-looking professional, this is exciting and scary in equal measure. The ability to process large masses of business data to make more accurate calculations has become one of the most sought-after skills for leaders across all industries, including insurance.<br><br>The presenters will introduce current data technologies used to manage, process and visualize data. They will cover solutions like Microsoft BI, Tableau, Amazon Redshift, Hadoop, Alteryx, Python, etc. and their applications in data, process improvement, automation, reporting and predictive analytics. In particular, they will explore management reporting and how it leverages data analytics to help business leaders with strategic decisions making, how to evaluate the effectiveness of management reporting structures, drivers of change and ways to improve them, and proposed steps toward entrenching this dynamic and insightful approach to management reporting in which insurance leaders can have confidence.

Description: Digital is the main reason over half of the companies on the Fortune 500 have disappeared since 2000." (CEO Accenture). We cannot be sure how global technology megatrends will disrupt the life insurance industry, but we can be certain that the future will not look like today. As large quantities of available data are no longer owned by insurance companies and statistical techniques become more accessible to non-statisticians, the pressure to keep up with technological advancement increases. Life actuaries must think critically about and be prepared for the coming changes and position themselves as leaders in an industry lagging behind in new technology adoption.

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.