How is machine learning used for loyalty programs and what can it teach us about insurance claim reserving? While triangular methods have been a foundational tool for decades, individual claim reserving gives the actuary far more information about changes and trends in the liability. Yet the commonly used individual claim reserving techniques leave some of the most valuable data unexamined. In this session, we’ll cover the benefits of reserving at the individual claim level and describe an approach that sits at the intersection of data science and actuarial science. This session will also introduce a new actuarial tool – the snapshot date triangle – and demonstrate how it can be combined with machine learning to produce a robust and powerful individual claim reserving system. You will learn why the snapshot date triangle was originally developed for estimating loyalty program liabilities and how it can be used in insurance contexts.

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