The emergence of the AI Life Insurance Valuations Actuary

Actuarial Technology Summit 2025 In regulatory environments like Solvency II, it is required that life insurers report solvency at 99.5% confidence levels.  For an AI model to be a feasible replacement for existing in-house models, it must be 99.5% accurate or higher on every single feasible model point (policy and valuation basis).  Clearly, Kriging is important, but that is just the start.  A combination of various disciplines is important in a regression setting. In this session, we will delve into the data, model, and technical requirements for performing in-house neural network valuations.  This is performed on a single computer from the viewpoint of an actuarial consultant.  This consultant could be an employee of the life insurance company or an external individual, such as an auditor or a regulator.   The benefits of being able to perform millions of valuations locally in a fraction of the usual time and without any further production cost are left for the attendee to put a price on.