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: Python has become the fastest-growing major programming language in the world. It has surged in popularity over the last decade, following the development of its industry-leading capabilities in data science, machine learning and artificial intelligence, and it is now one of the most globally popular and widely used languages. In this article, I discuss some of the reasons for its popularity and explain why many actuaries and insurers have started using Python.

Description: Neural Networks are no longer limited to classification and regression solutions to numeric problems. CNNs add the ability to classify images, and GANs can create new images. Learn how these capabilities can provide competitive advantages to early adopters, how they work, and how to implement them.

Description: Principal component analysis is an unsupervised linear transformation mainly used for dimension reduction. Determine key numerical variables with maximum variances in a dataset, identify the correlation and removal of redundant variables are the key aspects of this exploratory data analysis.