Machine Learning: An Analytical Invitation to Actuaries

This post highlights the various value-additions that machine learning can provide to actuaries in their analytical work for insurance companies. As such, a key problem of swapping specific risk for systematic risk in general insurance ratemaking is highlighted along with key solutions and applications of machine learning algorithms to various insurance analytical problems. The hypothesis is that in normal market conditions, premiums are kept at low levels to increase revenues and market share. The traditional approach requires precise figures (point estimates) and so leads to understatement of uncertainty. This keeps a comfort level for us but the hidden risk of underpricing in our premium estimates is hardly given the attention it merits.