Description: Data science, predictive analytics and AI are certainly the buzzwords these days. They are heavily used and developed in underwriting and marketing but for most actuaries, they might/may as well be science fiction. The presenters will demonstrate how a normal actuary can utilize data science techniques in their daily job function without forming an official data science team within a company. It can be done in a gradual fashion so that normal business is not disrupted and improvements are made over time. The usage of data science techniques will include demonstrations of the following areas: - Regression analysis - Economic scenario generator - Experience study - Data quality checks - Inforce compression for explicit hedging

Description: Open source programming languages have become a common part of the actuarial toolkit over the years. Using tools like R and Python, highly technical actuaries can automate onerous processes, easily work with massive amounts of data, create models and generate reports. Although these tools excel at turbo-charging analysts, it can be difficult for benefits to inure to the rest of the organization given the learning curve (“Can’t you just put this in Excel?”), control risks (“So your entire process could break overnight if some random person in Nebraska changes something?”), and internal IT environments. Listen in on a panel discussion where presenters share their personal stories of working with open source in a production setting. Hear about real world business problems, how open source tools were used, what risks were present, how they were overcome and the final business result.