Speaker(s): Tom Jenkins (OpenText)
Artificial Intelligence (AI) combined with Actuarial Science will offer new insights into human behavior and will have a profound impact for society.  An essential element within AI is the quality of data.  Data is collected, curated and archived through a set of methods collectively known as Information Management.
Information management is an essential tool available to actuaries throughout the world to develop new insights and business models that will benefit society by both providing enormous global scale with human level pinpoint precision. 
We are at a stage of development where we can be both “mile wide and mile deep” in our thinking.  However, this type of capacity requires that the actuarial science achieve a deeper understanding of information management and analytics.
One example of information management having significant impact would be in guiding the formulation of strategy and policy for the mental health care industry which is a critical issue in many countries that are experiencing high mental health claims so that benefits can be offered in an economical way.   Combining new sources of data with established population statistics will offer new insights.
As we increase the integration of Artificial Intelligence (AI) with Actuarial Science using these ever growing data sets, we will create new and broader applications within the economy. The use of AI in all its various applications requires data.  The quality and scale of this data has a direct correlation on the accuracy and value of the analytics outcome.   There has been unprecedented growth in both data as well as the regulations governing the access and use of this data. 
We must consider the recent trends in data usage and the regulations governing that data usage throughout the world.
Understanding the requirements of data at scale, the quality of the data archive and geographic regulation of the data access are all critical to developing a successful data architecture which in turns leads to optimal outcomes for any AI strategy regardless of the economic sector.
An effective data architecture is essential to realizing the opportunities of AI within actuarial science.