This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.