Description: Actuarial functions are being asked to do more with less: being challenged to provide more value to the business through applying insight and foresight. This article examines ways in which actuaries can provide business insights with very limited investment. The article provides actionable steps that actuaries easily apply to their daily work.

Description: Article summarizing the offerings from the AoF Section

Description: Interview with John Robinson, SOA President-Elect and Vice Chair, on several topics of importance to the SOA.

Description: Why Actuaries Should Welcome Automation

Description: Predictive analytics is impacting various actuarial disciplines. During this session, we will discuss how predictive analytics could impact and enhance retirement and pensions, along with how pension/retirement actuaries can utilize various enhanced analytical techniques. <em>This breakfast is open to all meeting attendees free of charge. If you plan to attend, you must register in advance.

Description: Article describing key steps to develop an incentive compensation plan that aligns to the company’s risk profile, mitigate excessive risk taking, retain talent and meet the disclosure requirements. The yearly development cycle includes develop the incentive compensation program, communicate and implement and assess the plan performance on both short and long term horizon.

Description: The authors explain big data and the potential uses of this information, especially as it is beginning to be adopted by the life insurance industry.

Description: Ever wanted to do a Kaggle data science competition, but didn't know where to start? This presentation meets the needs of actuaries interested in predictive analytics and competitive data science. The presentation will describe the nature of competitive data science, and provide insights into lessons learned by competing in the Airbus Ship Detection Challenge at Kaggle, as well as other Kaggle competitions. The presenters will provide an introduction to neural networks and describe the nature of the Airbus Ship Detection Challenge and other Kaggle competitions, plus a solution approaches that can be used to obtain top scores using Convolutional Neural networks (CNNs), random forests and boosted trees.