Presentation(s):
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Presenter(s): Wing Wong, FSA, MAAA, Principal , Milliman; Stanley Hsieh, Actuarial Analyst, Milliman
Life insurance companies rely on experience studies for predicting lapse
rates of insurance policy. Traditional actuarial lapse studies require
subjective judgement and consider only a limited number of factors such as
policy durations and product grouping. For life insurance companies to improve
the prediction of lapse behavior, better methods and more factors, such as more
internal policy related data or external economic data, should be considered.
With the improvement in technology and advent of big data, machine learning
techniques can be applied for the prediction of lapse rate. This session will
cover how machine learning technology is being applied to predicting lapse rates
by going through a real-life case study. Machine learning methods which are
being applied on the case study, including Generalized Linear Model, Decision
Trees, Random Forest and Gradient Boosting Machine will be introduced. How
machine learning models generate a more accurate result than traditional
experience studies will be discussed. At the end of the session, what life
insurance companies have to do to start taking advantage of data analytics will
be covered.
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