Today we live in a
world where “machine learning” and “artificial intelligence” are becoming
everyday vocabulary. The data driven nature of actuarial work often warrants
the question, “How can AI help?”. In a recent project, a team at KPMG
studied how machine learning can be applied to solve one of the classic
actuarial problems – estimating illiquidity premium for discount rate. A
Python-based AI tool was built to predict liquidity risk premium, trained with
bond characteristic data. In this webinar, the KPMG team will share their
findings from the AI-based illiquidity premium model, as well as lessons
learned in the process of ‘training the machine’.
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